Reviewerly/exHarmony huggingface dataset:
Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research

In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community has already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the join order optimization problem in databases.
Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.
Beyond Reality: Examining the Opportunities and Challenges of Cross-Border Integration between Metaverse and Hospitality Industries

The study explores the opportunities and challenges that the cross-border integration of “Metaverse + hotel” brings to visitors and the hospitality industry. The study analyses the engagement differences between Metaverse hotels and traditional online/offline strategies regarding engagement immersion, community interaction, personalization, environmental perception, and participation. Metaverse hotel brings the impact of realistic environmental simulation, personalized service, customer engagement, and consumer follow-up feedback. Further, it brings to the hospitality industry the impact of enhancing organizational performance, co-creating value for stakeholders, influencing customer relationship management, and changing how managers communicate. Finally, Metaverse technology raises challenges of security issues, technical barriers, high costs, and uncertain user needs.
Human Wildlife Conflict Mitigation Using YOLO Algorithm

Human wildlife conflict occurs when encounters between humans and wildlife result in negative outcomes such as loss of property, livelihood, and even life for both humans and wildlife, which has a significant impact on the eco system. Interactions between people and wild animals are becoming more common as the human population grows and biodiversity increases. Detecting wild animals and preventing them from entering human habitats, resulting in human-wildlife conflict. It is intended to use machine learning to identify various types of endangered animals. The Dataset contains over 10,000 images in which 7,000 images are training images and 3,000 images are testing images. Our proposed solution employs You only Look once (YOLO) algorithm which is a Convolution Neural Network (CNN) to detect and identify the wild animals using Machine Learning model. The proposed method yields 94% accuracy in detecting the endangered wild animals through the computer vision algorithms. The achieved accuracy percentage is comparatively the highest among the existing model [2] because of the YOLO V4 model.
A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classification, we propose a pipeline that includes image enhancement and data augmentation to improve the quality of extracted features and provide a final diagnosis. An efficient segmentation method based on a U-Net architecture is used to extract Regions of interest (RoI) from mammograms. The purpose is to combine deep learning (DL) with CBR to improve classification accuracy. DL provides accurate mammogram segmentation, while CBR gives an explainable and accurate classification. The proposed approach was tested on the CBIS-DDSM dataset and achieved high performance with an accuracy (Acc) of 86.71 % and a recall of 91.34 %, outperforming some well-known machine learning (ML) and DL approaches.
A new image encryption method based on memristive hyperchaos

This paper reports an effective image encryption method based on a novel two-dimensional memristive-coupled hyperchaotic Sine map. The performance analysis shows that the proposed map is easier to form hyperchaotic phenomenon in multiple parameter regions than some existing chaotic maps, and is suitable for image encryption. The new image encryption algorithm contains permutation and diffusion processes. The permutation process can make the pixel arrangement of the digital image disorderly, and the diffusion process can propagate small changes from the original image to the entire cipher-image with the help of a new bit-based double-loop-shift exclusive-OR (XOR) mechanism. The encryption and decryption effects are numerically investigated, and the security of the proposed algorithm is fully evaluated. Performance evaluations demonstrate that the proposed algorithm has well key sensitivity and robustness, and it has several advantages in terms of information entropy, correlation coefficient and the ability of defending differential attack, compared with the related algorithms.
An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis

Feature selection (FS) is generally associated with the process of using a probabilistic method to select optimal feature combinations during pre-processing steps in data mining. This technique can optimize the datasets’ features that need to be considered to heighten the performance of classification on the grounds of the selected optimal feature set. In this paper, a hybridization model is evolved and applied to select the optimal feature subset based on a binary version of the Hybrid Memory Improved Chameleon Swarm Algorithm (CSA) (HMICSA) and the k-Nearest Neighbor (k-NN) classifier. In this FS model, the following are proposed and applied: (1) Four kinds of transfer functions, (2) Amendments to the velocity of the CSA’s individuals, (3) Addition of internal memory to the CSA’s individuals, and (4) Hybridization of CSA with Ali baba and the Forty Thieves (AFT) algorithm. These actions are aimed to strike an adequate equipoise between global exploration and local exploitation conducts of the search space of the basic CSA. This is to mitigate the problem of early convergence, and to sidestep trapping into a local optima in CSA. The efficacy of the proposed FS algorithm was evaluated on 24 medical diagnosis benchmark datasets collected from different specialized repositories and compared with other k-NN-based FS methods. The all-inclusive outcomes using various evaluation methods disclose the competence of the proposed method in augmenting the classification performance compared to other methods, ensuring its potential in scouting the feature space and designating the most useful features for classification tasks.
Cyber Security Integration with Smart New Age Sustainable Startup Business, Risk Management, Automation and Scaling System for Entrepreneurs: An Artificial Intelligence Approach

Artificial Intelligence and cybersecurity play a crucial role in the success of a new-age sustainable startup business. In terms of risk management, Artificial Intelligenoe can be used to detect and prevent cyber threats, while also providing valuable insights into potential vulnerabilities. Automation can also improve the efficiency of security processes and help scale the business. However, entrepreneurs must be aware of the potential risks associated with AI and cybersecurity, including the possibility of data breaches and ethical issues. Strong security measures, such encryption and routine software updates, as well as knowledge of the laws and policies governing AI and cybersecurity are necessary to mitigate these dangers. A systematic literature review has been done in this paper. This paper provides an overview of new-age sustainable startup businesses and the importance of emerging technologies in new-age sustainable startup businesses, risk management, automation and scaling of entrepreneurs. Further, this paper tells about the impact of AI and Cyber Security in new age sustainable startup businesses. The advantages and challenges of Artificial Intelligence and Cyber Security in a new-age sustainable startup business are also discussed.
Jamming Recognition Algorithm Based on Variational Mode Decomposition

Aiming to address the issue of deception jamming generated by digital radio frequency memory (DRFM), this study proposes a feature extraction algorithm based on variational mode decomposition (VMD) for deception jamming recognition and composite deception jamming recognition. First, models are constructed for the real echo (RE) and the deception jamming signals. Second, the VMD is conducted. Third, the features are extracted from the decomposed intrinsic mode function (IMF) and fed into the support vector machine (SVM) for classification and recognition. To mitigate the challenge of high dimensionality and reduce the complexity of the learning task, mode selection and interclass divisibility of feature selection methods are employed. The effectiveness of the proposed algorithm is verified through simulations. Prior to feature selection, a signal-to-noise ratio (SNR) of 0 dB results in a jamming recognition accuracy exceeding 95%. After feature selection, the recognition accuracy remains largely unchanged, while the recognition speed significantly improves. Compared with other methods in the same field, the recognition accuracy shows a notable improvement. Furthermore, the proposed method is evaluated for its effectiveness in recognizing composite deception jamming, and simulation results validate its performance.
A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer’s Banking Cards

Open finance is evolving and extending open banking. This creates a large context that implies a financial and identity data exchange paradigm, which faces challenges to balance customer experience, security, and the self-control over personal identity information. We propose Self-Sovereign Banking Identity (SSBI), a Blockchain-based self-sovereign identity (SSI) to secure private data sharing by utilizing trusted customer’s banking cards as a key storage and identity transaction-signing enclave. The design and implementation of the SSI framework is based on the Veramo SDK and Ethereum to overcome the limitation of signing curve availability on the current banking Java Cards needed for Hyperledger Indy. SSBI uses the elliptic curve SECP256K1 for transaction signing, which exists for several payment cards in the market. SSBI enables automated financial services and trust in the service provider communication. This work analyzes the flow and framework components, and evaluates the usability, integration, and performance in terms of throughput, latency, security, and complexity. Furthermore, the proposed approach is compared with related solutions. The presented prototype implementation is based on a test Ethereum network and signing transactions on the banking card. The preliminary results show that SSBI provides an effective solution for integrating the customer’s banking cards to secure open banking identity exchange. Furthermore, it allows the integration of several scenarios to support trusted open banking. The Blockchain layer settings need to be scaled and improved before real-world implementation.
Data-Driven single image deraining: A Comprehensive review and new perspectives

Single Image Deraining (SID) aims at recovering the rain-free background from an image degraded by rain streaks. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods have obtained significant improvement over traditional model/prior-based ones. Current studies usually focus on improving the deraining performance by proposing different categories of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research.
EFPN: Effective medical image detection using feature pyramid fusion enhancement

Feature pyramid networks (FPNs) are widely used in the existing deep detection models to help them utilize multi-scale features. However, there exist two multi-scale feature fusion problems for the FPN-based deep detection models in medical image detection tasks: insufficient multi-scale feature fusion and the same importance for multi-scale features. Therefore, in this work, we propose a new enhanced backbone model, EFPNs, to overcome these problems and help the existing FPN-based detection models to achieve much better medical image detection performances. We first introduce an additional top-down pyramid to help the detection networks fuse deeper multi-scale information; then, a scale enhancement module is developed to use different sizes of kernels to generate more diverse multi-scale features. Finally, we propose a feature fusion attention module to estimate and assign different importance weights to features with different depths and scales. Extensive experiments are conducted on two public lesion detection datasets for different medical image modalities (X-ray and MRI). On the mAP and mR evaluation metrics, EFPN-based Faster R-CNNs improved 1.55% and 4.3% on the PenD (X-ray) dataset, and 2.74% and 3.1% on the BraTs (MRI) dataset, respectively. EFPN-based Faster R-CNNs achieve much better performances than the state-of-the-art baselines in medical image detection tasks. The proposed three improvements are all essential and effective for EFPNs to achieve superior performances; and besides Faster R-CNNs, EFPNs can be easily applied to other deep models to significantly enhance their performances in medical image detection tasks.
Examining the context‐specific reasons and adoption of artificial intelligence‐based voice assistants: A behavioural reasoning theory approach

Abstract AI‐based voice assistants are some of the most popular consumer products of artificial intelligence technology. This study investigates the influence of the reasoning process on overall consumer attitudes and adoption intentions towards AI‐based voice assistants. Behavioural reasoning theory was applied to hypothesize relationships between values, context‐specific reasons, attitudes, and adoption intention. Partial least squares structural equation modelling was used to test the research model and hypotheses using data from 550 potential consumers in India. This research suggests a positive influence of consumers' ‘reasons for’ on their attitude and adoption intentions towards AI‐based voice assistants, as well as a favourable impact of consumers' values on ‘reasons for’. This study discovered that ‘reasons for’ and ‘reasons against’ are not only opposites but are entirely autonomous and have an independent impact on consumers' attitudes towards AI‐based voice assistants. This research improved our understanding of AI‐based product adoption by identifying the paramount role of consumers' values and the ‘reasons for’ and ‘reasons against’ in shaping their attitudes and adoption intentions towards AI‐based voice assistants.
Trinity-Net: Gradient-Guided Swin Transformer-Based Remote Sensing Image Dehazing and Beyond

Haze superimposes a veil over remote sensing images, which severely limits the extraction of valuable military information. To this end, we present a novel trinity model to restore realistic surface information by integrating the merits of both prior-based and deep learning-based strategies. Concretely, the critical insight of our Trinity-Net is to investigate how to incorporate prior information into CNNs and Swin Transformer for reasonable estimation of haze parameters. Then, haze-free images are obtained by reconstructing the remote sensing image formation model. Although Swin Transformer has shown tremendous potential in the dehazing task, which typically results in ambiguous details. We devise a gradient guidance module that naturally inherits structure priors of gradient maps, guiding the deep model to generate visually pleasing details. In light of the generality of image formation parameters, we successfully promote Trinity-Net to natural image dehazing and underwater image enhancement tasks. Notably, the acquisition of large-scale remote sensing hazy images and natural hazy images in military scenes is not feasible in practice. To bridge this gap, we construct a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Remote Sensing Image Dehazing Benchmark</i> (RSID) and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Natural Image Dehazing Benchmark</i> (NID), including 1000 real-world hazy images with corresponding ground truth images, respectively. To our knowledge, this is the first exploration to develop dehazing benchmarks in the military field, alleviating the dilemma of data scarcity. Extensive experiments on three vision tasks illustrate the superiority of our Trinity-Net against multiple state-of-the-art methods. The datasets and code are available at https://github.com/chi-kaichen/Trinity-Net.
A Survey of Deep Learning for Alzheimer’s Disease

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.
Combined Interleaved Pattern to Improve Confusion-Diffusion Image Encryption Based on Hyperchaotic System

The quality of encryption is dependent on the complexity of the confusion-diffusion pattern and the quality of the keystream employed. To enhance complexity and randomness, this study proposes a combination of multiple interleaved patterns, including Zigzag, Hilbert, and Morton patterns to complicate the confusion-diffusion. The keystream is generated from the improved logistic map and the 6D hyperchaotic map, which complement each other due to their sensitivity to initial conditions and control parameters. This produces highly random and nonlinear keystreams, making them difficult to predict. The encryption process consists of four phases, alternating between diffusion and confusion. Furthermore, SHA-512 is used to enhance key space and sensitivity. Based on the test results, the proposed encryption technique can withstand various attacks, such as statistics, differential, brute force, and NIST randomness tests, as well as data loss and noise attacks. Most of the results are better than previous studies.