Reviewerly/exHarmony huggingface dataset:
ELoran Propagation Delay Prediction Model Based on BP Neural Network for Complex Meteorological Environment

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model.&#x0D; eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;
Contextual Mathematics Teaching Materials: Flipbook, Liveworksheet, Youtube

This study aims to produce interactive digital teaching materials. The teaching materials are in the form of flipbooks that are integrated with live worksheet and youtube applications and are presented online to students via links. This research is motivated by the lack of teaching materials that are suitable for learning in the pandemic period and the lack of learning activities that involve students to actively participate in finding mathematical concepts. Many teachers do not have teaching materials because they follow the learning system from home. As a result, students also do not have a guide to study the material independently so that teachers and students alike have difficulty in carrying out the learning process. Therefore, to help students understand the material with a series of daily activities that can be done at home, students are given teaching materials that can be accessed independently. This research is a design research with the type of development study. The subjects of this study were seventh grade students of SMPT IT Ar Raihan Bandarlampung. The results showed that the teaching materials developed had met the valid criteria based on the validator's assessment and showed a potential effect as seen from the percentage of student learning completeness which was more than 70%.
Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition

In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed for handling long-tailed multi-label recognition or label noise respectively, learning with noisy labels in long-tailed multi-label visual data has not been well-studied because of the complexity of long-tailed distribution entangled with multi-label correlation. To tackle such a critical yet thorny problem, this paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. In detail, we propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise by stitching up multiple noisy training samples. Equipped with Stitch-Up, a Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions, yielding cleaner labels for more robust representation learning with noisy long-tailed data. To validate our method, we build two challenging benchmarks, named VOC-MLT-Noise and COCO-MLT-Noise, respectively. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method. Compared to a variety of baselines, our method achieves superior results.
Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%.
Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance.
Lung Disease Classification using Dense Alex Net Framework with Contrast Normalisation and Five-Fold Geometric Transformation

lung disease is one of the leading causes of death worldwide. Most cases of lung diseases are found when the disease is in an advanced stage. Therefore, the development of systems and methods that begin to diagnose quickly and prematurely plays a vital role in today's world. Currently, in detecting differences in lung cancer, an accurate diagnosis of cancer types is needed. However, improving the accuracy and reducing training time of the diagnosis remains a challenge. In this study, we have developed an automated classification scheme for lung cancer presented in histopathological images using a dense Alex Net framework. The proposed methodology carries out several phases includes pre-processing, contrast normalization, data augmentation and classification. Initially, the pre-processing step is accompanied to diminish the noisy contents present in the image. Contrast normalization has been explored to maintain the same illumination factor among histopathological lung images next to pre-processing. Afterwards, data augmentation phase has been carried out to enhance the dataset further to avoid over-fitting problems. Finally, the Dense Alex Net is utilized for classification that comprises five convolutional layers, one multi-scale convolution layer, and three fully connected layers. In evaluation experiments, the proposed approach was trained using our original database to provide rich and meaningful features. The accuracy attained by the proposed methodology is93%, which is maximum compared with the existing algorithms
Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning

Text classification is the method of allocating a particular piece of text to one or more of a number of predetermined categories or labels. This is done by training a machine learning model on a labeled dataset, where the texts and their corresponding labels are provided. The model then learns to predict the labels of new, unseen texts. Feature selection is a significant step in text classification as it helps to identify the most relevant features or words in the text that are useful for predicting the label. This can include things like specific keywords or phrases, or even the frequency or placement of certain words in the text. The performance of the model can be improved by focusing on the features that are most important to the information that is most likely to be useful for classification. Additionally, feature selection can also help to reduce the dimensionality of the dataset, making the model more efficient and easier to interpret. A method for extracting aspect terms from product reviews is presented in the research paper. This method makes use of the Gini index, information gain, and feature selection in conjunction with the Machine learning classifiers. In the proposed method, which is referred to as wRMR, the Gini index and information gain are utilized for feature selection. Following that, machine learning classifiers are utilized in order to extract aspect terms from product reviews. A set of customer testimonials is used to assess how well the projected method works, and the findings indicate that in terms of the extraction of aspect terms, the method that has been proposed is superior to the method that has been traditionally used. In addition, the recommended approach is contrasted with methods that are currently thought of as being state-of-the-art, and the comparison reveals that the proposed method achieves superior performance compared to the other methods. In general, the method that was presented provides a promising solution for the extraction of aspect terms, and it can also be utilized for other natural language processing tasks.
Smart Edge Detection Technique in X ray Images for Improving PSNR using Canny Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using canny edge detection algorithm and compared with laplacian algorithm. Materials and Methods: Using the design of edge detection technique and to improve PSNR, canny edge detection algorithm is used along with gaussian filter and it is compared with laplacian algorithm. Canny edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95% and threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the canny edge detection algorithm has a higher mean PSNR value of 28.98db than the laplacian algorithm 27.08 db. It is observed that the canny edge detection algorithm performed better than the laplacian algorithm (p&gt;0.05) by performing an independent sample t-test. Conclusion: Canny edge detection has insignificantly greater PSNR when compared to laplacian algorithm.
Social Movements as Producers and Receivers of Knowledge: The Case of “Don’t Let Belgrade D(r)own”

In this paper I am seeking to resolve a theoretical dilemma. The main argument is that the relationship between social movements and knowledge is a “two-way street”, in which knowledge both gets diffused from critical communities and gets produced by the social movements. In order to resolve this tension one should, I argue, make an analytical separation between different levels of abstraction of knowledge. The macro (conceptual) level is, according to my approach, to be understood as taken over from critical communities. The micro level as contextual intervention into macro conceptualization of reality, whereas the meso level is to be viewed as knowledge produced by social movements when conceptual and presumed / contextual knowledge gets combined. It is at the meso level, in other words, that we can see what applied theory looks like after being exposed to a given context. Knowledge occurring at this level represents the outcome of work done by what Jameson and Eyerman call “movement intellectuals” or what Antoni Gramsci calls “organic intellectuals”. This is how one may theoretically resolve the above-indicated tension and prevent excluding one approach at the expense of the other.&#x0D; &#x0D; Article received: April 21, 2021; Article accepted: June 23, 2021; Published online: October 15, 2021; Original scholarly article&#x0D; How to cite this article: Balunović, Filip. "Social Movements as Producers and Receivers of Knowledge: The Case of 'Don’t Let Belgrade D(r)own'." AM Journal of Art and Media Studies 26 (October 2021): 103-114. doi: 10.25038/am.v0i26.474
Automatic Human Face Detection and Recognition Based On Facial Features Using Deep Learning Approach

In recent years, there has been an increased emphasis placed on the identification of face traits in studies. The human face is the most significant characteristic that may be used in the process of identifying a person. Even the most genetically identical twins may be distinguished from one another by a few key facial characteristics. Therefore, to discern one from the other, a human face identification and detection system that is based on facial traits is necessary. This study suggests a technique for automated human face identification and recognition based on facial characteristics that are achieved via the use of deep learning. It would seem that deep learning, with its high rate of accuracy, would be an appropriate method to use while carrying out face recognition. Face detection and identification may be accomplished using a process known as deep learning. According to the results of the study, it is possible to conclude that the approach that was suggested is superior to other ways in terms of accuracy, precision, recall, and f1-score.
Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data

Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works.
YASF: A Vendor-Agnostic Framework for Serverless Computing

The serverless execution model allows application developers to deploy their software using tiny functions with zero administration, no handling of resource provisioning, monitoring and scaling.Due to such advantages, the serverless model emerged as a new promising paradigm, where pay as you go offerings can be found by all public cloud providers.However, these offerings encourage vendor lock-in.This paper aims to address the vendor lock-in issue using a novel framework that combines the strength of an agnostic infrastructure configuration based on the Constructs Programming Model, and the creation of abstraction layers that supports function logic by handling provider-specific integration.The proposed framework abstracts away the specificities and complications of the various underlying serverless platforms and the application developers are only required to provide their specific functions.The evaluation consists of the deployment of a benchmark application across different cloud providers to demonstrate the ease and flexibility of the framework.
Formation of digital competence of future doctors in the study of the educational component «Medical informatics»

The article considers and analyzes the problem of students’ acquisition of digital competencies in the study of the educational component «Medical Informatics» in the institution of higher education. The purpose of the article is to analyze the theoretical essence and content of the concept of digital competence and the competence of future doctors in the field of digital health care. Consider digital medical education and the acquisition of digital competence by students of higher medical educational institutions of Ukraine, using as an example the acquisition of digital competence in the study of the educational component «Medical Informatics». To achieve this goal, the analysis and synthesis of search results in information systems to determine the concepts of research, methods of expert evaluation, testing, questionnaires, observation, modeling, forecasting. The process of implementation of information processes in the field of health care, which involves the use of digital technologies, and determines the priority need for future health professionals to acquire relevant practical skills vital to medical records, medical and social data, patient morbidity analysis, etc., was considered. The program of the educational component «Medical Informatics» is based on practical experience of applied medical and biological specialties and works in the context of systems and organizations of clinical and public health for the development of experiments, interventions and approaches. This depth of methods of medical informatics determines the basis of the discipline and ensures its consistency with the definition of a common set of basic digital competencies. Acquisition of basic digital competencies in the study of the educational component «Medical Informatics», which is both medical and scientific and technological areas, makes teaching medical students the basics of problems and tasks aimed at using authentic digital data and systems aimed at building future competencies of doctors in the field of medical informatics are important and necessary for a student with one basic type of education that meets the needs and career goals of the future doctor. In accordance with the requirements of the Standard, the educational component «Medical Informatics» provides students with key digital competecies, which assume that future physicians will be creative, ask the right questions, demonstrate scientific skepticism, question past approaches, and strictly apply planning methods of experiments, data analysis and assessment theory. It is established that the integrative learning outcomes, the formation of which is facilitated by the educational component «Medical Informatics» is the formation of the future doctor’s competence in the field of digital technologies.
Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking

Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions. It's important to many reasoning tasks to support human everyday activities. However, it's challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. To enforce the logical coherence among the predicted entities, attributes, and states, we design a new constraint decoding strategy and employ a coherence reward to improve the decoding process. Experimental results show that our proposed KIEST framework significantly outperforms the strong baselines on the public benchmark dataset OpenPI.
Moderately Distributional Exploration for Domain Generalization

Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches, yet its performance gain depends on the distribution discrepancy between the generated and target domains. Distributionally robust optimization is promising to tackle distribution discrepancy by exploring domains in an uncertainty set. However, the uncertainty set may be overwhelmingly large, leading to low-confidence prediction in DG. It is because a large uncertainty set could introduce domains containing semantically different factors from training domains. To address this issue, we propose to perform a $\textbf{mo}$derately $\textbf{d}$istributional $\textbf{e}$xploration (MODE) for domain generalization. Specifically, MODE performs distribution exploration in an uncertainty $\textit{subset}$ that shares the same semantic factors with the training domains. We show that MODE can endow models with provable generalization performance on unknown target domains. The experimental results show that MODE achieves competitive performance compared to state-of-the-art baselines.