Reviewerly/exHarmony huggingface dataset:
400 Gb/s physical random number generation based on deformed square self-chaotic lasers

A circular-sided square microcavity laser etched a central hole has achieved chaos operation with a bandwidth of 20.8 GHz without external optical feedback or injection, in which the intensity probability distribution of a chaotic signal with a two-peak pattern was observed. Based on the self-chaotic microlaser, physical random numbers at 400 Gb/s were generated by extracting the four least significant bits without other complex post-processing methods. The solitary chaos laser and minimal post-processing have predicted a simpler and low-cost on-chip random number generator in the future.
Polarimetric image denoising on small datasets using deep transfer learning

Although deep learning-based methods have achieved great success in various polarimetric imaging tasks, the performance and the generalization ability are strongly dependent on massive training data, which is a critical limitation and limits the practical applications. In this paper, for the first time to our knowledge, we present a deep transfer learning-based solution for polarimetric image denoising. This solution performs the transfer learning by fine-tuning a denoising model pre-trained on a large-scale color image dataset and using a small-scale polarimetric dataset. The experimental results show that, based on a small-scale dataset, the proposed network can achieve almost the same denoising performance as that with a large-scale dataset. The polarization parameters, i.e., the degree of polarization and the angle of polarization, can be reconstructed simultaneously. In addition, serials of experiments demonstrate the generalization ability of the method for different materials and noise levels.
Development of a Framework for Metaverse in Education: A Systematic Literature Review Approach

A more interactive learning environment is made possible by the metaverse, a made-up world with vastly expanding digital spaces.The metaverse is a development in synchronous communication that enables many users to share different experiences.This study proposes a research framework for adopting metaverse in education.A systematic literature review using the PRISMA methodology identified seventy-three research papers on metaverse and education.Also, this research provided various applications, challenges, dominant themes of research, and future perspectives of a metaverse in education.The proposed framework discusses multiple drivers for adopting a metaverse in education.There are few papers in the metaverse for education, so this research tries to fill the gap.This research also proposed twenty-seven future research questions which can addressed by future researchers.This research will benefit students and teachers across universities/ colleges and schools.
Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms

The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision–recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure’s numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images.
A Study of Metaverse Exhibition Sustainability on the Perspective of the Experience Economy

With the development of information technology, many well-known art museums and galleries have adopted metaverse exhibitions for online display and dissemination of art information, building sustainable ways to participate in and experience art. Users can access the metaverse exhibition platform through the internet and enjoy classic artworks in the virtual space, which has gained many forms of artistic perception and improved physical and mental health. Currently, the factors affecting user use intentions in existing metaverse exhibition studies still need to be clarified and need to be studied in depth. This paper explores the relationship between users’ entertainment experience, educational experience, presence, affective responses, and use intention through a survey of 536 users who had experienced a metaverse exhibition. The research results show that users’ use intentions are influenced by entertainment experiences and educational experiences. Presence mediates between entertainment experiences and educational experiences on use intention. Affective responses play a moderating role between the educational experience and use intention. This study provides a relevant theoretical basis for sustainable urban and socio-economic development from a digital perspective. At the same time, this study enables the metaverse exhibition platform to design and create valuable and healthy artistic content with higher quality. The emotional state of the user’s experience was better attended to so that mental health was enhanced and managed. Furthermore, it also provided a guidance program for the development of educational experience and economic sustainability of the exhibition industry.
Implementation of IoT Enabled RFID Driven Human Life Rescuer in Railways

As per the Data Compiled by the Ministry of Indian Railways, there are nearly 5413 people who lost their lives in Railway accidents from 2015-2020, most of which are due to careless and rushed boarding & de-boarding. Thus, a proposed model called “RFID enabled Railway Rescuer” is introduced to minimize these railway accidents. The proposed Internet of Things (IoT) system consists of a Radio Frequency Identification (RFID) tag, that detects the passenger in ZOR (Zone of Risk - The Door Edge) which in turn triggers the RFID reader fixed in the compartment. The Activated RFID reader will sense all the RFID tags that are present in the ZOR by providing alerts in the form of sound beeps or lights on the Door Edge. Or in other cases, it sends quick notifications to the passengers who are holding respective RFID tags. These notifications alert the passenger to change their standing position. By this, the proposed model tends to minimize railway accidents & thus save many innocent lives.
A weighted metric scalarization approach for multiobjective BOHB hyperparameter optimization in LSTM model for sentiment analysis

In recent years, hyperparameter tuning has become increasingly essential, especially for neural network models, where models with good performance generally require a long time to train. In contrast, fast-trained models often result in poor model performance. Thus, it is essential to determine the optimal hyperparameter configuration for a neural network model that can produce good model performance and fast training time. This paper presents a multiobjective optimization for Long-Short Term Memory (LSTM) model using Bayesian optimization-Hyperband (BOHB) with a weighted metric scalarization method. The objective functions used are the F1-score (Macro) and model training time, where both of the objective functions are considered equally important. The data used in this paper are the SmSA and EmoT datasets taken from the IndoNLU benchmark. The results are optimized model using multiobjective BOHB optimization with weighted metric scalarization method produced higher F1-score and faster model training time in both datasets compared to the baseline model and optimized model using single objective BOHB.
Source-free and black-box domain adaptation via distributionally adversarial training

Source-free unsupervised domain adaptation is one class of practical deep learning methods which generalize in the target domain without transferring data from source domain. However, existing source-free domain adaptation methods rely on source model transferring. In many data-critical scenarios, the transferred source models may suffer from membership inference attacks and expose private data. In this paper, we aim to overcome a more practical and challenging setting where the source models cannot be transferred to the target domain. The source models are considered as queryable black-box models which only output hard labels. We use public third-party data to probe the source model and obtain supervision information, dispensing with transferring source model. To fill the gap between third-party data and target data, we further propose Distributionally Adversarial Training (DAT) to align the distribution of third-party data with target data, gain more informative query results and improve the data efficiency. We call this new framework Black-box Probe Domain Adaptation (BPDA) which adopts query mechanism and DAT to probe and refine supervision information. Experimental results on several domain adaptation datasets demonstrate the practicability and data efficiency of BPDA in query-only and source-free unsupervised domain adaptation.
Predicting Disaster Tweets using Enhanced BERT Model

Twitter has become an essential means of communication in emergency situations. The widespread use of smart phones, anyone can immediately report any incident they witness. As a result, more agencies are interested in programmatically monitoring Twitter. Social media sites such as Twitter provide a common forum for sharing and exchanging personal experiences with others. Most of the people share personal experiences, neighborhood problems also the activities on social websites to let unwanted peoples to know. Many rescue organizations constantly monitor this kind of data to detect disasters and reduce the likelihood of casualties. However, humans cannot manually comb through vast amounts of data to uncover threats in real-time. To achieve this, many studies have proposed representing words in a form that computers can understand and applying machine learning techniques to the word representations to determine the sentiment of the text. Previous search techniques provide single embeddings or representations of words from a given document. However, the new Advanced Contextual Embedding is Bidirectional Encoder Representations from Transformers (BERT) method creates different vectors for the same expression with different settings. Although there are no specific studies on how BERT embeddings can help analyze catastrophe-type tweets, these representations have been successfully used in various Natural Language Processing (NLP) tasks.
Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

The Internet of Things (IoT) is comprised of millions of physical devices interconnected with the Internet through network that performs a task independently with less human interference. Despite the benefits of IoT, it is vulnerable to malicious attacks. Therefore, machine learning (ML) inspired decision-making is hardly preferred due to drawbacks (the requirement that every training dataset is stored on the central database), computation cost related to the training of massive amounts of information on the unified server, and security issues related to transferring attained statistics from IoT sensor to the server. The Federated Learning (FL) algorithm is the adaptable and most prominent way to resolve the shortcoming of ML-based techniques. The study proposes a Pelican Optimization Algorithm with Federated Learning Driven Attack Detection and classification (POAFL-DDC) technique in the IoT. The POAFL-DDC technique operates on decentralized on-device data for attack detection in the IoT. By substituting update weight with the central FL server, the data is placed on the local IoT device whereas federating training cycles on the DL model. For attack detection process, deep belief network (DBN) model is used in this work. In this study, the POA is utilized to optimize the DBN hyperparameters. The experimental validation of the POAFL-DDC algorithm is tested on TON_IOT dataset and the results are examined in terms of different measures. The experimental outcomes demonstrated that the POAFL-DDC technique reaches superior results over other models.
Certifying unknown genuine multipartite entanglement by neural networks

Abstract Suppose we have an unknown multipartite quantum state, how can we experimentally find out whether it is genuine multipartite entangled or not? Recall that even for a bipartite quantum state whose density matrix is known, it is already NP-Hard to determine whether it is entangled or not. Therefore, it is hard to efficiently solve the above problem generally. However, since genuine multipartite entanglement (GME) is such a fundamental concept that plays a crucial role in many-body physics and quantum information processing tasks, finding realistic approaches to certify GME is undoubtedly necessary. In this work, we show that neural networks can provide a nice solution to this problem, where measurement statistics data produced by measuring involved quantum states with local measurement devices serve as input features of neural networks. By testing our models on many specific multipartite quantum states, we show that they can certify GME very accurately, even including some new results unknown before. We also exhibit a possible way to improve the efficiency of our models by reducing the size of features. Lastly, we show that our models enjoy remarkable robustness against flaws in measurement devices, implying that they are very experiment-friendly.
Text Categorization using Supervised Machine Learning Techniques

Text categorization is a task for text mining that involves pattern classification and is essential for the effective management of textual information systems (TIS). Each document is automatically assigned one or more categories from a set of predetermined categories as part of TIS. There is increased interest in creating tools to aid people in more effectively finding, filtering, and managing existing digital resources as the amount of information is rising. Those developed tools are involved in the administrative sectors and social benefits. This paper provides a general overview of text categorization models using various supervised machine learning techniques, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, and compares their performance on various metrics, including accuracy and precision. The comparative analysis shows that the most effective algorithm for text classification is NVM with the highest accuracy of 96.86%, furthermore, AdaBoost is the worst in this case study with its minimum accuracy of 74.49%. We have also shown a comparison of other supervised machine-learning algorithms in this paper.
Financial development and income inequality in Africa

Abstract Across the globe, a rise in income inequality has been experienced for the last two decades, particularly in developing countries. This problem of income inequality poses a challenge to Africa’s ability to attain the United Nations (UN) Sustainment Development Goals (SDGs) of reduced inequalities (SDG-10). Against this backdrop, there is a need to harness the potential of financial development to reduce income inequality in Africa. Therefore, this study empirically examines how financial development affects income inequality in Africa. Financial development dimensions, access, depth, efficiency, and stability were considered to achieve the study’s objective. The study applied the system generalized method of moments (SGMM) to analyse data and the findings showed that each dimension of financial development had a varying impact on income inequality. Access, stability and efficiency components of financial development reduce income inequality, while the depth dimension of financial development exacerbates income inequality in Africa. Therefore, the study recommends that policymakers should not neglect other dimensions of finance in facilitating economic development.
Optimal state manipulation for a two-qubit system driven by coherent and incoherent controls

Optimal control of two-qubit quantum systems attracts high interest due to applications ranging from two-qubit gate generation to optimization of receiver for transferring coherence matrices along spin chains. State preparation and manipulation are among important tasks to study for such systems. Typically coherent control, e.g., a shaped laser pulse, is used to manipulate two-qubit systems. However, the environment can also be used—as an incoherent control resource. In this article, we consider optimal state manipulation for a two-qubit system whose dynamics is governed by the Gorini–Kossakowski–Sudarshan–Lindblad master equation, where coherent control enters into the Hamiltonian and incoherent control into both the Hamiltonian (via Lamb shift) and the superoperator of dissipation. We exploit two physically different classes of interaction with coherent control and optimize the Hilbert–Schmidt overlap between final and target density matrices, including optimization of its steering to a given value. We find the conditions when zero coherent and incoherent controls satisfy the Pontryagin maximum principle and, in addition, when they form a stationary point of the objective functional. Moreover, we find a case when this stationary point provides the globally minimal value of the overlap. Using upper and lower bounds for the overlap, we develop one- and two-step gradient projection methods operating with functional controls.
VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

Abstract Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies.
Machine Learning, Deep Neural Network and Natural Language Processing Based Recommendation System

Nowadays, data clustering has become a key research area in many fields. Text clustering is the process of classifying documents into predefined categories based on their content. It automatically assigns natural language texts to predetermined categories. Some of the main requirements of text retrieval systems include text clustering, which extracts text in response to a user query, and text understanding systems, which aim to transform text to answer questions, produce summaries or extract data, etc. In this paper, in order to solve this clustering problem, one of the unsupervised machine learning techniques of clustering has been applied for the exploitation of the database of the newspaper “El País” to build a recommendation system for the journal articles. This study included the following steps. First, the collection and preprocessing of the data by manipulating the keywords, then the analysis of the data by the K-means clustering method, followed by the choice of the number of clusters by the elbow method. Next, automatic language processing was used as a similarity measure with the K-means clustering method to determine similar articles based on sections, keywords, and titles. A deep neural network was also implemented to predict the temporal probabilistic distribution of the searched keywords. The results obtained from the clustering showed that there are twenty-three different segments of newspaper articles in this journal. Also, after training the neural network on the data, the temporal probability distributions of the searched keywords were visualized.
Controlling photons by phonons via giant atom in a waveguide QED setup

We investigate the single photon scattering in a phonon-photon hybrid system in the waveguide quantum electrodynamics (QED) scheme. In our consideration, an artificial giant atom, which is dressed by the phonons in a surface acoustic wave resonator, interacts with a coupled resonator waveguide (CRW) nonlocally via two connecting sites. Together with the interference effect by the nonlocal coupling, the phonon serves as a controller to the transport of the photon in the waveguide. On the one hand, the coupling strength between the giant atom and the surface acoustic wave resonator modulates the width of the transmission valley or window in the near resonant regime. On the other hand, the two reflective peaks induced by the Rabi splitting degrade into a single one when the giant atom is large detuned from the surface acoustic resonator, which implies an effective dispersive coupling. Our study paves the way for the potential application of giant atoms in the hybrid system.