Reviewerly/exHarmony huggingface dataset:
Understanding Variation in Subpopulation Susceptibility to Poisoning Attacks

Machine learning is susceptible to poisoning attacks, in which an attacker controls a small fraction of the training data and chooses that data with the goal of inducing some behavior unintended by the model developer in the trained model. We consider a realistic setting in which the adversary with the ability to insert a limited number of data points attempts to control the model's behavior on a specific subpopulation. Inspired by previous observations on disparate effectiveness of random label-flipping attacks on different subpopulations, we investigate the properties that can impact the effectiveness of state-of-the-art poisoning attacks against different subpopulations. For a family of 2-dimensional synthetic datasets, we empirically find that dataset separability plays a dominant role in subpopulation vulnerability for less separable datasets. However, well-separated datasets exhibit more dependence on individual subpopulation properties. We further discover that a crucial subpopulation property is captured by the difference in loss on the clean dataset between the clean model and a target model that misclassifies the subpopulation, and a subpopulation is much easier to attack if the loss difference is small. This property also generalizes to high-dimensional benchmark datasets. For the Adult benchmark dataset, we show that we can find semantically-meaningful subpopulation properties that are related to the susceptibilities of a selected group of subpopulations. The results in this paper are accompanied by a fully interactive web-based visualization of subpopulation poisoning attacks found at https://uvasrg.github.io/visualizing-poisoning
Revisiting Classical Controllers Design and Tuning with Genetic Programming

This paper introduces the implementation of a genetic programming (GP)-based procedure to the automatic design and tuning of process controllers. The proposed approach makes a significant contribution to the field of artificial intelligence (AI) in control engineering. Unlike other controller design methods, the GP-based program handles the entire design in the time domain, including differential operations like derivatives and integrals, without the need for intermediate inverse Laplace transformation. This approach not only simplifies the design process but also ensures that all generated controllers are implementable in physical systems. Furthermore, GP’s functions set includes various mathematical operations beyond basic arithmetic operators, such as trigonometric, exponential, and logarithmic operators. The performance and validity of the resulting controllers generated by the proposed GP-based approach are evaluated by verifying whether the generator can replicate the structure and performance of those produced by traditional controller design methods and, in some cases, achieve even better results. As a result, the GP-based approach presents a promising solution for automating the controller design process and addressing control problems in various engineering applications.
Feature Extraction Based Machine Learning Approach for Bone Cancer Detection

Osteosarcoma is a type of cancer that develops in the bones. Though it can happen in any bone, it commonly happens in long bones like the legs and arms. As a result, early detection and categorization of bone cancers have become critical for treating patients. A wavelet-based segmentation algorithm was utilized in this work to detect bone cancers. The segmented bone cancer components were then processed further for categorization. The enhanced convolutional neural network (ECNN) classification was employed in this investigation to differentiate between benign and malignant bone cancers. Collect multiple photos and use wavelet transform features to extract a trained classifier model. Sensitivity (97%), Specificity (97%), Precision (98%), Accuracy (97.5%), and F1Score (97.5) are the performance metrics for the ECNN deep learning (DL) algorithm. According to the results, ECNN deep learning beats deep learning methods, including SVM, ANN, and RNN. As a result, the ECNN deep learning technology can be used to diagnose bone cancer more accurately. Based on histology pictures, our enhanced model performs at the cutting edge of detecting osteosarcoma cancer.
Optimization of Rate Distortion Control Based on Fundamentals of Convolutional Neural Networks.

Abstract Roots of Convolutional Neural Networks or Fuzzy weights based Neural Networks and its fundamentals are mostly concerned with coordination and its respective logic's and moreover with consideration of Neuroscience terms and conditions in involvement of research practices towards Artificial Neural Networks, we need to abide that human visual characteristics(HVS) are considered with first priority towards developmental cases in research activities. Moreover cognitive abilities and emotion management are considered as second priority compare to Human visual System(HVS).
INOVASI PEMBUATAN LEMBAR KERJA PESERTA DIDIK ELEKTRONIK (E-LKPD) MENGGUNAKAN LIVEWORKSHEET

AbstractLearn from covid-19 pandemic, It is necessary to renew, adjust, and improve teaching and learning activities in schools. One of them is by utilizing and developing learning media based on technology that can show various microscopic and abstract concepts that are difficult to display or visualize directly, namely interactive worksheets based on live worksheets. This research was carried out in the 2021/2022 academic year at Senior High School Muhammadiyah Pekanbaru with the main material being is acid-base. The method of this research used was ADDIE (Analysis, Design, Development, Implementation, Evaluation) modeling with limitation up to the implementation stage only. The data taken in this research were in the form of literature studies, interviews, and questionnaires. The data collection instrument of this research was in the form of validity questionnaires and responses questionnaires data. The data obtained were then analyzed using qualitative descriptive analysis and quantitative descriptive analysis techniques. Interactive students worksheets based on the live worksheets on acid-base materials had been tested to be very valid with a percentage of 88.93% from media experts and 82,44% from material experts and got very good response from students with percentage of 92,66%. Based on these results it can be concluded that interactive student worksheets based on live worksheets on acid - base materials are declared to be valid and reliable so that this media is suitable to be applied in the learning process.Keywords : Acid Base, Liveworksheet, E-LKPD
Infrastructure Cloud Migration

The core of traditional IT architecture solutions is to manage the resources and architecture within the enterprise, and the final result is to build a closed technical system, even after the optimization of internal resources.
A Perspective way of designing Intelligent systems with Face Detection and Recognition using Artificial Intelligence for Authentication

This project proposes an AI-based approach for face detection and recognition using YOLOv4 and CNN. It aims to be used in various applications like security systems and automatic attendance marking in educational institutions. Traditional machine learning methods like FaceNet and MTCNN have not achieved satisfactory accuracy. Hence, deep learning techniques are employed. YOLO, a deep learning library, is utilized for implementation. The key steps include creating an effective dataset, labeling images with faces using LabelImg software, and training the dataset using Google Colab GPU. The trained dataset is used to mark bounding boxes around faces in images or real-time video. The YOLOv4 technique is implemented using the darknet framework. Around 20 images are used for testing, resulting in an approximate accuracy of 85% due to the high accuracy of YOLOv4.
Privacy-Preserving Personal Identifiable Information (PII) Label Detection Using Machine Learning

In today's data-driven world, the protection of PII is of paramount importance to safeguard personal privacy. PII tags serve as crucial markers for identifying and processing sensitive information within databases. However, the authentication and registration of PII tags can be time-consuming and error prone. To address this challenge, we propose a method for privacy controlled PII tag detection that harnesses the power of machine learning (ML) combined with regular expressions. Proposed approach leverages various techniques, including feature engineering, adaptive learning, and machine learning, to extract meaningful patterns and relationships from data. By training the model on large datasets that encompass diverse PII elements such as names, addresses, phone numbers, email addresses, and social security numbers, enable it to learn and classify PII identifiers across different documents effectively. One of the key advantages of the proposed method is its ability to automate the detection of PII identifiers, thereby reducing the reliance on manual interpretation and minimizing the potential for human error. By integrating machine learning algorithms, empower organizations to efficiently identify and process sensitive information present in their databases, bolstering privacy protection measures. Moreover, this approach facilitates the development of scalable and accurate solutions for privacy based PII tag search. This advancement paves the way for enhanced data privacy across the enterprise, ensuring compliance with regulations and standards pertaining to the protection of personal information. By combining the strengths of ML and regular expressions, the proposed method enables organizations to detect and handle PII identifiers more effectively. This not only streamlines data management processes but also strengthens privacy safeguards, and more secure and privacy-aware data ecosystem.
Exploring Pretrained Models and Transfer Learning Techniques for Image Retrieval

In recent years, the field of content-based image retrieval (CBIR) has witnessed significant advancements and garnered immense attention. With the increase of digital imagery across various domains, the need for effective image search and retrieval techniques has become increasingly important. Pre-trained deep neural networks have arisen as effective tools for image analysis and feature extraction due to their ability to learn complex visual representations from vast amounts of data. By harnessing the power of pre-trained deep neural networks, such as AlexNet, ResNet, VGG16, and GoogLeNet, researchers seek to enhance CBIR systems. This paper explores the significance of utilizing pre-trained networks for content-based image retrieval (CBIR). It includes a literature review discussing similar CNN architectures & their applications as well as identifying research gaps. The paper conducts a comparative study of the four pre-trained networks: AlexNet, ResNet, VGG16, and GoogLeNet, evaluating their performance and suitability for CBIR. It also demonstrates transfer learning techniques to fine tune AlexNet and VGG16 models using MNIST and Fashion MNIST dataset. Evaluation using LRAP as a metric revealed good performance for both models, with AlexNet consistently outperforming VGG16, albeit with longer execution times.
A Comparison of Machine Learning Algorithms and Transformer-based Methods for Multiclass Sentiment Analysis on Twitter

The growing number of tweets being produced on Twitter and other social media platforms has made sentiment analysis a crucial task in comprehending individuals' viewpoints and emotions. We focus on the challenge of Twitter emotion classification in this paper and perform sentiment analysis on six different emotions: joy, anger, sadness, fear, surprise, and disgust. The performance of four distinct models is studied as this topic is addressed as a multiclass classification challenge: Naive Bayes, logistic regression, SVM, and DistilBERT, a transformer-based model. Experiments are run on a dataset of over 16,000 tweets that have been annotated with six emotions: joy, anger, sadness, fear, surprise, and disgust. DistilBERT outperforms the other models with an overall accuracy of 93.5%, according to our results. The accuracy of Naive Bayes is 78%, the accuracy of logistic regression is 89%, and the accuracy of SVM is 88%. Our research demonstrates the utility of transformer-based models, specifically DistilBERT, in sentiment analysis tasks using Twitter data. The findings also highlight the significance of appropriate pre-processing techniques in the performance of traditional machine learning models. Our findings have practical implications for businesses looking to gain insights into customer opinions and preferences via social media platforms such as Twitter.
Nuclei Classification in Histopathology Images Using Fuzzy Ensemble of Convolutional Neural Networks

The development of computational pathology and healthcare studies depends on cell nuclei classification. It has several lifesaving uses, particularly in cancer diagnosis. It is helpful in various applications, including disease diagnosis and medicinal therapy. Due to the number of problems, human examination of these image slides is unpleasant and time-consuming. Morphological traits add to the intricacy. We suggested a fuzzy distance ensemble of Convolutional Neural Networks to achieve state-of-the-art nucleus classification performance. This new model is easier to train and makes more accurate predictions using underlying basic classifiers. To examine how well our model worked, we used the PanNuke dataset. Our model’s scores are 0.811 for accuracy, 0.811 for F1, 0.815 for precision, and 0.809 for recall.
MVT: Using Mixup in Vision Transformers for Enhanced Data Augmentation

Using large deep neural networks for image classification can be very effective, but it has some negative effects such as memorization and vulnerability to "adversarial examples". To tackle such issues we propose to apply mixup [4] in various parts of attention space of vision transformer [1] architecture. Mixup is a data augmentation technique which involves training a neural network on pairs of examples and their associated labels with pre defined weights associated to each input. By doing so, the network is encouraged to behave simply linearly between training instances, thereby acting as a form of regularization. While previous work on mixup has focused on the image domain, we introduce it here in the context of the encoder blocks of the vision transformer and explore its impact on classification accuracy across different datasets.
Exploring the Dynamics and Implications of Offline Peer-to-Peer Payments

Electronic financial systems are becoming more and more common because of the development of the Internet and communication technology. Digital numbers on computers are replacing physical currency as it loses favor. However, some issues need to be resolved with electronic financial systems, which are generally managed by commercial organizations. These drawbacks include internet connectivity, astronomical fees for utilizing the system, and security issues. Many nations throughout the world are contemplating central-bank digital currency (CBDC) as a remedy. Offline peer-to-peer (P2P) payments have emerged as a viable solution for secure and convenient financial transactions that do not require an Internet connection. This research paper examines the need for offline peer-to-peer payments, the system's logical architecture, including its fundamental building elements, its primary use cases, and the difficulties involved in putting it into practice. Several cutting-edge methods and tools that help to overcome the issues associated with offline payments are discussed. These include the use of secure cryptographic protocols, tokenization, and the adoption of near-field communication (NFC) or Bluetooth technologies for device-to-device communication. By examining the key components of offline P2P payments, this study aims to provide insights and recommendations for advancing offline P2P payment systems.
A Brief Survey of Energy-Efficient Task Scheduling Algorithms in the Cloud

Research and development in cloud computing technology have become one of the primary focuses for academics and enterprises because of its immense popularity, higher utility, and ever-increasing demand. Scheduling cloud tasks in an energy-efficient manner is challenging and has become an important area of research too. Energy is becoming scarce, and high energy consumption from data centers forces us to develop energy-efficient algorithms so that cloud resources are not over and under-utilized. The main challenge here is to develop energy-aware algorithms for different environments that perform optimally without compromising the deadline and other important QoS parameters. This paper has systematically reviewed and classified recent energy-efficient scheduling algorithms along with their objectives, type, working concepts, pros, limitations and simulation tools. Our systematic and comprehensive survey will benefit the initial understanding of energy-efficient algorithms for the new researchers who wish to further research in the energy domain.
Approach for Personalized Recommendations to Enhance Customer Service Process in Peruvian Restaurants using OpenAI Contextual Chatbot

In the current digital era, efficiently accessing relevant information is crucial for various applications such as restaurant recommendation systems, website searches, book recommendations, among others. This study presents an approach for personalized recommendations in improving the customer service process in restaurants using OpenAI's Contextual Chatbot. The approach consists of six phases: (1) Data preprocessing, (2) Embedding and storage, (3) Scheduled updating, (4) Document retrieval, (5) Context adaptation and request creation, and (6) Response generation. OpenAI's text embeddings are used to convert application data into vectors and store them in a vector database. These vectors are used to retrieve similar records and generate contextualized responses using the GPT-3.5 model. The chatbot's performance is evaluated in terms of accuracy and user satisfaction. Two scenarios were used in the experimentation: (a) with the proposed solution and (b) without the solution. The results demonstrated an operational efficiency of 86.67% with the proposed solution and the versatility of the proposed methodology, showcasing its potential for application in a wide range of domains, including websites, books, PDFs, and other forms of documentation.
Analysis of the NS-3 module QKDNetSim for the Simulation of QKD Networks

Quantum Key Distribution (QKD) is a promising technology that allows two nodes to privately agree on a key through a quantum channel. Unfortunately, QKD is still in experimental phase and researchers must rely on simulators to replicate the behaviour of a quantum network. One of the most widespread is QKDNetSim, a module for the C++ network simulator NS-3. However, this module is very limited in its behaviour, so it does not faithfully represent a real quantum network. In this work we analyse the structure and components of QKDNetSim, as well as its shortcomings and how they affect the quality of the simulation
Efficient Database Evolution in Digital Library Reengineering

With the advancement of internet applications, extensive information systems were created to effectively manage and provide easy access to documents, which coincided with a global initiative to convert physical documents into digital format, making them accessible through the internet. After two decades, these databases are well-structured and organized, although the software used to manage them is gradually becoming outdated. Additionally, once the initial digitization and creation of metadata are completed, it is sensible to enhance the metadata further to provide more detailed information about the documents. In this article we propose a tool to facilitate the evolution of large documentary databases
Unveiling the Dark Side of Social Media: Developing the First Galician Corpus for Misogyny Detection on Twitter and Mastodon

This work aims to develop the first Galician corpus for the detection of misogyny on Twitter and Mastodon. We collect and analyze linguistic data in Galician on these social media platforms, identifying manifestations of misogyny in digital communication. The process involves data collection, text selection, and normalization, followed by thorough cleaning. We apply machine learning techniques to train accurate models for classifying the presence of misogyny. The resulting corpus facilitates analysis and study by research teams interested in misogyny in Galician. This scientific advancement contributes to the understanding and prevention of misogyny in the Galician-speaking community, promoting equality and respect in digital communication in Galician
Teacher Training as a Strategy of Professional Development: Perceptions and Challenges

This article aimed to explore the beliefs and opinions of in-service secondary-level teachers towards teacher training for professional development. Moreover, it intended to identify major issues that existed in the field of teacher training in Nepal. It is a qualitative research study in which the teachers' stories and experiences have been documented. A purposive sampling strategy was used to select the sample from the target population. Unstructured in-depth interviews were conducted to collect data. The data were further analysed thematically. The study found that many teachers perceived teacher training as a strategy of professional development that equipped them with both the content and the pedagogical knowledge and skills. However, the study has highlighted various issues and challenges such as geographical difficulty, inaccessibility to the resources, centrality in training practices in the federal system, nepotism, favouritism, and political relations while selecting the trainees. The study implies that the training courses, provisions, policies, and programs are to be developed and launched based on the teachers’ needs. Furthermore, teacher training should be decentralised.
UPAYA MENINGKATAN HASIL BELAJAR EKONOMI MELALUI MEDIA KOMIK SAINS PADA MATERI PERTUMBUHAN DAN PERKEMBANGAN EKONOMI KELAS XI IPS SMA NEGERI 2 SIGLI TAHUN PELAJARAN 2022/2023

PTK with the title Efforts to Improve Economic Learning Outcomes through Media Science Comics on Economic Growth and Development Material for Class XI IPS SMA Negeri 2 Sigli Academic Year 2022/2023 , which aims to find out efforts to improve economic learning outcomes through the media of science comics on economic growth and development material Class XI IPS SMA Negeri 2 Sigli Academic Year 2022/2023. The subjects of this study were 18 students in class XI Social Sciences at SMA Negeri 2 Sigli for the academic year 2022/2023. To obtain the author's data using test and non-test techniques. After the data was collected, the authors processed and analyzed the data by comparing the results of observations and tests in cycle I and cycle II. The increase in the average value was 49.4 in the initial conditions to 66.7 in cycle I and to 80 in cycle II. The average value of the first cycle increased 17.3% from the initial conditions, the average value of the second cycle increased 13.3% from the first cycle. While the learning completeness in the first cycle there was an increase of 44.4% from the initial conditions, the second cycle increased 27.8% from cycle I. The overall class average score increased by 30.6%. At the end of the study there were positive changes in students regarding the understanding of economic growth and development. By using science comic media it turns out to be able to improve economics learning outcomes in the material of economic growth and development.Keywords: Learning outcomes, science comic media and economic growth and development
Penton Herrera, L. J. (2022). English and Students with Limited or Interrupted Formal Education: Global perspectives on teacher preparation and classroom practice

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