Reviewerly/exHarmony huggingface dataset:
EFFECT OF CORPORATE SOCIAL RESPONSIBILITY COSTS ON THE FINANCIAL PERFORMANCE OF SUGAR MANUFACTURING COMPANIES IN KENYA

&lt;p&gt;The impact of CSR costs on financial performance is becoming increasingly important to a wide variety of corporate stakeholders. It is considered to be one of the instruments that businesses need to use to establish and maintain a calm working atmosphere. Engaging in corporate social responsibility and their disclosures are common in the recent business setting around the globe. Corporate social responsibility has become more significant over the past decades, and the validity of research on how it is associated with firm performance remains uncertain and incomplete. Many public and private Sugar manufacturing firms in Kenya have been facing poor financial performance, and some are closing up, even after receiving funding from the government. Consequently, this study aimed to establish the effects of corporate social responsibility cost on the financial performance of sugar manufacturing companies in Kenya. The specific objectives of this study were; to assess the effect of community development costs on the financial performance of sugar manufacturing companies in Kenya, to examine the effect of environmental responsibility costs on the financial performance of public and private sugar manufacturing companies in Kenya, and to evaluate the effect of economic responsibility costs on the financial performance of sugar manufacturing companies in Kenya. The study was guided by the stakeholder theory, legitimacy theory and theory of the firm. The study adopted a correlation research design. The study's target population was 12 sugar manufacturing companies in Kenya. A census sampling technique was used to select all 12 sugar manufacturing companies in Kenya. A secondary data collection sheet was used to document information from audited financial statements of the companies downloaded from the website from 2012 to 2021. Panel Data was analyzed using descriptive and inferential statistics. Panel regression analysis indicated that community development costs, economic responsibility costs and environmental responsibility costs had a significant effect on financial performance with a coefficient of 0.2146, 0.2896, 0.2728 and p-value of 0.006, 0.000, and 0.000. The study depicted that corporate social responsibility costs had a significant effect on financial performance with an R2 of 0.29 which implied that corporate social responsibility costs explain 29% of the financial performance of sugar companies in Kenya. Therefore, the study concluded that corporate social responsibility costs improve financial performance. The study recommended that the management of sugar companies should provide more funds to community development costs, policymakers should develop strategic policies that would enhance the practice of economic social responsibility, and respond to environmental demands for a cleaner environment.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;JEL:&lt;/strong&gt; Q10; L60; L66&lt;/p&gt;
Factors Associated With Speaking English as a Foreign Language Effectively in Higher Education

This study aimed to investigate the factors that influence the effective speaking of English as a foreign language (EFL) in higher education. Data were collected through an open-ended, in-depth interview with seven interviewees and analyzed using the thematic analysis (TA) technique. The results showed six significant themes related to effective student speaking: aptitude, motivation, opportunity, learning media, learning strategy, and language function. Future research must collect more diverse instances of effective speaking functions and components. Multiple examples of speaking in many genres developed by demographically diverse students in natural settings will provide more realistic and complete views of schoolchildren’s increasing ability to use nominal expressions to construct and communicate meaning for academic objectives.&#x0D; Keywords: Aptitude, EFL, Higher Education, Speaking Skills
GraphTune: An Efficient Dependency-Aware Substrate to Alleviate Irregularity in Concurrent Graph Processing

With the increasing need for graph analysis, massive Concurrent iterative Graph Processing (CGP) jobs are usually performed on the common large-scale real-world graph. Although several solutions have been proposed, these CGP jobs are not coordinated with the consideration of the inherent dependencies in graph data driven by graph topology. As a result, they suffer from redundant and fragmented accesses of the same underlying graph dispersed over distributed platform, because the same graph is typically irregularly traversed by these jobs along different paths at the same time. In this work, we develop GraphTune , which can be integrated into existing distributed graph processing systems, such as D-Galois, Gemini, PowerGraph, and Chaos, to efficiently perform CGP jobs and enhance system throughput. The key component of GraphTune is a dependency-aware synchronous execution engine in conjunction with several optimization strategies based on the constructed cross-iteration dependency graph of chunks. Specifically, GraphTune transparently regularizes the processing behavior of the CGP jobs in a novel synchronous way and assigns the chunks of graph data to be handled by them based on the topological order of the dependency graph so as to maximize the performance. In this way, it can transform the irregular accesses of the chunks into more regular ones so that as many CGP jobs as possible can fully share the data accesses to the common graph. Meanwhile, it also efficiently synchronizes the communications launched by different CGP jobs based on the dependency graph to minimize the communication cost. We integrate it into four cutting-edge distributed graph processing systems and a popular out-of-core graph processing system to demonstrate the efficiency of GraphTune. Experimental results show that GraphTune improves the throughput of CGP jobs by 3.1∼6.2, 3.8∼8.5, 3.5∼10.8, 4.3∼12.4, and 3.8∼6.9 times over D-Galois, Gemini, PowerGraph, Chaos, and GraphChi, respectively.
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1% on an independent test set. Among the three classes the best model gained the highest accuracy (99.3%) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients.
Mapping of Total Lime Using Remote Sensing and GIS Technology, Case Study: Garmian District, Kurdistan Region-Iraq

Calcium carbonate, lime, is a major component of the regolith, especially in arid and semi-arid regions. Lime affects soil characteristics, is a substantial store of earthly carbon, and is used in mineral investigation. Total lime is functioning component in the cultivation of Calcium carbonate and it is formed when calcium ions in hard water interact with carbonate ions to make limescale. Remote Sensing (RS) data and techniques integrated with Geographical Information System (GIS) play an important and prominent role in studying soil properties and how they are distributed by introducing the proportions and concentrations of chemical elements in the soil into GIS programs and then mapping the density of these elements and linking them spatially to the study area, and thus the spatial analysis of these elements. The study aims to map the distribution of the lime- rich regions utilizing remote sensing and geographical information system data and techniques integrated with field data observations in the Garmian region, Sulaimaniyah Province, Kurdistan Region, Iraq. Total of 80 well-distributed samples were tested precisely in the laboratory. Twelve samples were used as training samples in the classification, and the rest were used for checking the accuracy of the produced maps. The results also showed that Maximum likelihood classification is a reliable method that can be used on Landsat images to extract viable information on lime- rich regions and can predict the attendance and distribution of the lime-rich regions. It is clear that the amounts of calcium carbonate increase towards the study area south because the parent material is limestone and sandstone, which are rich in calcium carbonate, and also because of the low rates of rainfall in that area, which is not sufficient to dissolve the lime and wash it from the soil section.
Analisis Klasifikasi Broken Home pada Anak Menggunakan Metode Naïve Bayes Classifier

Broken home is a term that defines a situation in a family where most people handle no harmony, happiness, or peace. The impact of a broken home on a depressed family on children who can experience mental, emotional, and behavioral changes that are uncontrolled and undirected. Therefore, a classification is needed to categorize a child in a family as a broken home or not. The classification process will apply the Naïve Bayes Classifier classification method by taking into account the factors that refer to the statement that a child is called a broken home. With this classification, it is hoped that it can help know what and how a broken home child can be called a broken home and with this classification, it is expected that parents can minimize broken homes in children in the future by paying attention to the determining factors.
Penerapan Naïve Bayes pada Potensi Akademik Siswa SD Negeri 5 Singakerta

Student potential cannot only be measured based on the result of academic scores, and many things influence student academic determination. The purpose of this research is to prove that students' potential is influenced by many things, such as character, academic activity, socioeconomic status, and distance of residence. By using the naïve Bayes method and testing with the confusion matrix, it will give results for this research. The data is from V-grade students at SD Negeri 5 Singakerta, with 120 students assisted by the homeroom teacher. Based on the results of the tests that have been carried out using a data sample of 10 students and 1 data using the Naïve Bayes, it is obtained that students have academic potential, and the results with the confusion matrix are accuracy of 75%, precision of 81%, and recall of 89%. In this case, it can be concluded that the academic potential of students can not only be measured based on the results of the final grade, but many other factors have an effect, the application of the Naïve Bayes in students' academic potential is appropriate to use.
Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square

Government builds public facilities to support the needs of the community. The use of these public facilities needs to be re-evaluated, and one way to do it is through community response. Google Maps is one platform that receives the most responses from the community about location. Google Maps Reviews allow us to see how the public reacts to a location. Naïve Bayes method is used for classification in this study because it is one of the simple methods in machine learning that can be easily applied to several experiments conducted by the author. In the classification process, reviews produce many features that will be calculated based on their class. More features generated, more features processed too in the system. Chi-Square feature selection will be used to reduce features that have low dependence on the system. In this study, performance values will be calculated based on the experimental use of feature ratios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. The results show that the use of 10% Chi-Square features produces the best performance, with an accuracy rate of 86.94%, precision of 80.42%, recall of 80.42%, and f-measure of 80.42%.
Cross-domain Transfer of Defect Features in Technical Domains Based on Partial Target Data

A common challenge in real world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. In many technical domains, however, it is only the defect or worn reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class 1st dataset, a state-of-the-art labeled source domain dataset that contains highly related classes e.g., a related manufacturing error or wear defect but originates from a highly different domain e.g., different product, material, or appearance = 2nd dataset is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and by architecture robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.
Speech Recognition Technologies for Creating Knowledge Resources from Communications

Speech recognition technologies have been used in an increasing number of situations to support or substitute work traditionally done by people such as call analysis in contact centers and support for creating minutes of council meetings.We are promoting research and development of speech recognition technologies because we believe that speech significantly supports human activities, especially in business.In this article, we describe the historical development of speech recognition technologies cultivated by NTT laboratories and the future contributions and roles of such technologies in human and corporate activities.It also introduces the use of non-linguistic information read from voice, such as emotion, gender, and age, which has been attracting attention.
PENGARUH SOSIAL EKONOMI ORANG TUA DAN KARAKTERISTIK INDIVIDU TERHADAP HASIL BELAJAR SISWA KELAS XI DI SMA NEGERI 1 KOTA JAMBI

AbstractThis study aims to determine 1) The socio-economic description of parents, individual characteristics and learning outcomes. 2) The influence of parents' socioeconomic and learning outcomes. 3) the influence of individual characteristics on learning outcomes. 4) The influence of parents' socioeconomic and individual characteristics simultaneously on student learning outcomes. This research design uses quantitative research methods, with data collection techniques through observation, interviews, and questionnaires. Where the population in this study were 143 students with a sample of 105 students. The research instrument test technique used in this study is the Validity and Reliability Test, while the classical assumption test used is the Linearity Test, Multicollinearity Test, Normality Test, Homogeneity Test, and Multiple Regression Test. The results showed that: 1) The socio-economic of parents was included in the poor category with an average TCR value (X1) of 50.60%, (X2) 70.11%, and the students' learning outcomes in economics subjects with a value of less than 68 as many as 71 students while those who scored above 68 were 34 students. 2) There is a socio-economic influence of parents on learning outcomes which can be seen from the value of tcount &gt; ttable of parents' socioeconomic variables of 3.158 &gt; 1.658. 3) There is an influence of individual characteristics on learning outcomes that can be seen from the value of tcount &gt; ttable, namely 4.461 &gt; 1.658. 4) There is a simultaneous influence of parents' socioeconomic and individual characteristics on learning outcomes as indicated by the value of Fcount &gt; Ftable of 16.475 &gt; 3.04. Socio-economic parents affect the development of children's learning in the facilities and means of learning at home and at school. In addition to fulfilling the character building facilities, it also aims to improve better study habits, as well as the spirit of learning in students. Indirectly socio-economic conditions have an impact on students' self-change in order to get better learning outcomes than before.Keywords: Socio-Economic, Characteristics, Learning Outcomes.
IH-TCGAN: Time-Series Conditional Generative Adversarial Network with Improved Hausdorff Distance for Synthesizing Intention Recognition Data

As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.
Efektivitas Penggunaan Metode Quantum Reading Terhadap Kemampuan Membaca Pemahaman Siswa Sekolah Dasar

This study aims to describe the effectiveness of using the Quantum Reading on the reading comprehension ability of sixth grade elementary school students. The results showed that the use of the Quantum Reading on students' reading comprehension skills was positive, students' reading comprehension skills using the Quantum Reading method showed better learning outcomes than before using the Quantum Reading. The results of inferential statistical analysis using the t test formula, it is known that thecalculated obtained is 7.3 with a frequency of dk = 26-1 = 25, at a significance level of 5% obtained ttable = 2.05. So, tcount &gt; ttable or the null hypothesis (H0) is rejected and the alternative hypothesis (H1) is accepted. This proves that the use of the Quantum Reading effective on the reading comprehension ability of Grade VI elementary school students
Statistical Data Retrieval Technique in Astronomy Computational Physics

Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.