Reviewerly/exHarmony huggingface dataset:
Joint Demodulation and Error Correcting Codes Recognition Using Convolutional Neural Network

Demodulation of the communication signals and blind identification of error-correcting codes (ECC) are two essential purposes in adaptive modulation and coding (AMC) techniques and non-cooperative communication fields. The existing approaches treat them as two separate problems, namely the demodulation of signals with known a <i>priori</i> knowledge (such as channel state information (CSI) or channel noise) and the ECC type identification that depends on the demodulation results. In this paper, a novel one-stage ECC identification approach based on a multi-task deep convolutional neural network (MT-DCNN) is presented. With this architecture, the proposed method automatically recognizes the ECC types of the baseband in-phase and quadrature-phase (IQ) data without relying on any conventional demodulators. Precisely, the proposed MT-DCNN consists of three modules: the feature extraction module, the demodulation module, and the ECC types recognition module. Experiment results show that the proposed architecture can accurately identify the ECC types of the baseband IQ signals and is superior to those of the existing two-stage recognition approaches.
Review Model Teknologi Perguruan Tinggi Menurut Sudut Pandang Supply Chains Management

The community's need for higher education is very important. Given the regulations of UU No.234 u 2000 concerning guidelines for the establishment of higher education institutions, it is quite easy for foundations and institutions, several institutions are competing and interesting to establish universities. Higher education is currently an attractive business field. The existence of graduates in the community or the market will always be an attribute of the community's assessment of the original university. If the alumni are well absorbed by the market, the university will get a positive assessment. Every university has an interest in knowing the level of user satisfaction of its graduates as an important part of the evaluation and projections of the institution. Problems arise when graduates are not well absorbed. Many factors could be the cause. In a business, of course, you must look at it from the point of view of market needs, the same thing as higher education institutions. If you don't pay attention to the market aspect, it is certain that graduates are not well absorbed due to lack of quality. In this article, we will discuss the sustainability of business processes in higher education based on user reviews of graduates. The continuity of this business process using a supply chain model approach.
How to structure a knowledge-sharing process based on different configurations of time and space dimensions:

The availability and progress of information and communication technologies opened new possibilities for learning processes. Even though there are recent studies about the use of technologies in learning processes, a more systematic and deep debate about the forms of knowledge sharing in teaching processes and how technologies can be used in different educational contexts is still necessary. This paper investigates forms of knowledge sharing used in an education setting and introduces a method to the analysis of the knowledge sharing process based on four possible configurations of knowledge sharing (same-time same-place, same-time different-place, different-time same-place, different-time different-place) under the perspective of digital literacy. As the study object, teachers of an engineering course were interviewed with respect to the pedagogical practices adopted in the context of the Covid-19 Pandemic and the feasibility of continuing some of them after this period. The coding of these experiences and the reflection on them permitted the introduction of a framework for the structuration of knowledge-sharing practices in educational environments that employ different configurations of space-time in the educational process, under the conceptual lenses of digital literacy. This method seems to be a feasible tool for analysis of different situations where there are different practices of knowledge sharing.
A Data-Loader Tunable Knob to Shorten GPU Idleness for Distributed Deep Learning

Deep Neural Network (DNN) has been applied as an effective machine learning algorithm to tackle problems in different domains. However, training a sophisticated DNN model takes days to weeks and becomes a challenge in constructing research on large-scale DNN models. Distributed Deep Learning (DDL) contributes to accelerating DNN training by distributing training workloads across multiple computation accelerators (e.g., GPUs). Although a surge of research works has been devoted to optimizing DDL training, the impact of data-loading on GPU usage and training performance has been relatively under-explored. It is non-trivial to optimize data-loading in DDL applications that need intensive CPU and I/O resources to process enormous training data. When multiple DDL applications are deployed on a system (e.g., Cloud and HPC), the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. Therefore, our work first focuses on investigating the impact of data-loading on the global training throughput. We then propose a throughput prediction model to predict the maximum throughput for an individual DDL training application. By leveraging the predicted results, A-Dloader is designed to dynamically allocate CPU and I/O resources to concurrently running DDL applications and use the data-loader allocation as a knob to reduce GPU idle intervals and thus improve the overall training throughput. We implement and evaluate A-Dloader in a DDL framework for a series of DDL applications arriving and completing across the runtime. Our experimental results show that A-Dloader can achieve a 23.5% throughput improvement and a 10% makespan improvement, compared to allocating resources evenly across applications.
EMPATHY TRAINING AS A MEANS OF PREVENTING BULLYING IN SELECTED SECONDARY SCHOOLS: ALIMOSHO LOCAL GOVERNMENT AREA, LAGOS, NIGERIA

Bullying is a maladaptive behaviour that children and adolescents face daily in schools all around the world. It is prevalent in today's schools and is commonly found in secondary schools. It is a complicated social problem that can have serious negative implications for both bullies and victims, especially when the victim is physically or psychologically harmed. The study, therefore, examines empathy training among students to prevent bullying behaviour in selected secondary schools in Alimosho local government area of Lagos state. A survey method was utilized using the structured questionnaire as an instrument of data collection. Utilizing a simple random sampling, three hundred and seventy-six copies of the questionnaire were administered to students in the four secondary schools. However, three hundred and thirty were retrieved which were used for the analysis. Quantitative data were tested using Quantitative data was analysed utilizing the descriptive statistics with the aid of the Statistical Package for Social Science (SPSS) version 23. A descriptive analysis was used with the use of frequency counts and percentages for the analysis of the data. Findings of the study shows that many victims of bullying get involved in substance abuse and lose interest in schooling. Therefore, empathy training and encouraging victims to make timely reports on incidences of bullying are crucial to mitigating bullying and its consequences in secondary schools in Lags State. Keywords: Bullying, Empathy training, prevention of bullying, Secondary schools
Design Decisions in the Architecture Development of Advanced Systems: Towards traceable and sustainable Documentation and Communication

Future technical systems, so-called Advanced Systems (AS), will be significantly more capable than current ones, but also more complex.This increase in complexity needs to be addressed during the development of AS, which resembles an ongoing complex problem-solving process (PLP).In PLP, documenting and communicating decisions is paramount.A major result of PLP decision making is the system architecture, which defines the basic concepts and properties of the system.Documenting and communicating these design decisions properly is especially important since they allow later changes to remain traceable and organizationally sustainable.These activities are also relevant for learning and sustainably saving knowledge.Developers however lack the appropriate tools to do so, and thus often report in unstructured natural language and via different channels.Virtual collaboration platforms such as Microsoft Teams could significantly help documenting and communicating design decisions during the PLP.They are ideal for storing information such as decision rationales and for communicating them to different project stakeholders without changing systems.Nevertheless, developers still lack methodological guidelines and simple technical support for using them in the context of architecture development.As a first step, we therefore held semi-structured group interviews with various company representatives to identify problems in communicating and documenting design decisions and to evaluate solutions with employees.We found that developers lack uniform documentation processes and templates.In addition, they see documenting and communicating design decisions not as a significant part of their work, but nevertheless complain about incomplete and missing communication of these decisions.In this paper we report in detail our findings and use them to develop preliminary support methodology for documenting and communicating design decisions on collaboration platforms.The proposed methodology consists out of 4 phases: the identification, documentation, communication, and management of relevant design decisions.It includes tools and guidance to support developers in their daily work.
Advantages of a Specialty over a two-level Bachelor’s — Master’s degree model in Engineering education

The problems arising during the training of engineering personnel according to the two-level Bologna system are analyzed. Contradictions are revealed between the discrete nature of the formation of project and research-oriented competencies and the essence of engineering activity. The ways of overcoming this contradiction by combining bachelor’s and master's degrees into a single specialty program are proposed. It is concluded that within the framework of the specialty, it is possible to improve the quality of education without significant material costs, due to the methodology of the formation of curricula.
THE THEMATIC RELATION OF THE SUBJECTS IN THE “SNOW WHITE AND THE HUNTSMAN” MOVIE SCRIPT: A SYNTACTICAL ANALYSIS

Every language in the world has their own characteristic of syntactical structure. The important parts in grammatical system include subject, direct object, and indirect object (Van Valin, 2004). This study aims to find out the thematic relation of the subjects in the movie script. The data source of the study was taken from “Snow White and The Huntsman” movie script. The data used the clauses available in the script that contained verb-specific semantic role to identify whether the subject was categorized as agent, experiencer, recipient, stimulus, theme, or patient. The data were analyzed by using thematic relation categorization proposed by Van Valin (2004). The result showed that there are five semantic roles of subjects found in the script such as agent, experiencer, recipient, theme, and patient. The finding seems to contribute filling the gaps of the previous studies as well as providing a gap for further inquiries to be explored more by the future researchers.
Offline Handwritten Tai Le Character Recognition Using Wavelet Deep Convolution Features and Ensemble Deep Variationally Sparse Gaussian Processes

Abstract Handwriting recognition is an important application in the field of pattern recognition. Because handwritten Tai Le has similar characters and the proportion of characters with similar shapes is relatively high, this paper proposes a wavelet deep convolution (WDC) feature combined with an ensemble deep variational sparse Gaussian process (EDVSGP) offline handwritten Tai Le recognition method. A Tai Le recognition sample database is constructed, 2 and 3 levels of wavelet decomposition prior features are extracted, and then, the wavelet a priori feature is transformed into a WDC feature. To avoid the problem of dimensionality, LDA+PCA is used to reduce the dimensionality of the feature. Moreover, to better recognize handwritten Tai Le characters, 6 deep variationally sparse Gaussian process (DVSGP) models are integrated as an EDVSGP classification model. Using the handwritten Tai Le character database, the proposed method is superior to the existing methods, with an accuracy, recall and F1-score of 94.15%, 94.15%, and 94.15%, respectively. The universality of the method is verified on the dataset of handwritten Chinese characters with similar shapes (HCFC2021.4) and Devanagari dataset.
Perbandingan Algoritma untuk Mereduksi Noise pada Citra Digital

Image restoration is one of the stages in the field of Digital Image Processing. Image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabillistic models of image degradation. The mathematical algorithm to reduce noise in digital images in this study uses 8 filtering algorithm methods. The purpose of this study is to compare 8 filtering algorithm and conclude which algorithm is the best for reducing noise in digital images. The method for generating noise uses Rayleigh Noise and Erlang (Gamma) Noise. The algorithm for reducing noise is Arithmetic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Median Filter, Maximum Filter, Minimum Filter, and Midpoint Filter. The measurement to determine which algorithm is the best using Root Mean Square Error (RMSE). Tests were carried out on 15 digital images by testing 1200 times. The conclusion of this study is that the best algorithm for noise reduction is Median Filter by resulting the smallest RMSE value of 6.0860942.
The Effect of Silica Dioxide (SiO2) Nanoparticle Coating and Duration of Coffee Immersion on Discoloration of Thermoplastic Nylon Denture Base

Introduction: Silica dioxide (SiO2) nanoparticle have long been used as a denture base coating. Thermoplastic nylon denture base material is prone to discoloration due to its amide bonds absorb water easily. Meanwhile, coffee contains chlorogenic and tanic acid, which can change the color of denture bases. Purpose: This study was to examine the effect SiO2 coating and duration of coffee immersion on discoloration of thermoplastic nylon denture base. Method: Samples consisted of 24 thermoplastic nylon in square-shaped (30 x 30 x 2 mm), divided into 4 groups (n = 6) which were control (without SiO2 coating) and treatment (with SiO2 coating) groups, which then were immersed in coffee solution for 15 and 30 days. Discoloration test was conducted using spectrophotometer by measuring the delta absorbance of light before and after coffee immersion. Result: The lowest delta absorbance was in the 15-day treatment group (0.019 ± 0.006) and the highest was in the 30-day control group (0.085 ± 0.028). Two-way ANOVA test showed that SiO2 coating and coffee immersion had an effect on discoloration of thermoplastic nylon (p &lt;0.05). Post hoc LSD test showed that there were significant differences between the control and treatment group at 15 and 30 days of coffee immersion (p &lt;0.05). Conclusion: SiO2 as a thermoplastic nylon denture base coating can reduce discoloration by coffee immersion for 15 and 30 days.There were no differences between 15 and 30 days of coffee immersion on thermoplastic nylon’s discoloration in the control and treatment groups.
An Application of Generalized Linear Models to Fine Needle Aspiration in Breast Cancer

Breast cancer is currently the most dangerous cancer for women worldwide. Doctors routinely employ biopsies, diagnostic mammograms, and other techniques to detect and diagnose breast cancer. The Fine Needle Aspiration, also referred to as fine needle biopsy, is a technique for assessing tumors that involves inserting a needle into a mass to retrieve alive cells. However, the current breast biopsy test is time-consuming and unable to detect early breast cancer. Applying the statistical tools to fine-needle aspiration is helpful in developing its feasibility and reducing test time, thereby reducing the cost of service as well as waiting time. In this study, the diagnostic model was fitted with a generalized linear model as the framework and Least Absolute Shrinkage and Selection Operator regression as the essential methods. Amongst cellular level features, which are variables in the model, some features were identified that play an essential role in the models, including texture, smoothness, concave points, and fractal dimension. The high accuracy (&gt;0.9) obtained from the model in data testing supported that Generalized-Linear-Models-based machine prediction can effectively assist physicians in their clinical diagnosis. In addition, essential features in the model could be considered to have some association with the hidden lesion of breast cancer.
PENGARUH JARAK TEMPAT TINGGAL PESERTA DIDIK TERHADAP KEAKTIFAN MENGIKUTI MATA PELAJARAN IPS DIKELAS VII SMP NEGERI 1 KASIMBAR KEC. KASIMBAR KAB. PARIGI MAUTONG

The purpose of this study was to determine the effect of the distance where students live in their active participation in social studies subjects in Class VII SMP Negeri 1 Kasimbar Kec. Kasimbar Kab. Parigi Mautong. This study uses a quantitative approach Expo facto. Data collection techniques using a questionnaire (questionnaire) and sampling using the Slovin formula (Stratifite Random Sampling) while the sample used in this study was 60 students of class VII.) The technique of Simple Linear Regression Analysis. The results show that the effect of the distance from which students live on their active participation in social studies subjects can be seen from the statistical test results obtained t_count &gt; t_table or 28,229&gt;2,001, this means that the distance from which they live (X) has a significant effect on their active participation in subjects Social Studies (Y) with a large influence of 0.428 or 43% based on this result the distance of the students' residence to their active participation in Social Studies subjects in Class VII SMP Negeri I Kasimbar Kec. Kasimbar Kab. Parigi Mautong is quite high. The implication of the research is that teachers are expected to be able to try to maintain and increase the learning activity of students in social studies subjects in class VII SMP Negeri 1 Kasimbar
The implementation of the integrated performance assessment (IPA) model to improve English students' speaking skills

This study aims to analyze the extent to which the application of the IPA model could improve Indonesian students’ speaking skill, especially speaking skills for discussion course. It was classroom action research in which the stages of its activities include planning, implementation, evaluation, and reflection. This research’s subjects were the third semester English Department University of Bengkulu. The researchers only took four cycles as the data of the research. Qualitative and quantitative data were analyzed to find a valid conclusion. The results show that the application of the IPA model improved the basic skills of students’ speaking skills in class. It can be seen from the improvement in almost every cycle. The mean score in cycle 1 was 80, followed by 80.3 in cycle 2, but slightly decreased in cycle 3 with a mean score of 79.6 and got higher again in cycle 4 with a mean score of 82. Internal and external factors also contribute to the students’ speaking skills.
Cryptocurrency and Its State of Research

This article maps the existing academics and working papers about cryptocurrency, or digital currency that uses blockchain. Cryptocurrency is one of the applications of blockchain that can accelerate economic digitalization within countries, which in the end could improve economic efficiency, effectiveness, and access for citizens. By the type of issuers, cryptocurrencies can be placed into three categories: central bank-issued digital currency, private institution digital currency, and peer-to-peer crypto-assets. The advent of cryptocurrencies requires strong regulatory frameworks to protect the users, ethics guidelines to direct the development, risk benefits study to identify pitfalls, users’ psychology, law enforcement guidelines to ensure a safe environment, and economic initiatives to provide society with maximized benefits. This paper shows research findings, methods, and future research directions in cryptocurrency.
Comparison Of Two Mean Vectors Under Differential Privacy For High-Dimensional Data

The multivariate hypothesis testing problem is a more interesting task of the statistical inference for high-dimensional data nowadays, in which the dimension of the observation vectors is diverging and could even be larger than the sample size.However, in many applications of multivariate hypotheses problems, the data are highly sensitive and require privacy protection.Here we consider a private non-parametric projection test for the comparison of the high-dimensional multivariate mean vectors that guarantees strong differential privacy.The empirical evidence shows that the non-parametric projection test under differential privacy gives accurate inference under the null hypothesis and a higher power under the local alternative hypothesis.
A Structural Learning Method for Graphical Models

This work is centred on investigating dependencies and representing learned structures as graphs.While there are a number of methods available for discrete and Gaussian random variables, there is no such method readily available for continuous variables that are non-Gaussian.For such methods to be reliable, it is necessary to have a way to measure pairwise and more importantly, conditional independence.In this work, an algorithm is created that uses both mutual information and a kernel method together to account for these dependencies and yield a graph that represents them.This method is then demonstrated through a simulation setting, comparing the results to an algorithm often used in Gaussian settings, additionally discussing future steps regarding this project.
Machine Learning for Precision Medicine: Model Selection, Estimation, and Inference

In the era of precision medicine, high-throughput data are routinely collected.These high dimensional data defy classical regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting.In this talk we will introduce several cutting-edge machine learning methods, developed by my group in the last few years, for modeling (censored) outcome data with high dimensional predictors.Specifically, we will introduce a Dantzig selector for fitting survival models with high dimensional predictors, followed by various semiparametric and nonparametric feature screening methods for handling ultra-high dimensional predictors.We will also discuss statistical inference for regression models with high dimensional predictors.With high dimensional outcome data, we will introduce a new class of high dimensional Gaussian graphical regression models with predictors.The talk focuses on statistical principles and concepts behind these methods, which are motivated and illustrated by various biomedical examples, which have precision medicine contexts.