Reviewerly/exHarmony huggingface dataset:
Leveraging Big Data Analytics for Cultural Teaching Competence in international Chinese Linguistic Learning using Weighted Random Forest Model

The teaching of Chinese language and culture has gained significant importance on the global stage due to China's growing influence in various domains. International Chinese language teachers play a crucial role in promoting cross-cultural understanding and facilitating effective communication between Chinese and non-Chinese speakers. This paper aims to explore the concept of cultural teaching competence for international Chinese language teachers, with a focus on the Chinese national context and the application of a cultural teaching framework supported by big data analytics. The model uses the Integrated Machine Learning Teaching Framework (iMLTF). The model constructs the cultural teaching framework for the evaluation of the International Chinese Language based on Chinese National Context. The iMLTF model uses the Multivariant examination integrated with the Weighted Random Forest model. The simulation analysis expressed that the proposed iMLTF model achieves the higher classification accuracy value of 98% compared with the conventional state-of-art techniques.
An Intelligent Framework for Estimating Software Development Projects using Machine Learning

The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs.
An Efficient Data Analytics and Optimized Algorithm for Enhancing the Performance of Image Segmentation Using Deep Learning Model

Image segmentation is the key topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality among many others. There are numerous algorithms are developed in the literature for image segmentation. This paper provides a broad spectrum of pioneering works for instance and semantic level segmentation with mask Region based Convolution Neural Network with Monarch butterfly Optimization (RCNN-MBO) architecture. The system is initially constructed in a Python environment with images of people and animals being input. Remove the unnecessary data from the gathered datasets during the pre-processing stage. Next, use a stochastic threshold function to segment the image. Then update the segmented images into a designed model for detecting and classifying a group of images. The main goal of the designed approach is to attain accurate prediction results also improve the performance of the designed model by attaining better results. To enhance the performance, two activation functions were used and MBO fitness is updated in the classification layer. It improves the prediction results and takes less time to detect and classify images. Finally, the experimental outcomes show the reliability of the designed approach by other conventional techniques in terms of accuracy, precision, sensitivity, specificity, F-measure, error rate, and computation time.
STEGANOGRAPHY CAPACITY ANALYSIS OF TEXT IN TEXT WITH SENTENCE STRUCTURE IN INDONESIAN

Data security issues become very important when the computer has been used as a communication tool on a global network (internet). One method that is quite popular for securing data from irresponsible parties is text-based steganography, where confidential data or information is hidden or inserted into other text media so as not to arouse suspicion from other parties. Based on the input data calculated on the number of words, it has a significant influence on the results of the stegotext. This can be seen from the comparison of the length of the input message which is calculated in the number of words with the length of the output message (stegoteks) which is influenced by the length of the style (pattern) of the sentence structure used. For input data with a word length of 1, the average capacity of the stegotext is 8 to 9 words or 12.4%, for input word length 2, the average capacity of the stegotext is 22 words or 8.85% and the input word length is 4 then the capacity of the stegotext is between 30 to 33 words or equivalent to 12.91%. Keywords :Text Steganography; Sentence Pattern; Payload Balancing Capacity
Quantum Annealing with Inequality Constraints: The Set Cover Problem

Abstract Quantum annealing is a promising method for solving hard optimization problems by transforming them into quadratic unconstrained binary optimization (QUBO) problems. However, when constraints are involved, particularly multiple inequality constraints, incorporating them into the objective function poses challenges. In this paper, the authors present two novel approaches for solving problems with multiple inequality constraints on a quantum annealer and apply them to the set cover problem (SCP). The first approach uses the augmented Lagrangian method to represent the constraints, while the second approach employs a higher‐order binary optimization (HUBO) formulation. The experiments show that both approaches outperform the standard approach for solving the SCP on the D‐Wave Advantage quantum annealer. The HUBO formulation performs slightly better than the augmented Lagrangian method in solving the SCP, but its scalability in terms of embeddability in the quantum chip is worse. The results demonstrate that the proposed augmented Lagrangian and HUBO methods can successfully implement a large number of inequality constraints, making them applicable to a broad range of constrained problems beyond the SCP.
Does the Perception of Unemployment Risk Affect the Willingness of Urban Citizenship of Chinese New-Generation Migrant Workers?

Presently, the pace of transformation within traditional sectors of the Chinese economy is accelerating, resulting in the gradual demise of traditional occupations. Consequently, the emerging cohort of Chinese migrant workers confronts unprecedented challenges pertaining to their professional prospects and assimilation into urban society. In order to examine the impact of perceived unemployment risk on the inclination of new-generation migrant workers to embrace urban citizenship, we introduce the notions of environmental dynamism and active skill development as moderating variables. Employing multiple regression analysis, we construct a dual moderating model. The results of our study indicate that the perception of unemployment risk positively predicts the willingness of new-generation migrant workers to embrace urban citizenship. Moreover, we observe that environmental dynamism exerts a positive moderating influence on the relationship between unemployment risk perception and citizenship inclination. Additionally, active skill development serves to positively moderate the moderating effect of environmental dynamism. This study not only reveals the unique influence of subjective unemployment risk perception on the inclination towards urban citizenship but also sheds light on the role played by environmental dynamism and active skill development in this context. The findings of this research further enhance the theoretical framework pertaining to the study of urban citizenship inclination among new-generation Chinese migrant workers.
The Reason for Non-Reporting Crime Victimization: A Study on Urban Area in Bangladesh

This study has mainly explored the current extend of dark figure of crime in urban areas in Bangladesh. It was exploratory in nature. The samples were selected through randomly using a clustered sampling technique from 14 particular areas and respondents were selected as victimized person or their household heads in this study. It has conducted with a structured and standardized survey questionnaire for data collection and using SPSS statistical tools for analyzing data. The socio demographic findings of the study is that the total number of the respondents was 3,957, among them most of them are female about 57% who are belonging in a Muslim family with average family size of 5 members with average age of 37 years, with average income 37,000 TK and living in their own house in urban area and most of them are housewife and about 52% have completed the secondary level of education. On the other hand the major findings are indicated as about 74% did not go to any institution for reporting, while only 26% are approached for legal help. As per the study findings, theft constitutes the highest number for victimization, where 79% percent of the theft victim did not report for it. Even though within the total type of crime, about 49 percent of the victim who did not report for the crime found out to be a theft victim. There are various reasons have found for dark figure of crime such as Police behavior is not friendly to reporting, victim considered the crime as insignificant and police service needed monetary involvement etc. but most of respondents have said they wanted to avoid the inconvenience of calling the police in such petty cases (About 25.7%). Finally, in this study, it has been explained the findings as the three main perspectives such as problem of existing criminal justice system, victim’s social and also their personal perspectives.
Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification

Image watermarking preserves digital content. This study introduces a new watermarking approach employing Sub-Band Discrete Cosine Transform and Deep neural networks, GRNN and CNN. The method embeds robust, invisible watermarks in greyscale photos and compares the two neural network topologies. The watermark is added using sub-band DCT. Watermark embedding in low-frequency sub-bands resists photo processing. The binary watermark modifies sub-band DCT coefficients to determine embedding intensity, resisting signal deterioration, and assaults. GRNN and CNN neural networks extract watermarks accurately. CNN extracts hierarchical features from images, enabling robust watermark recovery even under distortions, whereas non-parametric GRNN stores the whole training data to create predictions. The watermarking approach is tested on several greyscale photos. PSNR, SSIM, MSE, and NCC measure performance. The watermark tests noise addition, compression, and filtering. Compare GRNN and CNN's watermark extraction strengths and shortcomings to assess image watermarking suitability.
Analysis of control methods’ influence on physical Human – Human interaction

In physical Human – Human Interaction (pHHI) research, actuators are used to construct physical interactions. Various types of actuator control methods have been adopted by researchers, such as Position – Position (PP) control, Admittance control, and Bilateral control. However, how different control methods affect human operation and interaction characteristics has not been clarified. This study aimed to clarify whether pHHI depends on the control method. In the experiment, a Leader – Follower system was constructed using four control methods: PP control, Admittance control (by PD controller), Admittance control (by Acceleration Based Controller), and Bilateral control, and subjects executed the cooperative task. The objective evaluation showed that the admittance control resulted in a more significant angular error between interfaces than the PP and Bilateral control. Also, the subjective evaluation results showed that the admittance control decreased both the naturalness and quality of the interface perceived by the user. Admittance control has been unstable depending on virtual dynamics, which may have induced the drop observed in the subjective evaluation scores.
Peran Dinas Perpustakaan Dan Kearsipan Kabupaten Labuhanbatu Selatan dalam implementasi sustainable devolepment goals

The Library and Archives Service of South Labuhanbatu Regency is an agency implementing local government affairs in the field of libraries and archives. The purpose of this research is to find out the various efforts of the South Labuhanbatu Regency Library and Archives Service in implementing work programs that are in line with the SDGs. This study uses data collection techniques through observation, interviews, and documentation. In this study, researchers used a data validity test through data triangulation. The data analysis used refers to the Miles and Huberman model. In this study there were 4 people as informants, consisting of librarians at the Library and Archives Service of South Labuhanbatu Regency. Based on the research that has been carried out, it can be concluded that the Library and Archives Service of Labuhanbatu Selatan Regency has an important role in supporting the SDGs, especially in pillars 1 and 4, namely poverty alleviation and delivery of quality education. The results of this research can be used by libraries to develop information services that support the SDGs.
Lip print patterns in forensic investigation: Detectability, sex differentiation, and reliability

Lip print patterns are increasingly recognized as a vital resource within forensic investigations, providing a distinctive means for identifying individuals. This extensive investigation examines the detectability, possibility for sex distinction and forensic consequences of lip print patterns. Through both direct visualization and scoring procedures, a comprehensive examination was undertaken to analyze the intricate patterns of lip prints exhibited by male and female participants in Indonesia. The results of our study provide clear evidence for the infrequency of similar lip print patterns among individuals, confirming previous research conducted by Suzuki and Tsuchihashi. It is worth mentioning that type II lip print type was the prevailing type in Indonesian males and females. Furthermore, noticeable variances based on sex were seen in the upper left (UL) and lower middle (LM) quadrants. This research emphasizes the crucial significance of lip prints in criminal investigations, emphasizing their wide range of uses and relevance in diverse forensic contexts. The distinctiveness, singular nature, consistency, genetic transmission, and wide range of lip lines establish them as dependable means for personal identification. Lip print analysis has promise as a prospective means of scientific validation in forthcoming legal procedures, with the capacity to augment forensic methodologies and offer valuable contributions to the judicial process. The results reported in this study provide a foundation for creating a comprehensive set of criteria for assessing the lip line, which can serve as a valuable tool for the progression of forensic science and the pursuit of justice.
A study on nursing assist system using deep reinforcement learning for prevention of patients falls

We have been studying two main methods for preventing patient falls and reducing the workload of medical staff at medical and nursing care facilities, using artificial intelligence and robotics technologies. One is a realtime fall risk assessment of patients and their surrounding environment using deep learning. The other is a method to present recommendations and nursing assist to medical staff for optimal interventions for patients using deep reinforcement learning to reduce the risk. In this paper, we analyze the results of incorporating nursing theories into deep reinforcement learning, and introduce the effectiveness, limitations, and future prospects of the application. First, as applications problem of deep reinforcement learning, we apply representative nursing theories of Roy, Johnson, Orem, Henderson, Peplau, Orlando, Abdellah, King, Wiedenbach, Travelbee, Leininger, Newman, to achieve consistency with the medical field. Next,as an effect of their application, we show that it is possible to clarify what information from the clinical field should be used for the main design parameters of deep reinforcement learning, i.e., state, action, and reward. As a limitation of the application, the conventional training and evaluation by reproducing scenes in dynamic 3D simulations can be used to evaluate operational efficiency strategies and safety based on environmental sensing data and external feedback from patients on intervention. However, it is necessary to incorporate internal feedback into the evaluation of the validity of the actions of nursing assist robots and medical staff. Finally, the use of a virtual environment, such as a metaverse with avatars, will suggest future work to solve this problem
英語発話時の口唇運動の計測に関する研究

The purpose of this study is to improve the English pronunciation of Japanese people by focusing on the English pronunciation method that Japanese people have difficulty with, and by analyzing various approaches such as EMG analysis, image analysis, and voice analysis in speaking English. Experiments were conducted by measuring the EMG signals around the oral cavity during the pronunciation of English vowels, video of mouth movements, and measurements of the pronounced voices. The experimental results were analyzed by each method, and the possibility of vowel identification was verified. The results showed that it was possible to identify some vowels, but not all.
視覚的探索課題における認知負荷がリダイレクテッドウォーキングの知覚閾値に与える影響

A new method of movement in Virtual Reality (VR) is redirected walking (RDW), which is implemented by manipulating the magnification of movement in VR in relation to the same movement in reality, such as taking two steps forward in the VR space when one step forward is taken in reality. RDW is implemented by manipulating the user's viewpoint in VR according to the magnification of the movement. However, if the manipulation is perceived, it causes discomfort. Therefore, the amount of manipulation of RDW is limited based on the user's perception threshold. Although RDW thresholds have been studied in previous research works, they might fluctuate depending on the cognitive load caused by different tasks in VR. Because tasks are rarely performed by walking alone in VR, RDW must be designed by considering the cognitive load in dual tasks. Therefore, in this study, we used a visual search task, which is an important task for humans, as a secondary task to quantitatively evaluate the relationship between the cognitive load (memory load and visual load) and the perceptual threshold in the visual search task, and to study the effect of cognitive load on the perception of RDW. The experimental results showed that the perceptual threshold of RDW increased when the memory load was increased. However, no change in the perceptual threshold of RDW was observed when the visual load was increased. This suggests that the memory load dominates the effect of visual search tasks on RDW and that the increased memory load increases the perceived threshold of RDW. In addition, the method of exploration can affect the perception of RDW because vestibular, visual, and somatosensory information differ depending on the way the body is moved during the visual exploration task.
Sign Language Recognition utilizing LSTM and Mediapipe for Dynamic Gestures of ISL

Humans, in general, are social creatures who communicate themselves through an assortment of spoken languages. Deaf and Mute individuals converse in a manner that's comparable, however many others are ignorant of their sign language. As a result, there is a need to develop a system that facilitates communication among the hearing and hard-of-hearing communities. This research offers a real-time Indian Sign Language (ISL) recognition system for 24 dynamic signals using the Mediapipe framework and an LSTM network. The method proposed in the study involves training a LSTM to differentiate between different signs using a dataset created of 24 dynamic gesture signs. To accomplish dataset creation, a pre-trained Holistic model of the Mediapipe framework is used as a feature extractor. The results of the study demonstrate that the above approach achieves 97% test accuracy.
Dealing with Negative Samples with Multi-Task Learning on Span-Based Joint Entity-Relation Extraction

Recent span-based joint extraction models have demonstrated significant advantages in both entity recognition and relation extraction. These models treat text spans as candidate entities, and span pairs as candidate relationship tuples, achieving state-of-the-art results on datasets like ADE. However, these models encounter a significant number of non-entity spans or irrelevant span pairs during the tasks, impairing model performance significantly. To address this issue, this paper introduces a span-based multitask entity-relation joint extraction model. This approach employs the multitask learning to alleviate the impact of negative samples on entity and relation classifiers. Additionally, we leverage the Intersection over Union(IoU) concept to introduce the positional information into the entity classifier, achieving a span boundary detection. Furthermore, by incorporating the entity Logits predicted by the entity classifier into the embedded representation of entity pairs, the semantic input for the relation classifier is enriched. Experimental results demonstrate that our proposed SpERT.MT model can effectively mitigate the adverse effects of excessive negative samples on the model performance. Furthermore, the model demonstrated commendable F1 scores of 73.61%, 53.72%, and 83.72% on three widely employed public datasets, namely CoNLL04, SciERC, and ADE, respectively.
Decoding the Encoded – Linguistic Secrets of Language Models: A Systematic Literature Review

Language models’ growing role in natural language processing neces- sitates a deeper understanding of their linguistic knowledge. Linguistic probing tasks have become crucial for model explainability, designed to evaluate models’ understanding of vari-ous linguistic phenomena. Objective: This systematic review critically assesses the linguistic knowledge of language models via linguistic probing, providing a comprehensive overview ofthe understood linguistic phenomena and identifying future research areas. Method: We performed an extensive search of relevant academic databases and analyzed 57 articles pub- lished between October 2018 and October 2022. Results: While language models exhibit extensive linguistic knowledge, limitations persist in their comprehension of specific phe- nomena. The review also points to a need for consensus on evaluating language models’ linguistic knowledge and the linguistic terminology used. Conclusion: Our review offers an extensive look into linguistic knowledge of language models through linguistic probing tasks. This study underscores the importance of understanding these models’ linguistic capabilities for effective use in NLP applications and for fostering more explainable AI systems.