Reviewerly/exHarmony huggingface dataset:
On the general classifiers <i>ge</i> and <i>zàg</i> in Hakka

Abstract This paper investigates the distribution and properties of the Hakka general classifiers ge and zàg . We focus on the [determiner/numeral + classifier + noun] construction where we observe the relations between the general classifiers and their following nouns, chosen based on their frequency in this construction. We adopt a corpus-based collostructional analysis which calculates the collocational strength values of ge and zàg with following nouns. A Hakka corpus was compiled for the study. The three-way distinction in the collostructional analysis (attractive, neutral, and repulsive) is directly mapped to acceptability of various degrees. The results show that ge is highly correlated with human-denoting nouns, whereas zàg is highly correlated with animal-denoting nouns. Nouns denoting abstract entities or concrete objects without physical properties like size or shape usually lack specific classifiers, and both ge and zàg can collocate with them, albeit with varying degrees of preference. We argue that both ge and zàg are general classifiers because both are more frequently used than specific classifiers and both exhibit disjointed semantic distribution and allow abstract nouns. While they show preferences for different nouns, requirements to qualify as general classifiers are equally met.
Performance of a U<sup>2</sup>-net model for phase unwrapping

Phase unwrapping plays a pivotal role in optics and is a key step in obtaining phase information. Recently, owing to the rapid development of artificial intelligence, a series of deep-learning-based phase-unwrapping methods has garnered considerable attention. Among these, a representative deep-learning model called U2-net has shown potential for various phase-unwrapping applications. This study proposes a U2-net-based phase-unwrapping model to explore the performance differences between the U2-net and U-net. To this end, first, the U-net, U2-net, and U2-net-lite models are trained simultaneously, then their prediction accuracy, noise resistance, generalization capability, and model weight size are compared. The results show that the U2-net model outperformed the U-net model. In particular, the U2-net-lite model achieved the same performance as that of the U2-net model while reducing the model weight size to 6.8% of the original U2-net model, thereby realizing a lightweight model.
Few Shot Class Incremental Learning via Grassmann Manifold and Information Entropy

Few-shot class incremental learning is a challenging problem in the field of machine learning. It necessitates models to gradually learn new knowledge from a few samples while retaining the knowledge of old classes. Nevertheless, the limited data available for new classes not only leads to significant overfitting problems but also exacerbates the issue of catastrophic forgetting in the incremental learning process. To address the above two issues, we propose a novel framework named Grassmann Manifold and Information Entropy for Few-Shot Class Incremental Learning(GMIE-FSCIL). Different from existing methods that model parameters on the Euclidean space, our method optimizes the incremental learning network on the Grassmann manifold. More specifically, we incorporate the acquired knowledge of each class on the Grassmann manifold, ensuring the preservation of their inherent geometric properties by Grassmann Metric Learning(GML) module. Acknowledging the interconnected relationships of knowledge, with information entropy we create a neighborhood graph on Grassmann manifold to maintain inter-class structural information by Graph Information Preserving(GIP) module, thus mitigating catastrophic forgetting of learned knowledge. In our evaluation of CIFAR100, miniImageNet, and CUB200 datasets, we achieved significant improvements in terms of Avg compared to mainstream methods, with at least 2.72%, 1.21%, and 1.27% increases.
Quantum mutual information redistribution in a pure tripartite state by a number partitioning algorithm

Quantum information distribution in a tripartite state plays a fundamental role in quantum information processes. Here we investigate how a bipartite unitary transformation ${U}_{AB}$ redistributes the quantum mutual information with the third party $C$ in a tripartite pure state ${|\ensuremath{\psi}\ensuremath{\rangle}}_{ABC}$ in a ${d}_{A}\ifmmode\times\else\texttimes\fi{}{d}_{B}\ifmmode\times\else\texttimes\fi{}{d}_{C}$-dimensional Hilbert space. In particular, we focus on finding out the optimal unitary transformation ${U}_{AB}^{*}$ that maximizes the quantum mutual entropy between party $A$ and party $C, I(A:C)=S({\ensuremath{\rho}}_{A})\ensuremath{-}S({\ensuremath{\rho}}_{B})+S({\ensuremath{\rho}}_{C})$. We show that the mutual entropy $I(A:C)$ is upper bounded by $2S({\ensuremath{\rho}}_{C})$ derived from the Araki-Lieb inequality. This upper bound can be realized via an optimal unitary transformation for any pure state with the rank ${r}_{C}$ of ${\ensuremath{\rho}}_{C}$ satisfying ${r}_{C}\ensuremath{\le}{d}_{A}$. For a generic pure state with ${r}_{C}&gt;{d}_{A}$, the upper bound cannot be realized by any bipartite unitary transformation. To maximize the mutual entropy in the later case, we propose a fast numerical algorithm to produce an approximate optimal unitary transformation, where our optimization is transformed into a modified number partition problem. The validness of our algorithm is confirmed by its comparison with the results from the Adam algorithm for parameterized unitary transformations. Our approximate algorithm thus provides a practical protocol to implement redistribution of quantum mutual information for a tripartite quantum state with high dimensions.
Enhanced Hyperledger Fabric Network Set-Up for Remittance and Settlement Process

Blockchain is a magnificent technology that has the ability to decentralize the storage, sharing, and management of data and information. Hyperledger Fabric, an open-source, permissioned blockchain initially introduced by IBM as Hyperledger Fabric v1.0 and then more recently as Hyperledger Fabric v1.4.4, is one of the most recent blockchain systems to emerge. There is no clear technique for comparing attributes like performance, security, and scalability, despite the fact that there are multiple blockchain platforms. Furthermore, the new Hyperledger Fabric version was never compared to any other blockchain platform. In the study, the evaluation and settlement for Hyperledger Fabric 1.4.4 have been done. A complete set-up is presented in this document. As more Hyperledger Fabric networks go live, serviceability and operational aspects are becoming increasingly critical. With logging enhancements, health checks, and operational analytics, Fabric v1.4 takes a major step ahead. Fabric v1.4.4 is hence the recommended release for production use. Under heavy workload circumstances, however, the performance of the Hyperledger Fabric v1.4.4 platform did not match that of con- temporary conventional database systems.
Triggering an Email Alert Based on Price Comparison by Web Scraping Using Python

Web scrapping offers a response for the people who want to grab the access to structured information on the web in an electronic plan. Web scrapping, otherwise called web extraction, is a strategy to separate information from the World Wide Web (www) and save it to a record framework or dataset for later recovery or examination. Web scratching utilizes savvy robotization to extricate millions and billions of information from the unlimited information facilitated over the web. Web scrapping has various utilizations like Price Intelligence, Market Research, Real Estate, News and content monitoring, and so on. This paper is about scrapping the prices from different e-commerce websites and then comparing them and finally triggering an email having the link of the website offering a lesser price for the same product. Various websites like trivago, for example, offer least price for specific services after comparing the prices offered by different websites. Authors have attempted to cater this service for a large variety of products and user be getting an email as a result, which will have the link specifying the details of the product along with the website offering least price.
An Approach to Mizo Language News Classification Using Machine Learning

TheAndrew, B. increase in the availability ofSandeep, D. data on the Internet in the past years has created an enormous amount of data and research in the field of Artificial Intelligence and Machine Learning. With the advancement inRobert, L. technology, computational power has also increased dramatically in the past few years, and this has led to more and more advancements in ArtificialAlexander, G. Intelligence research and its applications. Mizo language, which is a low-resource language, also tends to emerge in recent years along with these advancements and with the help of news articles collected from the two biggest news outlets for the Mizo language namely Vanglaini and The Aizawl Post, an approach to news classification based of their category was done in this paper. This paper tested several machine learning methods using supervised classification techniques and got the highest accuracy among other low-resource languages in most of the models tested and among which Multinomial Naive Bayes classification gives an accuracy of 96% and is the highest when compared to the other models.
Impact of Data Poisoning Attack on the Performance of Machine Learning Models

The twenty-first century has witnessed widespread adoption of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). These techniques have provided reliable solutions in various areas, including statistics, information theory, and mathematics. Given the prevalence of ML techniques, there exist various adversaries which question the robustness of ML models. Adversaries aim to manipulate models to their advantage, reducing their performance and accuracy. Data poisoning attack is one such adversary in which the attacker manipulates models by introducing specially crafted poisoned data into the training dataset. This paper presents the performance analysis of different machine learning models with and without the influence of data poisoning attack to predict the probability of diabetes and its effect on accuracy and precision. It has been observed that the SVM (RBF) classifier performs best on clean data, while the KNN classifier is highly affected by data poisoning, with a lesser impact on the SVM (Linear kernel) classifier.
Random Forest Classifier-Based Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a cancerous condition which affects bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by hematologists through observing the blood and bone marrow smears under a microscope. In order to diagnose and classify leukemia, sophisticated cytochemical tests are employed. However, such processes are resource-intensive, time-consuming, and reliant on the expertise of the doctors doing them. In order to diagnose leukemia, image processing techniques are used to examine microscopic smear images for signs of cancerous cells. These methods are simple, quick, cheap, and not influenced by the views of specialists. In this paper, a computer-aided automated diagnostic method is proposed to classify ALL and healthy cells based on Random Forest classifier with most significant features. For this model, the public dataset ALL-IDB 2 has been utilized. The proposed approach provided an accuracy of 99.73% to classify the cells (ALL and healthy). Also, it shows an improvement in accuracy of 6.16%, 16.4%, and 10.43% in comparison to the approaches, i.e., morphological + color feature with SVM, Hausdorff dimension + shape feature with SVM, and GLCM + Morphological with SVM, respectively.
Teenager Friendly News Classification Using Machine Learning Model

Adolescents frequently encounter news reports multiple times daily, which can induce feelings of anxiety, stress, and fear when they come across stories about crimes. Studies indicate that young people tend to replicate behaviors and attitudes they observe in the news, making them vulnerable to becoming numb to violence and increasingly prone to violent and aggressive conduct. Prolonged and repeated exposure to such events may have serious consequences, including fear, insensitivity, and behavioral changes. So, it is essential to have a system that can approximately classify safe and unsafe news for teenagers and only the safe news is visible to them. We address this challenge of text extraction and classification from News Headlines using well-known statistical measures and machine learning (ML) models. In this proposed system, we compare Linear Support Vector Classifier (LSVC), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Random Forest Classifier (RFC), and Decision Tree Classifier (DTC) algorithms in which LR outperforms the other algorithms.
Priority Based Load Balancing for Intercloud Computing Environments

Adoption of cloud computing based computing solutions is growing day by day in nearly all sectors of society. Technological advancements such as load balancing help service providers uphold the quality of service, and thereby retain the confidence of service consumers. Collaborations among cloud environments can be established for resolving load balancing and fault tolerance issues up to a certain extent. However, the gigantic increase in consumption of cloud services makes cloud resource management difficult in intercloud environments too. Load balancing helps resolve workload balancing problems for collaborated cloud platforms. We present an enhanced and priority-oriented mechanism for sharing resources for workload balancing in collaborated cloud environments. In order to provide enhanced load balancing solution, the suggested resource sharing mechanism works on the priority values of participating instances. Employment of the suggested load balancing mechanism avoids starvation by means of lowering waiting time. The suggested technique has been implemented on a physical cloud testbed built using OpenStack cloud computing setup on CentOS Linux operating system. The experimental results reveal lesser waiting time of the highly loaded cloud instances.
Paillier Cryptosystem Based Robust and Reversible Image Watermarking

Watermarking is the method in which an image or a text is secretly embedded in an image taken as the original image. It is helpful in recognizing the original possessor of a particular content. It generally works on different objects like pictures, audio, video etc. Reversible and Robust Watermarking is a technique in which the original image as well as the watermarked image can be retrieved successfully even if the original or the image formed after the embedding phase is attacked by noise or other factors. This work is presenting an algorithm or a technique based on Robust and Reversible Watermarking. The algorithm presented in this work is divided into three phases: namely, watermark generation phase; the phase in which the generated watermark is inserted into the original image; and the phase of extraction of watermark and the original image separately or lossless. The watermark is obtained and embedded to the original image by using weber’s differential excitation descriptor, reference from law of weber, and interpolation linearly. Paillier Cryptosystem is used to encrypt the embedded image. To make the embedded image more secure, Discrete Wave Transform is applied. The original image is not necessary for the extraction process. Linear interpolation in inverse method is used to retrieve the value of the watermark of each block consisting of pixels. To determine the accuracy and robustness of the original image and the watermark that is extracted from the embedded image, different operations, or factors like PSNR, BER and Surviving bit rate are calculated. Random tampered zones could also be found using this algorithm.
A Robust Remote User Authentication Scheme for Supply Chain Management Using Blockchain Technology

For a company's product life cycle to be faster, more efficient, and more effective, supply chain management (SCM) is critical. Nevertheless, present SCM systems must be improved to ensure genuine products and transactions are private and secure. As a result, this study proposes a safe SCM system depending upon blockchain and the Internet of Things to ensure that the products are genuine. All SCM stakeholders can initiate an encrypted, secure transaction for their goods or services due to the Quick Response (QR) scanner that works with the Internet of Things (IoT) and the blockchain-integrated distributed system. Finally, a genuine product from the manufacturer will be sent to the customer. To make authentication quicker and easier for scattered IoT devices, a lightweight asymmetric key encryption mechanism known as Diffie-Hellman key exchange and Hyperledger Fabric-based blockchain technology with on-chain smart contracts are deployed. The service provider registers each SCM stakeholder and provides them with their own public and private keys, which will be used to authenticate participants and IoT devices. Examining security and scalability shows that the recommended solution is more trustworthy and secure than existing methods.
Validating δ-Currency Using Model Checking

In recent years, there is a surfeit of digital currencies, virtual currencies, and cryptocurrencies. These currencies serve as alternatives to fiat currencies in the form of physical currencies or deposits in banks. Some of the common characteristics that differentiate these currencies are how they gain and maintain their value, the anonymity of transactions, and considerations of security, data integrity, and transaction performance in a distributed computing scenario. Digital Currencies are generally issued by Central Banks raising privacy concerns. Cryptocurrencies work outside such formal mechanisms raising concerns about volatility and the possibility of loss. In this paper, we discuss a new variant called δ-Currency which attempts to navigate these concerns and arrive at an alternative mechanism that addresses privacy and financial security considerations in a novel manner. The δ-Currency is currently at a conceptual stage. We make use of Model Checking to validate the architecture, design principles, and implementation approach of δ-Currency. Model Checking focuses on fundamental building blocks of computation and is generally used to validate complex systems operating in an environment that enables a high degree of concurrency. In this paper, we make use of Model Checking to arrive at a common vocabulary, abstractions, and framework to understand seemingly disparate systems which nevertheless achieve the same objective.
What mosses can teach us about design fabulations and feminist more-than-human care

Feminist theory is core to posthumanist HCI and can support more-than-human designers and researchers in questioning what human is being decentered and what marginalized voices are foregrounded. In this article, I interweave feminist and posthumanist design scholarship that affirms natureculture relations through the practice and concept of fabulation. I present a research-through-design practice of producing a short film “I Moss You” that is recorded through a microscope and tells a story about mosses and menses, space travel and earthly survival. I contribute by positioning fabulation as a feminist posthumanist design approach that challenges dominant sociotechnical imaginaries of progress, involves non-humans in speculative storytelling and fosters joy in taking care of and enlivening relations with earth. I discuss how fabulation aligns with feminist values in HCI and design, including critiquing power hierarchies and dominant norms of technology, emphasizing situated knowledges in design practice, and attuning to relational becoming with ecology.
Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

As volcanic stratigraphy provides important information about volcanic activities, such as the eruption style, duration, magnitude, and their time sequences, its observation and description are fundamental tasks for volcanologists. Since outcrops are often obscured in nature, the first task would be identifying stratigraphic exposures in many cases. This identification/selection process has depended on humans and has difficulties in terms of time and effort consumption and in biases resulting from expertise levels. To address this issue, we present an approach that utilizes supervised machine learning with fine-tuning and forms the backbone to automatically extract the areas of stratigraphic exposures in visible images of volcanic outcrops. This study aimed to develop an automated method for identifying exposed stratigraphy. This method will aid in planning subsequent field investigations and quickly outputting results. We used U-Net and LinkNet, convolutional neural network architectures developed for image segmentation. Our dataset comprised 75 terrestrial outcrop images and their corresponding images with manually masked stratigraphic exposure areas. Aiming to recognize stratigraphic exposures in various situations, the original images include unnecessary objects such as sky and vegetation. Then, we compared 27 models with varying network architectures, hyperparameters, and training techniques. The highest validation accuracy was obtained by the model trained using the U-Net, fine-tuning, and ResNet50 backbone. Some of our trained U-Net and LinkNet models successfully excluded the sky and had difficulties in excluding vegetation, artifacts, and talus. Further surveys of reasonable training settings and network structures for obtaining higher prediction fidelities in lower time and effort costs are necessary. In this study, we demonstrated the usability of image segmentation algorithms in the observation and description of geological outcrops, which are often challenging for non-experts. Such approaches can contribute to passing accumulated knowledge on to future generations. The autonomous detection of stratigraphic exposures could enhance the output from the vast collection of remote sensing images obtained not only on Earth but also on other planetary bodies, such as Mars.
Defense against adversarial attacks via textual embeddings based on semantic associative field

Abstract Deep neural networks are known to be vulnerable to various types of adversarial attacks, especially word-level attacks, in the field of natural language processing. In recent years, various defense methods are proposed against word-level attacks; however, most of those defense methods only focus on synonyms substitution-based attacks, while word-level attacks are not based on synonym substitution. In this paper, we propose a textual adversarial defense method against word-level adversarial attacks via textual embedding based on the semantic associative field. More specifically, we analyze the reasons why humans can read and understand textual adversarial examples and observe two crucial points: (1) There must be a relation between the original word and the perturbed word or token. (2) Such a kind of relation enables humans to infer original words, while humans have the ability to associations. Motivated by this, we introduce the concept of semantic associative field and propose a new defense method by building a robust word embedding, that is, we calculate the word vector by exerting the related word vector to it with potential function and weighted embedding sampling for simulating the semantic influence between words in same semantic field. We conduct comprehensive experiments and demonstrate that the models using the proposed method can achieve higher accuracy than the baseline defense methods under various adversarial attacks or original testing sets. Moreover, the proposed method is more universal, while it is irrelevant to model structure and will not affect the efficiency of training.