Reviewerly/exHarmony huggingface dataset:
Internal Environment of the Organization and Strategic Choice in Cement Manufacturing Firms in Kenya

Purpose: The objective of this research is to look into the internal environment elements of the organization that have an impact on strategic choice in the Kenyan cement industry.&#x0D; Methodology: A descriptive research design was used. The target population consisted of six Kenyan cement manufacturing firms. To collect data, a census and a questionnaire were used. The collection tool was piloted prior to the actual study to determine its validity and reliability. The arithmetic standard deviation and mean were utilized to assess central tendency and dispersion.&#x0D; Findings: Research findings are expected to significantly inform cement manufacturing firms and accelerate infrastructural development in Kenya as well as East Africa. Organizational structure was also positively correlated at (r =.368 and P&gt;0.05), indicating a positive relationship, and organizational culture was positively correlated at (r =.358 and P &gt;0.05), indicating a positive (+ve) and significant relationship. At (r) =.597 and P&gt;0.05, managerial leadership styles had the greatest positive influence on strategic choice. The results of the study on organizational resources and strategic choice in Kenyan cement manufacturing firms revealed a moderately positive relationship that was statistically significant (r=.436 and P&gt;0.05). According to the study, organizational structure, culture, managerial leadership style, and resources all have a positive (+ve) and significant effect on strategic choice.&#x0D; Unique contribution to theory, practice and policy (recommendation): The research hopes to inform policy on the strategic importance of supporting and strengthening strategic choices through adapting dynamic organizational structures and implementation policies for Kenyan cement manufacturing and beyond. The study will further enable top management of Kenyan cement manufacturing firms unlock the value of their investment through effective strategic leadership. The study will help improve the management and development of strategic areas of capacity building and improve policies geared towards enhancing the strategic choice.
A Diachronic Shift in Japanese Word Length Distribution

Given the typological differences between the Indo-European languages, which are fusional, and Japanese, which is agglutinative, the debate around the measuring unit of Japanese word length is unsurprising. This study delved into diachronic issues and calculated word length in Old, Early Middle, Middle, Early Modern, and Modern Japanese using data from eight writing systems, including 21 genres. This study aimed to clarify how word length distribution has shifted throughout history. The findings revealed that word length is associated with the writing system. Old Japanese bore the longest length, as it was utterly logographic. Since Early Middle Japanese, Japanese text has been written using a phonographic and logographic mix, and word length appears shorter. Furthermore, word length is associated with the diversity of genres. Moreover, an investigation of word length and frequency indicated that textbooks, sharehon, ninjoohon, and tales, which appeared after the Nara Period and used mixed Chinese character and kana writing, fit into the power law function.
Prediction model of motorcycle accident in economic and driving behaviour factors

The number of motorized vehicles, especially motorcycles, is also offset by increased traffic accidents. As is known, road accidents essentially depend on four interrelated factors: human behavior, vehicle efficiency, environmental conditions, and the characteristics of the infrastructure. However, most accidents are attributable to the first three factors, almost always to improper user behavior. This study aims to determine motorcyclists’ socio-economic characteristics and conduct on the intensity of accidents. The research location is on the Pandaan-Purwosari National Road, Pasuruan Regency, Section 094‑098 (Surabaya-Malang). Three hundred forty respondents are motorcyclists who have experienced accidents in this segment. The research method is interviews and questionnaires—data analysis using Structure Equation Modeling (SEM), with software SmartPLS (Partial Least Square). The result of accident modeling Y=0.299X1+0.154X2+0.077X3+0.554X4. The first biggest influence on the chance of an accident is the characteristics of driving behavior (X4) exceeding speed (X4.10). The more often the rider exceeds the rate, the higher the chance of an accident. The second most significant influence of socio-economic characteristics (X1) is the age indicator (X1.2), the more mobility in the productive age, the higher the risk of accidents.
SELF: A Stacked-based Ensemble Learning Framework for Breast Cancer Classification

Abstract Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, about 2.3 million cases and 685000 deaths have been reported worldwide. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machine-based smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stacked-based ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computer-aided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and SELF selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and the logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on the BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition

Named Entity Recognition (NER) systems often demonstrate great performance on in-distribution data, but perform poorly on examples drawn from a shifted distribution. One way to evaluate the generalization ability of NER models is to use adversarial examples, on which the specific variations associated with named entities are rarely considered. To this end, we propose leveraging expert-guided heuristics to change the entity tokens and their surrounding contexts thereby altering their entity types as adversarial attacks. Using expert-guided heuristics, we augmented the CoNLL 2003 test set and manually annotated it to construct a high-quality challenging set. We found that state-of-the-art NER systems trained on CoNLL 2003 training data drop performance dramatically on our challenging set. By training on adversarial augmented training examples and using mixup for regularization, we were able to significantly improve the performance on the challenging set as well as improve out-of-domain generalization which we evaluated by using OntoNotes data. We have publicly released our dataset and code at https://github.com/GT-SALT/Guided-Adversarial-Augmentation.
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is partly due to the lack of reasoning capabilities of dialogue platforms and the hand-crafted rules that require extensive labor. One possible way to improve user experience and relieve the manual efforts of designers is to build an end-to-end dialogue system that can do reasoning itself while perceiving user's utterances. In this work, we propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. Our proposed method allows a single transformer model to directly walk on a large-scale knowledge graph to generate responses. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs. We investigate the reasoning abilities of the proposed method on both task-oriented and domain-specific chit-chat dialogues. Empirical results show that this method can effectively and efficiently incorporate a knowledge graph into a dialogue system with fully-interpretable reasoning paths.
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a semi-supervised framework for DocRE with three novel components. Firstly, we use an axial attention module for learning the interdependency among entity-pairs, which improves the performance on two-hop relations. Secondly, we propose an adaptive focal loss to tackle the class imbalance problem of DocRE. Lastly, we use knowledge distillation to overcome the differences between human annotated data and distantly supervised data. We conducted experiments on two DocRE datasets. Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard. Our code and data will be released at https://github.com/tonytan48/KD-DocRE.
Better Language Model with Hypernym Class Prediction

Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs. In this study, we revisit this approach in the context of neural LMs. We hypothesize that class-based prediction leads to an implicit context aggregation for similar words and thus can improve generalization for rare words. We map words that have a common WordNet hypernym to the same class and train large neural LMs by gradually annealing from predicting the class to token prediction during training. Empirically, this curriculum learning strategy consistently improves perplexity over various large, highly-performant state-of-the-art Transformer-based models on two datasets, WikiText-103 and Arxiv. Our analysis shows that the performance improvement is achieved without sacrificing performance on rare words. Finally, we document other attempts that failed to yield empirical gains, and discuss future directions for the adoption of class-based LMs on a larger scale.
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning

Autonomous vehicles utilize urban scene segmentation to understand the real world like a human and react accordingly. Semantic segmentation of normal scenes has experienced a remarkable rise in accuracy on conventional benchmarks. However, a significant portion of real-life accidents features abnormal scenes, such as those with object deformations, overturns, and unexpected traffic behaviors. Since even small mis-segmentation of driving scenes can lead to serious threats to human lives, the robustness of such models in accident scenarios is an extremely important factor in ensuring safety of intelligent transportation systems. In this paper, we propose a Multi-source Meta-learning Unsupervised Domain Adaptation (MMUDA) framework, to improve the generalization of segmentation transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain Mixed Sampling to augment the images of multiple-source domains (normal scenes) with the target data appearances (abnormal scenes). To train our model, we intertwine and study a meta-learning strategy in the multi-source setting for robustifying the segmentation results. We further enhance the segmentation backbone (SegFormer) with a HybridASPP decoder design, featuring large window attention spatial pyramid pooling and strip pooling, to efficiently aggregate long-range contextual dependencies. Our approach achieves a mIoU score of 46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art model by more than 7.50%. Code will be made publicly available at https://github.com/xinyu-laura/MMUDA.
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers

Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings. As most research on active learning has been carried out before transformer-based language models ("transformers") became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date. This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings. For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers. In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks. For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification.
Investigating user perceptions of commercial virtual assistants: A qualitative study

As commercial virtual assistants become an integrated part of almost every smart device that we use on a daily basis, including but not limited to smartphones, speakers, personal computers, watches, TVs, and TV sticks, there are pressing questions that call for the study of how participants perceive commercial virtual assistants and what relational roles they assign to them. Furthermore, it is crucial to study which characteristics of commercial virtual assistants (both existing ones and those envisioned for the future) are perceived as important for establishing affective interaction with commercial virtual assistants. By conducting 26 interviews and performing content analysis of the interview transcripts, this study investigates how the participants in the study perceive, engage, and interact with a variety of commercial virtual assistants. The results lead to better understanding of whether forms of attachment are established or if some sort of relationship is produced between humans and commercial virtual assistants. Key takeaways from our results indicate that, in their current state, the lack of humanlike characteristics in commercial virtual assistants prevents users from forming an emotional attachment to commercial virtual assistants, but this does not deter them from using anthropomorphic language to describe commercial virtual assistants. Yet, our results reveal that users expect commercial virtual assistants’ attributes to be more humanlike in the future.
Using XAI Techniques to Persuade Text Classifier Results: A Case Study of Covid-19 Tweets

Background: To offer a transparent decision support system able of classifying tweets' sentiment into positive, neutral, and negative sentiment and explains the prediction result by XAI techniques Methods: We started by data preprocessing phase.For data representation, we used TF-IDF, and we applied four machine-learning algorithms including Naive Bayes, random forest, logistic regression, and support vector machine, as well as four deep learning RNN, LSTM, GRU, and Bi-directional RNN.To raise model trust, we used LIME and SHAP to improve model explainability.Findings: The empirical findings show that the Logistic Regression model and SVM model using the TF-IDF feature extraction approach have the best performance when compared to the other models, with an average accuracy of 84% and 86% respectively.The data balancing step pushed the accuracy of the Random Forest model from 47% to 73%, other models slightly changed.The performance of deep learning models was better than traditional machine learning models, LSTM and GRU achieve approximately 78%, and Bi-directional RNN achieve 79% for dataset 2. Novelty and applications: we propose a highly accurate approach for SA which has been tested on two datasets.Also, to increase trust in model prediction, we explain the predicted sentiment.
Explaining with Diagrams for Promoting Deeper Learning: School Practice to Integrate Knowledge Across Subjects

Abstract Diagrams are considered a powerful strategy for understanding and problem-solving. Self-explanation is believed to be behind the effectiveness of diagrams. However, it is not clear to what extent students engage in self-explanation. The possibility that students do not always engage in self-explanation is suggested by empirical studies showing that diagrams do not always produce efficacious outcomes. Ichikawa [1] also argued that it is unclear how students interpret diagrams and discussed the need to have students explain themselves using diagrams. In science learning, diagrams effectively help students learn the principles behind phenomena. Having students use diagrams to explain phenomena may help them understand the principles of the phenomena correctly and integrate their knowledge effectively across subject areas. Therefore, in this study, we developed lesson instructions in which students were required to explain the phenomenon using diagrams after the teacher had explained it. Students were also given the opportunity to solve the problem collaboratively after the explanation had been provided. The study involved 71 8th-grade students in one school. Fifty-eight same grade students from a traditional public school also participated in the study and served as the control group. A “basic knowledge test”, which tested students’ knowledge in a fill-in-the-blank format, and a “principle understanding test” and “transfer test,” which tested students’ knowledge in an explanation format, were administered. The results demonstrated no significant difference in the basic knowledge test, but the score in the principle understanding test and the transfer knowledge test was higher for the students who received the experimental instruction. This study indicates that deeper understanding is facilitated by combining the experience of using diagrams with peer explanation.
CHAPTER 4.4. HUMANIZATION OF EDUCATION BY FORMING A PICTURE OF THE WORLD IN THE SUBJECTS OF LEARNING BY MEANS OF INFORMATION AND COMMUNICATION TECHNOLOGIES

The chapter is devoted to the humanization of education by forming a picture of the world in the subjects of learning by means of information and communication technologies. The following questions are considered: 1) general characteristics of the “humanization” and “humanitarianization of education” notions in the context of problem solving of innovative learning; 2) ways of rationalization in means of world cognition and the personality’s cognitive structures formation; 3) functional features in human semantic memory and general characteristics of the personality’s cognitive structures.
The LSU Department of Classics in the Pre-War and Post-War Period

This article analyzes the history of the Department of Classical Philology at Leningrad University from 1938 to 1946, except when the department was evacuated to Saratov during the Blockade of Leningrad. Documentation from the archival collections (the Central State Archive of St. Petersburg and the Joint Archive of St. Petersburg State University) and memoirs (mainly those of O. M. Freidenberg) were used. The study of the curriculum of the department, introduced in 1938, allowed us to draw a conclusion about the comprehensive historical-philological training of students, which had its roots in the pre-revolutionary times. It has been established that the policy of the head of the department, O. M. Freidenberg, of promoting young personnel in the department (with the handover of courses to them, not always rationally justified, in the main department) contributed to a conflict situation which led to a public discussion in May 1946 at the academic council of the department she headed. This meeting should not be categorized as an ideological campaign: it was based on professional disagreement. The scandalous nature of B. L. Galerkina’s and N. V. Vulich’s defense was due not so much to personal motives as to scientific differences in the views on the tasks of classical philology between O. M. Freidenberg and representatives of the “old” school. Dissertations turned out to be hastily done, the reason for which was the material factor.
To Determine the Effectiveness of Modular Approach in Teaching Matter and Change in it’s States in General Science for Elementary Classes

Module is a unit of work in a course of instruction that is virtually self-contained and a method of teaching that is based on the building up skills and knowledge in discrete units. Module is self-instructional package; it enables the learner to learn independently. The aim of this study was to investigate the effectiveness of modular approach in teaching matter and change in its states in general science for elementary classes. The major objectives of the study were (i) to determine the role of modular teaching in the academic achievement of students of science at elementary level, (ii) to find out whether the modular teaching is more effective than traditional methods. Study design was pre-test and post-test. All class 5th elementary school students studying general science subject were constitute the population of the study. 40 Students of class 5th studying in general science subject from Govt. Girls Primary School Ziarat Kaka Sahib No. 1 were served as sample of the study. Pre-test and post-test were used as research instrument and data was collected through pre-test and post-test. Collected data was analyzed and interpreted using T-test through SPSS. The analysis of the collected data revealed the following findings. Treatment of modular approach had great effect on experimental group and its academic achievement was better than that of control group. Teaching of G. Science through modular approach was more effective than traditional teaching approach. The study testified a collection of positive outcomes during application of modular approach. Therefore, elementary school teachers may use modular approach in science to improve the academic achievement of students. It is recommended that teachers may be recognized high level of communication through questioning as it endorses participation enhances learning and inspires students. It is recommended that teachers and students should follow modular approach to get maximum benefits from this innovation. Teachers training institutes may make modular approach as a part of their curriculum content and teacher educators may be trained in different model of modular approaches.