Reviewerly/exHarmony huggingface dataset:
Investigating the impact of class-dependent label noise in medical image classification

Label noise is inevitable in medical image databases developed for deep learning due to the inter-observer variability caused by the different levels of expertise of the experts annotating the images, and, in some cases, the automated methods that generate labels from medical reports. It is known that incorrect annotations or label noise can degrade the actual performance of supervised deep learning models and can bias the model's evaluation. Existing literature show that noise in one class has minimal impact on the model's performance for another class in natural image classification problems where different target classes have a relatively distinct shape and share minimal visual cues for knowledge transfer among the classes. However, it is not clear how class-dependent label noise affects the model's performance when operating on medical images, for which different output classes can be difficult to distinguish even for experts, and there is a high possibility of knowledge transfer across classes during the training period. We hypothesize that for medical image classification tasks where the different classes share a very similar shape with differences only in texture, the noisy label for one class might affect the performance across other classes, unlike the case when the target classes have different shapes and are visually distinct. In this paper, we study this hypothesis using two publicly available datasets: a 2D organ classification dataset with target organ classes being visually distinct, and a histopathology image classification dataset where the target classes look very similar visually. Our results show that the label noise in one class has a much higher impact on the model's performance on other classes for the histopathology dataset compared to the organ dataset.
Fog-enabled private blockchain-based identity authentication scheme for smart home

As the Internet of Things (IoT) technology continues to advance, it has found widespread application in various fields, including smart health, smart cities, and smart transportation. Among these applications, smart homes are particularly noteworthy due to their intimate connection to our daily lives. However, the use of smart devices in a home environment exposes users to various security threats, such as impersonation attacks and insider privilege attacks, as users must communicate with multiple devices through a public channel. Additionally, traditional authentication schemes that rely on trusted third-party present a single point of failure, as users and smart devices must be registered and authenticated by a central authority. Blockchain technology offers a decentralized, tamper-proof, and flexible solution for authentication and access control of data. By using blockchain, the single point of failure problem in traditional authentication schemes can be resolved. In the context of smart homes, the real-time nature of the environment necessitates the use of fog nodes to provide localized computing services. Fog nodes are closer to IoT devices than cloud nodes, making fog computing more efficient and faster than cloud computing. This paper proposes an authentication scheme for blockchain-enabled fog nodes in smart homes. The scheme involves registering all fog nodes and intelligent devices on a local private blockchain, and authentication is performed jointly by smart contracts on the blockchain and off-chain operations. The scheme provides comprehensive security and better performance, as demonstrated by security analysis and performance evaluation. Moreover, the proposed scheme offers a certain level of user privacy protection.
Homogeneous network publishing privacy protection based on differential privacy uncertainty

With heterogeneous networks become more prevalent, there is a growing need for privacy protection mechanisms in homogeneous networks within heterogeneous networks. Homogeneous networks are particularly vulnerable to malicious attacks when they are published. To solve this issue, we propose a privacy protection method for homogeneous network publishing based on differential privacy uncertainty. Our method is inspired by partitioning, which divides the homogeneous network into community subgraphs and bridging subgraphs. Then, we use differential privacy and random perturbations to achieve the coding rearrangement and decoding of the subgraph uncertain trees. Next, we perturb the node degree sequence based on the generalization idea and design different reconstruction strategies to perturb the subgraph structure features. Finally, we perform postprocessing to fuse the subgraphs by measuring the sensitivity of the bridging nodes. We conducted extensive experimental analysis on real datasets, and the results show that our privacy protection method can simultaneously guarantee the privacy and availability of homogeneous network data publishing.
Issues of ubiquitous music archaeology: Shared knowledge, simulation, terseness, and ambiguity in early computer music

The reconstruction of tools and artworks belonging to the origins of music computing unveils the dynamics of distributed knowledge underlying some of the major breakthroughs that took place during the analogue-digital transition of the 1950s and 1960s. We document the implementation of two musical replicas, the Computer Suite for Little Boy and For Ann (Rising) . Our archaeological ubiquitous-music methods yield fresh insights on both convergences and contradictions implicit in the creation of cutting-edge technologies, pointing to design qualities such as terseness and ambiguity. Through new renditions of historically significant artefacts, enabled by the recovery of artistic first-hand sources and of one of the early computer music environments, MUSIC V, we explore the emergence of exploratory simulations of new musical worlds.
Bugs in machine learning-based systems: a faultload benchmark

The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs’ lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most important factors in each one. Then, we explore the challenges of generating a benchmark of bugs in ML-based software systems and provide a bug benchmark namely defect4ML that satisfies all criteria of standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, and usability. This faultload benchmark contains 100 bugs reported by ML developers in GitHub and Stack Overflow, using two of the most popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important challenges in Software Reliability Engineering of ML-based software systems, like: 1) fast changes in frameworks, by providing various bugs for different versions of frameworks, 2) code portability, by delivering similar bugs in different ML frameworks, 3) bug reproducibility, by providing fully reproducible bugs with complete information about required dependencies and data, and 4) lack of detailed information on bugs, by presenting links to the bugs’ origins. defect4ML can be of interest to ML-based systems practitioners and researchers to assess their testing tools and techniques.
Weaving the pluriverse: childhood encounters with the underground worlds of Birrarung Marr

This article weaves together a series of encounters between young children, trees, and mycelium within the multispecies worlds of Birrarung Marr, an ancient Aboriginal meeting place in the city of Naarm/Melbourne, Australia. Using creative ethnographic methods, it focuses on children’s involvement in an immersive theatre-making project exploring the intricate subterranean networks of communication and care between She-Oak trees and rhizo-mycelial networks in the local area. Weaving ethnographic description and careful engagements with Indigenous philosophies, the concept of pluriversality is evoked to consider how multispecies worlds are variously assembled, stolen, sustained, exhausted, regenerated, and interwoven along Birrarung Marr. Disrupting Western developmental models of childhood, the article argues that children’s animistic understandings of multispecies relations can inform responses to climate change which are sensitive to the intricate dependency relations that sustain life.
Sentiment analysis and research based on two‐channel parallel hybrid neural network model with attention mechanism

Abstract Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores contextual semantic information, and the traditional Recurrent Neural Network (RNN) has information memory loss and vanishing gradient, this paper proposes a Bi‐directional Encoder Representations from Transformers (BERT)‐based dual‐channel parallel hybrid neural network model for text sentiment analysis. The BERT model is used to convert text into word vectors; the dual‐channel parallel hybrid neural network model constructed by CNN and Bi‐directional Long Short‐Term Memory (BiLSTM) extracts local and global semantic features of the text, which can obtain more comprehensive sentiment features; the attention mechanism enables some words to get more attention that highlights important words and improves the model's sentiment classification ability. Finally, the dual‐channel output features are fused for sentiment classification. The experimental results on the hotel review datasets show that the Accuracy of the proposed model in sentiment classification reaches 92.35% and the F 1 score reaches 91.59%.
Network analysis of terms in the natural sciences insights from Wikipedia through natural language processing and network analysis

Abstract Scientists use specific terms to denote concepts, objects, phenomena, etc. The terms are then connected with each other in sentences that are used in science-specific language. Representing these connections through term networks can yield valuable insights into central terms and properties of the interconnections between them. Furthermore, understanding term networks can enhance assessment and diagnostics in science education. Computational means such as natural language processing and network analysis provide tools to analyze term networks in a principled way. This study utilizes natural language processing and network analysis to analyze linguistic properties of terms in the natural science disciplines (biology, chemistry, and physics). The language samples comprised German and English Wikipedia articles that are labelled according to the respective discipline. The different languages were used as contrasting cases. Natural language processing capabilities allowed us to extract term networks from the Wikipedia articles. The network analysis approach enabled us to gain insights into linguistic properties of science terms and interconnections among them. Our findings indicate that in German and English Wikipedia terms such as theory, time, energy, or system emerge as most central in physics. Moreover, the science-term networks display typical scale-free, complex systems behavior. These findings can enhance assessment of science learner’s language use. The tools of natural language processing and network analysis more generally can facilitate information extraction from language corpora in the education fields.
Metaverse Concepts and Marketing

Metaverse is a virtual reality world where users can interact with each other, buy and sell things, and fulfil their dreams through virtual reality or augmented reality. This technology allows people to participate in social activities and entertainment like never before, providing a different dimension of participation, socialization, and digital living space. In the development process of technology, social media has changed the habits of use and involvement, and this change has revolutionized many areas, from personal space to commercial applications. This chapter reviews and discusses metaverse concepts and marketing. The study findings show that metaverse offers businesses a new way to reach their target audience. Integrating the metaverse into future marketing strategies will be the way forward and for the brand to exist for a long time because metaverse offers brands of all kinds the chance to seamlessly combine the accessibility and convenience of the digital world with the immersive experiences of the physical world.
An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms

Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.
Securing Banking Credentials from SQL Injection Attacks using AES Algorithm

Nowadays, people have many bank accounts, and to remember those banking credentials, the details will be noted in a notepad or notes app on the mobile phone. If a phone is hacked, the entire data is destroyed, and our data is at risk. This Web-application will include a sign in and log in page, and after successfully signing in, the user will be able to save their banking credentials, such as mobile number, bank name, account number, ATM card number, card expiry date, UPI id, and UPI pin. The encryption of bank credentials using the AES (Advanced Encryption Standard) technology is the key point here. The system’s ability to handle SQL Injection increases the database’s security and prevents the introduction of hacking code into the database.
Sentiment Analysis using Deep Learning: A Domain Independent Approach

The practice of finding emotion embedded in textual data is known as sentiment analysis, sometimes known as opinion mining. Various sentiment analysis algorithms, including classic Machine Learning models and Deep Learning models, have been suggested up until now. Some Machine Learning-based models, such as Naive Bayes, Decision Tree, SVM, and others, have demonstrated exceptional performance in sentiment categorization. Although Machine Learning algorithms have demonstrated high performance, they are constrained by the quantity of the dataset employed and include feature extraction tasks, which are time demanding. As a result, this study considers Deep Learning (DL)-based models, which include automated feature extraction and can handle massive amounts of data. One of the major issues with existing sentiment analysis models is that they are domain-dependent; hence, if there is a dataset available from a domain on which the model was not trained on, its accuracy is significantly reduced. To make the model domain agnostic, it is trained on datasets from three distinct domains: Twitter US Airline Review dataset, the IMDb Movie Review dataset, and the US Presidential Election dataset. The suggested sentiment analysis model is trained on five different deep learning models: CNN-GRU, CNN-LSTM, CNN, LSTM and GRU. The model's performance was evaluated using test data from three datasets on which the model was trained, as well as a fresh book review dataset scraped from the Amazon website.
A Python-based Grade Converter Application

The aim of this study is to provide a platform where university students can easily convert their cumulative grade point average (CGPA) to percentage. Usually, students are quite comfortable in percentage system as CGPA is a new concept to most of them. Percentage is the basic requirement criteria to be addressed/written in many official forms. As university students get their result in CGPA format, they need to convert them first in percentage, but the procedure of converting CGPA to percentage is not known to many. There are many websites and formulae available on the web, but the accuracy of each is different. In addition, the manual way of calculation can increase error rate. To overcome this issue, a system is proposed that converts CGPA to percentage in an accurate format. A student needs to enter his/her CGPA, and in just one click, he/she gets a converted percentage. The system is developed using Python’s GUI, i.e., Tkinter. The system can be executed in any operating system and is easy to use.
Manuscripts Character Recognition Using Machine Learning and Deep Learning

The automatic character recognition of historic documents gained more attention from scholars recently, due to the big improvements in computer vision, image processing, and digitization. While Neural Networks, the current state-of-the-art models used for image recognition, are very performant, they typically suffer from using large amounts of training data. In our study we manually built our own relatively small dataset of 404 characters by cropping letter images from a popular historic manuscript, the Electronic Beowulf. To compensate for the small dataset we use ImageDataGenerator, a Python library was used to augment our Beowulf manuscript’s dataset. The training dataset was augmented once, twice, and thrice, which we call resampling 1, resampling 2, and resampling 3, respectively. To classify the manuscript’s character images efficiently, we developed a customized Convolutional Neural Network (CNN) model. We conducted a comparative analysis of the results achieved by our proposed model with other machine learning (ML) models such as support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and XGBoost. We used pretrained models such as VGG16, MobileNet, and ResNet50 to extract features from character images. We then trained and tested the above ML models and recorded the results. Moreover, we validated our proposed CNN model against the well-established MNIST dataset. Our proposed CNN model achieves very good recognition accuracies of 88.67%, 90.91%, and 98.86% in the cases of resampling 1, resampling 2, and resampling 3, respectively, for the Beowulf manuscript’s data. Additionally, our CNN model achieves the benchmark recognition accuracy of 99.03% for the MNIST dataset.
New theoretical model of the evolution of blazed gratings in the ion beam etching process

Blazed gratings play a key role in advanced fields such as metaverse, AR and VR, etc. A good triangular cross section morphology is critical for its performance and applications. To investigate how triangular blazed gratings are evolved from rectangular masks, blazed gratings were fabricated by ion beam etching process. A new theoretical model called six-surface intermediate (SSI) model is proposed to explain the morphological evolution from rectangular homogeneous masks to triangular blazed gratings. The actual morphologies of blazed gratings with different process parameters were characterized by scanning electron microscopy. These observations confirm the correctness of the new model. Our research is of important guiding significance for the fabrication of blazed gratings with controllable morphology.
Codeformer: A GNN-Nested Transformer Model for Binary Code Similarity Detection

Binary code similarity detection is used to calculate the code similarity of a pair of binary functions or files, through a certain calculation method and judgment method. It is a fundamental task in the field of computer binary security. Traditional methods of similarity detection usually use graph matching algorithms, but these methods have poor performance and unsatisfactory effects. Recently, graph neural networks have become an effective method for analyzing graph embeddings in natural language processing. Although these methods are effective, the existing methods still do not sufficiently learn the information of the binary code. To solve this problem, we propose Codeformer, an iterative model of a graph neural network (GNN)-nested Transformer. The model uses a Transformer to obtain an embedding vector of the basic block and uses the GNN to update the embedding vector of each basic block of the control flow graph (CFG). Codeformer iteratively executes basic block embedding to learn abundant global information and finally uses the GNN to aggregate all the basic blocks of a function. We conducted experiments on the OpenSSL, Clamav and Curl datasets. The evaluation results show that our method outperforms the state-of-the-art models.
Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution

Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways for more than a decade. In particular, a conventional research named KernelGAN has recently been proposed. To estimate the SR kernel from a single image, KernelGAN introduces generative adversarial networks(GANs) that utilize the recurrence of similar structures across scales. Subsequently, an enhanced version of KernelGAN, named E-KernelGAN, was proposed to consider image sharpness and edge thickness. Although it is stable compared to the earlier method, it still encounters challenges in estimating sizable and anisotropic kernels because the structural information of an input image is not sufficiently considered. In this paper, we propose a kernel estimation algorithm called Total Variation Guided KernelGAN (TVG-KernelGAN), which efficiently enables networks to focus on the structural information of an input image. The experimental results show that the proposed algorithm accurately and stably estimates kernels, particularly sizable and anisotropic kernels, both qualitatively and quantitatively. In addition, we compared the results of the non-blind SR methods, using SR kernel estimation techniques. The results indicate that the performance of the SR algorithms was improved using our proposed method.