Reviewerly/exHarmony huggingface dataset:
THE ROLE OF CLOUD COMPUTING IN MACHINE LEARNING APPROACHES

This paper is presented to explain the intersection of cloud computing (CC) and machine learning (ML), focusing on their synergies, challenges and solutions. It shows the changes in the Internet service area led by cloud computing (CC) and the economic impact of data collection and analysis. The document specifically addresses security issues in distributed models and introduces the concept of edge computing as a version of cloud computing (CC) for time-sensitive data. In this paper, we are discussing about data encryption, distribution of rights, and transfer of data responsibility from service providers to end users. The document breaks down Cloud computing into service and delivery models, addressing security issues related to integrity, availability, and identity threats. It offers machine learning (ML) algorithms as a solution to security and data quality management. With the help of this paper, we are highlights the challenges of integrating Cloud Computing (CC) and machine learning (ML), including data exchange latency, scalability optimization, resource management, data security, model deployment, and monitoring. A resource plan is provided to train organizations in the use of Cloud Computing (CC) and machine learning (ML). The summary ends by highlighting the evolution of Cloud Computing (CC) and machine learning (ML) integration to shape the future of computing and analytics and make organizations more competitive in the digital age.
THE EFFECTS OF MIND MAPPING ON 5TH GRADERS’ VOCABULARY LEARNING IN TRAN QUOC TOAN PRIMARY SCHOOL IN DISTRICT 5

This article investigated the impacts of mind mapping on the vocabulary development of fifth-graders at Tran Quoc Toan Primary School. The present study utilized a quasi-experimental design: pretest, posttest control group design. Participants were 68 students divided into two groups: the experimental group (Class 5/4) and the control group (Class 5/2). The experimental group was taught mind mapping techniques while the control group received conventional methods. Before the intervention, a pretest was administered to both groups to ensure that their vocabulary level is similar. Subsequently, an 18-week treatment was implemented. A posttest was conducted after the intervention to examine students’ vocabulary. The results of the posttest analysis indicate students in the experimental group improved their vocabulary performance was significantly higher than the ones in the control group. In addition, the data obtained from the questionnaire and interviewsof the experimental group revealed that the participants expressed a positive attitude toward using mind mapping techniques.
Properties of Bipolar Fuzzy Automata

This work focuses on establishing the relationship between bipolar fuzzy automata, reverse bipolar fuzzy automata, and double bipolar fuzzy automata. It also introduces the concepts of bipolar fuzzy subsystems, reverse bipolar fuzzy subsystems, and double bipolar fuzzy subsystems, and explores various properties associated with these subsystems. Furthermore, the paper aims to introduce the categorical aspects of bipolar fuzzy automata and reverse bipolar fuzzy automata, along with their functorial relationship.
Investigating the Traits of Soft Semigraph-Associated Degrees

Soft set theory, introduced by D. Molodtsov, is a mathematical approach that tackles uncertain data. Semigraph is a generalization of the concept of a graph. By incorporating soft sets into semigraphs, a new concept called soft semigraphs has emerged. The study of soft semigraphs has gained significance in graph theory due to their valuable applications in parametrization. This paper explores the properties of various vertex degrees in soft semigraphs, including degrees, end degrees, edge degrees, adjacent degrees, and consecutive adjacent degrees.
FCAODNet: a fast freight train image detection model based on embedded FCA

The fault detection of freight train image has some problems, such as low detection accuracy and slow detection speed. Aiming at the problem of slow detection speed in the process of train image fault detection, a lightweight object detection model fast channel attention network (FCAODNet) is proposed in this study. FCAODNet consists of four modules, including feature extraction network (FEN), lightweight multi-scale feature fusion (LMFF), prediction across scales (PAS), and decoding modules. FEN extracts image features, LMFF fuses features, PAS predicts the location of the target object, and the decoding module obtains the final prediction result. FCAODNet's FEN adopts CSPDarknet53tiny. The designed LMFF is embedded with two FCA modules to improve the detection accuracy. Experiments on train datasets and public datasets show that FCAODNet outperforms other state-of-the-art models in detection speed and has good detection accuracy and robustness.
SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking

Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Sparse Efficient Neural Depth and Deformation (SENDD), extends prior 2D tracking work to estimate flow in 3D space. SENDD introduces novel contributions of learned detection, and sparse per-point depth and 3D flow estimation, all with less than half a million parameters. SENDD does this by using graph neural networks of sparse keypoint matches to estimate both depth and 3D flow anywhere. We quantify and benchmark SENDD on a comprehensively labelled tissue dataset, and compare it to an equivalent 2D flow model. SENDD performs comparably while enabling applications that 2D flow cannot. SENDD can track points and estimate depth at 10fps on an NVIDIA RTX 4000 for 1280 tracked (query) points and its cost scales linearly with an increasing/decreasing number of points. SENDD enables multiple downstream applications that require estimation of 3D motion in stereo endoscopy.
You Don’t Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images

Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthetic images that produce realistic images, i.e., images with good fidelity scores, such as low Fréchet Inception Distance (FID) and high Peak Signal-To-Noise Ratio (PSNR). However, the quality of synthetic images is not limited to fidelity, and a wide spectrum of metrics should be evaluated to comprehensively measure the quality of synthetic images. In addition, quality metrics are not truthful predictors of the utility of synthetic images, and the relations between these evaluation metrics are not yet clear. In this work, we have established a comprehensive set of evaluators for synthetic images, including fidelity, variety, privacy, and utility. By analyzing more than 100k chest X-ray images and their synthetic copies, we have demonstrated that there is an inevitable trade-off between synthetic image fidelity, variety, and privacy. In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety. For intra- and cross-task data augmentation, mode-collapsed images and low-fidelity images can still demonstrate high utility. Finally, our experiments have also showed that it is possible to produce images with both high utility and privacy, which can provide a strong rationale for the use of deep generative models in privacy-preserving applications. Our study can shore up comprehensive guidance for the evaluation of synthetic images and elicit further developments for utility-aware deep generative models in medical image synthesis.
Underground-pipeline mapping system based on inertial measurement unit: Research and application

Underground pipelines are increasingly used for power and communication cables and oil and gas transportation. Mapping the pipeline trajectory is an essential and challenging task for construction and maintenance. This study introduces an underground-pipeline mapping system that involves establishing a trajectory calculation model, developing a robot, and compiling a trajectory calculation program. Innovative complementary and geometric modifications are proposed to obtain satisfactory results. To improve the accuracy of mileage recording, the mileage-recording function is separated from the supporting wheel-set. The maximum errors of the measured trajectories in the vertical and horizontal directions are 0.1264% and 0.2124%, respectively. The system is practical and reliable; data can be uploaded quickly, and the software interface is easy to operate. Hence, the system meets the requirements for engineering applications and can be effectively used for the construction and maintenance of underground pipelines.
Unsupervised Multitask Learning for Oil and Gas Language Models with Limited Resources

Abstract In this paper we explore the development of an oil and gas language model (LM) using an unsupervised multitask learning approach. A large language model (LLM)enables computers to understand and generate human language. The study addresses data scarcity and domain-specific language challenges, showcasing the model's performance on specific oil and gas tasks and qualitative testing. To do that, we collected a highly diversified dataset of 33,000 documents in energy and oil and gas domains to train and benchmark our model.Our findings demonstrate that even a small model, properly finetuned on domain-specific data, outperforms larger models trained on generic corpora, highlighting the benefits of finetuning LMs in technical domains. The paper contributes to advancing natural language processing (NLP) in the oil and gas industry, emphasizing the importance of addressing domain-specific nuances and limitations for improved NLP model performance and reliability.
EVALUASI PENGGUNAAN MODUL INTERAKTIF BATUK DAN SESAK SEBAGAI MEDIA PEMBELAJARAN MANDIRI STASE KOMPREHENSIF

The Cough Shortness interactive module is a medium that was developed to facilitate students' independent learning at the comprehensive stage, in order to increase the graduation rate for UKMPPD participants at the Faculty of Medicine, University of Riau. This study aims to determine the success of an interactive module that was developed by measuring the increase in user knowledge of the module and the level of user acceptance (usability). This research is a quantitative study using an experimental one group pretest post-test design to assess increased knowledge and quantitative descriptive to see user acceptance of the cough and tightness interactive module using 2 questionnaires, namely the System Usability Scale (SUS), User Experience Questionnaire (UEQ) . This research was conducted at the Faculty of Medicine, University of Riau in September – November 2022. The results of this study showed an increase in mana knowledge with a different test between the average pre-test score of 57.91 and the average post-test of 82.83, paired sample T-test shows the difference with sig. (2–tailed) of 0.000 (p &lt;0.05). The SUS score of 71 indicates that the interactive cough shortness module is developed in the acceptability ranges obtained are "acceptable" which means acceptable. On the grade scale, the category "C" is obtained and on the adjective ratings, the category "good" is obtained. The overall UEQ score has an average value of 1.52 with a "good" interpretation. Pragmatic quality has an average value of 1.80 with an "excellent" interpretation, for hedonic quality has an average value of 1.22 with a "good" interpretation. With the results of this study it can be concluded that the interactive cough and shortness of breath module succeeded in facilitating users to learn independently, by showing good acceptance from users of the interactive modules that were developed and able to increase user knowledge
A Dynamic Access Control Scheme with Conditional Anonymity in Socio-Meteorological Observation

&lt;p&gt;Socio-meteorological observation is an essential part of meteorological information construction, where unofficial organizations and individuals (volunteers) are employed to collect meteorological data. Thanks to the participation of social forces, the density and richness of meteorological data are improved significantly, and hence more economical and social benefits are brought. However, problems such as privacy leakage and data islands hamper the sustainable development of socio-meteorological observation. To solve the problems, we propose a dynamic access control scheme with conditional anonymity in socio-meteorological observation. In the proposed scheme, conditional anonymity of volunteers is supported. On the one hand, the real identity of each valid volunteer is private; On the other hand, the real identity of the malicious volunteers will be revealed if they attempt to inject erroneous meteorological data into the system. In addition, a lazy update mechanism is designed, where the fluidity of the volunteers and attribute revocation of the data users are fully considered. Finally, we compare the proposed scheme with similar schemes theoretically and experimentally.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Period-Time Parametric Identification Method for Solving Location and Navigation Tasks

With regard to location and navigation tasks for single-position passive observer, a bearing-free method for identifying parameters of a polynomial model of object motion has been developed taking into account evolution of the discrepancy between the periodic radiated and received quasi-periodic signal. The passage of a signal in an arbitrary physical environment is considered, at the same time, knowledge of the period of the emitted signal and assessing the current Doppler frequency are not required. The method is based on counting the number of periods of the received signal in a given surveillance intertissue. The issues related to the analysis of the resulting discrepancy by the observability of the method and its accuracy characteristics are considered. Useful practical recommendations and an illustrative context are given.
Emoji use validates the potential for meaning standardization among ideographic symbols

Technological innovations for online communication reduce the impact of signal transience on meaning standardization while boosting access to reliable patterning across multiple linguistic and nonlinguistic contexts - both asynchronous and synchronous. We classify emojis as ideographic symbols, examine their interdependence with surrounding words when reading/writing, and argue that emoji use validates the potential for meaning standardization in ideographs.
Mind the gap: Why is there no general purpose ideographic system?

Morin has identified an intriguing puzzle about human communication systems, and one element of the solution: Inscriptional sign systems pose more coordination problems, making sender/receiver coadaptation more difficult. But I reject his view of written language, concluding that inscriptional sign systems can be generalist. The upshot is a cost-based proposal about why generalist ideographic systems are essentially unknown.
Complete and partial time-delay signature suppression in a laser array

We model dynamics of a quantum dot based micropillar laser array subject to the time-delayed optical feedback. The global coupling provided by the feedback generates a rich set of various instabilities including chaotic regimes with strong time-delay signature in the autocorrelation function. We demonstrate that the dispersion of the array coupling phases leads to effective suppression of the time-delay signature due to the dispersion of the system’s internal timescales. We find that the transition to the complete suppression of the time-delay signature appears via a chimera state where highly correlated and non-correlated laser outputs coexist. The degree of correlation in the chimera state depends on the coupling phase dispersion.
Handwriting for Efficient Text Entry in Industrial VR Applications: Influence of Board Orientation and Sensory Feedback on Performance

Text entry in Virtual Reality (VR) is becoming an increasingly important task as the availability of hardware increases and the range of VR applications widens. This is especially true for VR industrial applications where users need to input data frequently. Large-scale industrial adoption of VR is still hampered by the productivity gap between entering data via a physical keyboard and VR data entry methods. Data entry needs to be efficient, easy-to-use and to learn and not frustrating. In this paper, we present a new data entry method based on handwriting recognition (HWR). Users can input text by simply writing on a virtual surface. We conduct a user study to determine the best writing conditions when it comes to surface orientation and sensory feedback. This feedback consists of visual, haptic, and auditory cues. We find that using a slanted board with sensory feedback is best to maximize writing speeds and minimize physical demand. We also evaluate the performance of our method in terms of text entry speed, error rate, usability and workload. The results show that handwriting in VR has high entry speed, usability with little training compared to other controller-based virtual text entry techniques. The system could be further improved by reducing high error rates through the use of more efficient handwriting recognition tools. In fact, the total error rate is 9.28% in the best condition. After 40 phrases of training, participants reach an average of 14.5 WPM, while a group with high VR familiarity reach 16.16 WPM after the same training. The highest observed textual data entry speed is 21.11 WPM.
Prediction of Age, Gender, and Ethnicity Using CNN and Facial Images in Real-Time

Age, gender and ethnicity prediction is very important nowadays as it has many real-life applications in various types of fields but it is also a very challenging task because of reasons such as the complexity of facial appearance and lighting. Convolutional Neural Networks (CNN) is a deep learning artificial neural network which has shown great success in solving this problem. CNNs use convolutional layers to extract characteristics from the input data and apply a set of filters to the data, which helps to identify different types of patterns in the data. The outputs of CNN are then passed through fully connected layers which perform classification based on the identified patterns. OpenCV is a machine learning library which is available as an open source that helps in performing real-time operations. This library detects objects through captured or real-time images or videos. We propose a real-time age, gender and ethnicity prediction system with the help of OpenCV and then pass it through CNN. In this paper, we trained three CNN models each for age, gender and ethnicity on the preprocessed dataset. We used a large-scale dataset containing facial images of different individuals with labels of all three attributes. We evaluated the performance of trained models on a test set of images and calculated benchmarks such as accuracy. We used OpenCV to capture real-time images from a webcam, preprocessed that image, and then used the trained CNN models to predict the age, gender and ethnicity of the captured image in real-time. The accuracy rates obtained from age, gender and ethnicity models are 82.5, 89.45 and 86.69 percent respectively.
Chinese named entity recognition based on MacBERT and Joint Learning

Extracting medical entities from Chinese medical texts is of great significance to the establishment and application of medical information systems. Chinese text lacks spacers to segment words, which makes it difficult to recognize entities. Therefore, this paper proposes a named entity recognition method combining MacBERT and Joint Learning. The MLM as correction BERT (Mac-BERT) pretrained language model, which alleviates the differences between pre-training and fine-tuning stages, obtains dynamic word vector feature expression, and then enters into a framework that integrates the named entity recognition task and the word segmentation task for joint learning, so as to improve the feature capture ability of the model. Experiments show that the model can effectively obtain the important entities in the instruction of traditional Chinese medicine. On the Chinese medicine instruction dataset, fusing MacBERT with Joint Learning model achieved the best result with 68.43% F1, which increased by 1.6% compared with the two-layer BiLSTM non-joint model, and increased by 1.07% compared with the combined BERT with Joint Learning model. Experimental results show that the recognition performance is better than that of the non-joint model and other pretrained models.
Multimodality Imaging Data-Driven Prediction Architecture for Breast Cancer

Breast cancer is a significant public health concern and one of the primary causes of cancer-related fatalities among women worldwide. Early identification and diagnosis of breast cancer are critical for successful treatment and enhanced patient outcomes. Deep learning algorithms have shown significant potential in identifying and diagnosing breast cancer in recent years. This study presents a method for classifying breast cancer into three categories: Normal, Benign, and Malignant, based on a combination of five transfer learning architectures. This paper proposes a stacked approach for progressively learning and combining multimodal features for the classification using Computer Tomography (CT) and ultrasound images (UT). The entire image is first converted into high-level features for each modality by building a deep 3D-CNN. To combine the most important attributes for image classification, the suggested stacking of VGG-19, ResNet-50, Inception-V3, Xception, DenseNet-121, and MobileNet-V2 models is proposed. The proposed stacking approach achieved an accuracy of 94.01%, 92.33% and 91.21% for Malignant, Benign and Normal. Results showed that compared to preexisting individual learners, the proposed ensemble model achieves higher accuracy both globally and inside individual classes.
Chinese Named Entity Recognition Based on Grid Tagging and Semantic Segmentation

Chinese Named Entity Recognition (CNER) is one of the fundamental tasks in Chinese Natural Language Processing and has been extensively researched. However, previous studies have primarily focused on specific types of CNER subtasks, which hindered the overall progress and practical implementation of CNER. Recently, grid-tagging-based methods have emerged as having significant advantages in information extraction tasks due to the flexible annotation framework and model architecture they provide. Building on this, we unify the CNER task as a character pair relation classification problem and solve it by predicting the character pair relation matrix, which is similar to the semantic segmentation task in computer vision. To do this, we utilize the U-shaped segmentation module to capture low-level and high-level features in the feature map of image style, enabling us to extract more semantic information. We also effectively model the boundary information of the entity, which in turn enhances the overall accuracy of entity recognition. To test the effectiveness of our method, we conduct experiments on two NER datasets containing flat and nested entities in the Chinese medical domain (CMeEEV2, MMC). Our results demonstrate that our approach improves the F1 score by 0.73% and 1.07% respectively compared to current state-of-the-art models, further validating its effectiveness.