Reviewerly/exHarmony huggingface dataset:
Pengaruh Cluster Emiten terhadap Return Saham JSX Berbasis Parameter Rasio Analisa Fundamental

Abstract Purpose: This research aimed to find the effect of cluster techniques in determining stock selection to maximize return and minimize risk in the stock market. Research Methodology: The methodology consists of two of several algorithmic approaches of the clustering method to find hidden patterns in a group of datasets, i.e., Partitioning clusters (k-means) defined by the dataset object and its central area, and hierarchical clusters that group data through varying scales to be implemented into cluster trees or dendrogram. Dataset summary analysis of the fundamental ratio of stocks in the study was obtained from IDX stock data. Results: This study's classification has been obtained that consists of three zones: green, blue and red zone. The significance obtained provides an alternative form of stock categorization, creating an investment decision support system based on Cluster Analysis, the search for correlations and patterns between ratios of the Financial Statements as complementary tools of Investment Risk Management. Limitations: This research uses only two clustering algorithm methods to analyze the effect of clustering in maximizing return and minimizing risk and only used variables of financial reports for the company listed on the Indonesia Stock Exchange. Contribution: The risk management portfolio is a crucial part of being analyzed for investors and management to improve financial performance. As a complement to decision support, the risk management systems have to be analyzed based on cluster analysis and subsequent data mining to know the potential stock valuation in the market.
Multirate Digital Signal Processing for Image Reconstruction using Error Optimization Techniques: The Combined Adaptive Weiner Filter and the MDBUTMF

In this study, reconstruction of clearer images using error optimization techniques was done.Minimization of noise from the image and the study of the performance of a combination of the Weiner Filter (WF) via the Least Mean Square (LMS) Algorithm and the Modified Decision Based Unsymmetrical Trim Median Filter (MDBUTMF).MATLAB 7.11.0(R2010b) as a digital signal processing tool was used.The findings reveal that the WF-LMS algorithm fails to remove salt noise (zero valued pixels).These findings turn out to be a potential contribution of this study to the field of DSP in computational physics and optics.Several studies in multirate digital signal processing have not pointed this anomaly of WF-LMS algorithm in their work nor has it been published by any publishing institutions to the best of our knowledge.MDBUTMF performance was found to be better than WF-LMS at high noise variances.However, observations show that the resolution of the image with MDBUTMF decreases with increasing noise variances (NV).The results of the proposed method (a combination of WF-LMS and MDBUTMF) were found to be generally effective and superior compared to WF-LMS and MDBUTMF when used separately.The method therefore makes a potential contribution in improving restoration and visibility of images.For qualitative evaluation and measurement, the Mean Average Error (MAE), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) were used and the effectiveness of the filters increases in the order of WF-LMS, MDBUTMF and the proposed method.It is concluded that the generality that WF-LMS denoises salt and pepper noise no longer applies.It is also concluded that the proposed method is superior compared to the WF-LMS and the MDBUTMF when used separately.It is then recommended that WF-LMS needs modification to denoise the salt noise and thus the proposed method of combination of WF-LMS and the MDBUTMF.
Low-risk Privacy-preserving Electric Vehicle Charging with Payments

and ordering requests and offers.Furthermore, some protocols propose blockchain technology for handling the payment.However, the proposed protocols often (i) do not cover customer privacy to a full extent; (ii) do not allow for combined bidding and payment transfer with limited risk for both, the EV and the charging station; and (iii) do not cover both, gridto-vehicle and vehicle-to-vehicle charging.Due to limited trust and anonymity in privacy-preserving systems, there is also a substantial financial risk for both, the EV (or the consumer in general) and the charging station (or the producer in general).While the EV runs the risk of paying without receiving a sufficient amount of energy (or any energy at all), the charging station runs the risk of providing energy and not receiving sufficient compensation.This is largely unaddressed in prior work.Following a privacy-preserving protocol for grid-to-vehicle charging presented in [11], in this paper, we present a comprehensive protocol for EV charging with payments.The protocol preserves the privacy of all participants and the location privacy of the EVs during the exploration and bidding phase.Furthermore, pseudonymity is preserved during the charging phase and the payment phase, and a parameter is proposed which allows for controlling risk.Two different implementations for transferring installments in exchange for energy are described and evaluated.These implementations are compared in terms of risk, scalability, performance and privacy.The rest of this paper is structured as follows: Section II describes EV charging, blockchain technology, hash timelocked contracts and state channels.Section III describes the proposed privacy-preserving protocol.Section IV evaluates the protocol with respect to risk, privacy and scalability.Section V provides an overview of related work.Section VI summarizes this paper. II. BACKGROUNDThis section summarizes the relevant background for EV charging and blockchain technology. A. Electric Vehicle ChargingOne of the most common types of EVs relies on batteries which need to be recharged from an electric power source [15].Depending on their capacity and usage, these batteries may retain significant portions of their initial charge over longer periods of time.Three different types of charging can be distinguished [15], [20]: (i) G2V (Grid-to-Vehicle) Charging:The EV is charged by the power grid.This is the most common case and is usually done via a charging station, see e.g., [15]
The Role of Lean Marketing in Achieving Sustainable Competitive Advantage: An Analytical Study at Al-Mosul Dairy Manufactory

This paper aims to examine the relationship between Lean Marketing (LM) and implementing Sustainable Competitive Advantage (SCA). Based on discussing the different types of waste that a company can produce during the production process. This study targeted the Mosul Dairy Manufactory. In total (67%) valid completed questionnaires, out of (75) questionnaire forms distributed to the manufacturer’s marketing departments and distribution outlets. This study is based on a number of conceptual and applied questions concerning the relationship of correlation and its influence between the variables of research. The questionnaire was used as the main tool for collecting data and information. Statistical quantitative methods like (SPSS) were adopted in analyzing the data collected by the questionnaire. The main outcomes indicated, the existence of a significant correlation between the two variables, as well as, the existence of a significant impact of lean marketing on the sustainable competitive advantage. The main significance suggests; the necessity to adopt the concept of lean marketing in the manufacturing facility to achieve sustainable competitive advantage.
Online Learning Problems; Students' English Learning Barriers

Policies related to online learning which were then simultaneously used as a learning system did not appear without problems. As a result, countries that are not ready with this system, of course, face many problems, especially for teaching staff and students. This study, tries to explore problems that have the potential to hinder students' understanding during online learning, which means that researchers will only focus on obstacles in the learning process, both internal to students and external from teachers and parents, not technical matters such as network constraints, no quota and other related matters. Subjects in this study were 15 junior high school students in Penan, Pejarakan, Ampenan. Qualitative research with this descriptive approach, in the process using Observation and Interview methods as the main way to obtain data where the results of this study classify the main problems that arise into 3 parts, namely; 1) There is a delay from the subject in participating in online class activities, 2) There is a tendency not to take learning activities seriously by doing other activities such as playing games, watching TV and other activities and 3) the lack of time and the ability of companions during the learning process is good because they do not have sufficient understanding of the material or other activities or work to be undertaken.&#x0D;
Data validation and data quality assessment

Abstract Once data have been recorded, data validation procedures have to be conducted to assess the quality of the data, i.e. give a confidence grade. Furthermore, gaps may occur in time series and, depending on the purposes, these can be given values by application of e.g. interpolation. Since both aspects are strongly correlated, this chapter gives an overview on the main data validation and data curation/imputation methods. Instead of offering exhaustive details on existing methods, this chapter aims at providing concepts for most popular techniques, a discussion of their advantages and disadvantages in the light of different cases of application, and some thoughts on potential impacts of the choices that must be made. Despite involving mathematical methods, data validation remains a largely subjective process: every data user must be aware of those subjectivities.
Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining

The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a high performance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two layered meta classifications to address the limitations of existing stacking architecture to utilize Simulated Annealing Algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named; Adaboost algorithm, Gradient boosting algorithm, XGBoost classifier and bagging classifiers as well.
The Carbon Footprint Estimation based on Campus Activities in ITERA (Institut Teknologi Sumatera)

ITERA (Institut Teknologi Sumatera) is one of young university situated in Lampung Province, Indonesia. In 2018, the average population of campus (students, faculties, and staff) was about 9584 persons. The objective of this paper is to inventory Green House Gasses (GHGs) and then to calculate carbon footprint using equation by UI Greenmetric Guideline 2018 except for fuel and LPG consumption, paper use, and organic waste generation by using GHGs Protocol. Three scopes of GHGs emission were used to classify based on both direct and indirect source(s). The first scope was presented by LPG consumption and fuel consumption of campus’s car. Electricity consumption became scope 2 while the scope 3 involved paper use, organic waste generation including food waste and Yard trimmings, and transportation activities of both motorcycles and cars. The estimated GHGs emission was about 2846.541 metric ton CO2eq during 2018-2019 (one year) with the portion of each scope resulting 10.2%, 62%, and 28.2%, respectively. Electricity usage was being the highest contributor of carbon footprint. The inventory of GHGs will help top management of campus to evaluate and determine some strategies for minimization, reduction, and mitigation notably in electricity sector by some strategies such as substituting electric devices into eco-friendly products, applying energy management ISO 50001, and others.
ONLINE LEARNING STRATEGIES IN TARAS SHEVCHENKO NATIONAL UNIVERSITY OF KYIV AS A POST-PANDEMIC OUTCOME

The lockdown caused by the COVID-19 outbreak mainstreamed distance learning in all educational institutions worldwide and Ukraine in particular. Kyiv University’s staff faced the global challenge to teach in the crisis conditions. The university closure made it clear that remote learning demanded the implementation of totally new methodologies to cover the needs of students. An online survey resulted in approaching Zoom and Google Classroom as the most preferable and efficient in providing live learning opportunities. To reach valid data of the study, qualitative and quantitative analyses of off- and online exams were applied. The majority of respondents estimated the online exam format similar to face-to-face one and the practical value of online learning following the university shutdown as beneficial. Against this background, in post-pandemic reality, Kyiv University has applied urgent management practices to alleviate the impact of the COVID-19 pandemic on both students and staff. The new platform encompasses various modes and has covered all students and the teaching staff of Kyiv University from September 1 this year. The KNU Education Online digital platform as a multifunctional school network is a fruitful university decision to work out the strategy beneficial in the long run.
Multimodality during fixation – Part II: Evidence for multimodality in spatial precision-related distributions and impact on precision estimates

This paper is a follow-on to our earlier paper (7), which focused on the multimodality of angular offsets. This paper applies the same analysis to the measurement of spatial precision. Following the literature, we refer these measurements as estimates of device precision, but, in fact, subject characteristics clearly affect the measurements. One typical measure of the spatial precision of an eye-tracking device is the standard deviation (SD) of the position signals (horizontal and vertical) during a fixation. The SD is a highly interpretable measure of spread if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the SD is less interpretable. We will present evidence that the majority of such distributions are multimodal (68-70% strongly multimodal). Only 21-23% of position distributions were unimodal. We present an alternative method for measuring precision that is appropriate for both unimodal and multimodal distributions. This alternative method produces precision estimates that are substantially smaller than classic measures. We present illustrations of both unimodality and multimodality with either drift or a microsaccade present during fixation. At present, these observations apply only to the EyeLink 1000, and the subjects evaluated herein.
Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure

Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculation of the weight matrices. First, the weight matrices are divided into square sub-blocks based on spatial aggregation. Second, a coarse-grained block pruning procedure is utilized to scale down the model parameters. Finally, the BSR storage format, which is much more friendly to block sparse matrix storage and computation, is employed to store these pruned dense weight blocks to speed up the calculation. In the following experiments on MNIST and Fashion-MNIST datasets, the trend of accuracies with different pruning granularities and different sparsity is explored in order to analyze our method. The experimental results show that our coarse-grained block pruning method can compress the network and can reduce the computational cost without greatly degrading the classification accuracy. The experiment on the CIFAR-10 dataset shows that our block pruning strategy can combine well with the convolutional networks.
Modified Cache-Template Attack on AES

While it has been known for a long time that the cache behavior is a powerful source of the information leakage, more realistic attack scenarios have received a lot of attention by the cryptographic community. To develop practical cache-based attacks, there is an increasingly need to automate the process of finding exploitable cache-based side-channels in computer systems. Cache template attack is a generic technique that utilizes Flush+Reload attack in order to automatically exploit cache vulnerability of Intel platforms. Cache template attack on T-table-based AES implementation consists of two phases including the profiling phase and the key exploitation phase.Profiling is a preprocessing phase to monitor dependencies between the secret key and behavior of the cache memory. In addition, the addresses of T-tables can be obtained automatically.In the key exploitation phase, most significant bits (MSBs) of the secret key bytes are retrieved by monitoring exploitable addresses. In this paper, we propose a simple yet effective searching technique which accelerates the profiling phase by a factor of at most 64. To verify the theoretical model of our technique, we implement the described attack on AES. The experimental results confirmed a shorter runtime of the attack in comparison to the original attack.
Cyber Crime: Women Combating with the Negative Effect of Technology in the Era of Globalisation

In the modern era of globalization, there has been an abundant change in the lifestyle of individuals and with the advancement of technology, it has made man directly or indirectly depend on internet for all their needs. Internet has given easy access to everything and is used in almost every sphere. With the advancement of internet, it has had both the negative as well as positive effects on the lifestyle and working conditions of individuals. This particular growth in technology and internet driven lifestyle has also posed a concern in the form ofcyber-crime. Cyber-crime is defined as an unlawful act where the internet and the computer system is used either as a tool or a target or both. It can be committed in various forms like hacking, phishing, cyber terrorism, spoofing, stalking, data diddling etc. But as far as women safety is concerned, due to increase in cyber-crime “WOMEN” are badly affected an becomes the sufferer of cyber-crimes like pornography, cyber-stalking etc. which may result in serious violent acts like serious physical harm or harassment or any medium of sexual abuse. Information Technology Act of 2000 provides the laws pertaining to such crimes and the punishments prescribed therein but however, being a specific law on this subject matter, it fails to provide mechanisms and punishments for crimes committed specifically against women. For all such matters one has to refer to the more general law of the land i.e. Indian Penal Code. Thus, regulation and implementation of IT Act is much needed to safeguard the rights of women. Therefore, with this paper the researchers want to enlighten the problem and need of stricter laws relating to cyber-crime, which will help the victims to fight back against such heinous and devastating crime growing in a society with rapid and continuous phenomenon of globalization.
Application of Learning Videos and Quizizz in Increasing Students Interest in Learning English in Middle Schools

The government's policy towards the education sector during the pandemic by imposing Distance Learning is still experiencing many obstacles. Many students have difficulty understanding the concept of monotonous lessons and learning so that students become bored and lose interest in learning. This study aims to see the application of learning videos and Quizzes in increasing students' interest in learning English in the 9th graders. During the learning activities, students first listened to the learning video, then after understanding the material in the video, students were asked to work on practice questions on Quizizz. This study used qualitative descriptive research, with the research subject being the 9th graders. The instrument used is a test instrument in the form of a quiz on the Quizizz application which consists of 15 questions, and interviews as a reference to determine students' understanding and learning interest in the use of learning videos and Quizizz applications. The results showed that the use of learning media using audio-visual media was quite effective in increasing students' interest in learning and understanding English subjects. Because students better understand the material in the form of videos, and also this Quizizz media is not only easy to use but also fun like playing a game. So that students do not feel burdened with assignments. Interactive and fun learning has an impact on increasing student interest in learning.
Definition of a lightweight cryptographic solution to secure health data on IoT and cloud

The Internet of Things (IoT) and the Cloud Computing are among the main technologies influencing the quality and efficiency of our social and economic activities. Their importance is quite notable in several areas such as remote medical care and healthcare monitoring systems. These later face difficulties most of them are the expensive costs for the chronic diseases and the hospitals charges specially during the pandemic time. All these issues have led to the creation of new solutions for enhancing the quality of the healthcare systems. In this paper, we focus on the network architecture security including healthcare monitoring systems based on IoT and Cloud Computing. We present a recent review on the research field and we propose a secure solution based on lightweight cryptography in accordance with the limited resource environments within which health data are exchanged. The proposed solution is guaranteeing data confidentiality and ensuring integrity and authenticity.
The Attitude of Nursing Students Towards Mobile Learning

Purpose: This study was conducted to determine the attitudes of nursing students towards mobile learning.Method: This descriptive study was conducted with 369 students studying at the nursing department of a university who voluntarily accepted to participate in the study.Personal Information Form and "Attitude Scale Towards Mobile