Reviewerly/exHarmony huggingface dataset:
Tomeito or Tomahto: Exploring consumer's accent and their engagement with artificially intelligent interactive voice assistants

Abstract Artificially intelligent interactive voice assistants (AIIVAs) are developed to understand language, but there is limited insight into their ability to understand accents. While there have been substantial advancements in understanding multiple languages by AIIVAs, having an understanding of variety of accents is an emerging concern. To address these concerns, we contextualised our study in India, one of the world's most populated and diverse countries with varying accents and dialects. Study 1 collected qualitative data through semi structured interviews with participants, data was subsequently thematically analysed, and a typology was developed with respect to the context of use and consumers' emotional and rational reactions towards AIIVAs when interacting with accents. For Study 2, we implemented the quantitative research method. This was done to reiterate the conceptual model formulated from the qualitative research findings. Findings suggest that positive emotional action has emerged as the most significant factor, followed by rational action and negative emotional action. This study contributes significantly to the theoretical understanding of future consumer behaviour and human‐computer interaction trends. It provides practical implications for managers, tech developers, and other companies working and using speech‐to‐text automatic speech recognition to know that while they train their algorithms with languages, they should be mindful of the diverse accents of their consumers.
High-Availability Encryption-Based Cloud Resource Provisioning System

Cryptosystems are getting fascination because of numerous regular likenesses between these two-region disorder hypothesis and cryptography. In symmetric cryptosystems, the same key is utilized for both encryption and decoding reason. By going through these refinements some security aws were observed in these cryptosystems. To overcome these aws an improved scheme is proposed that is resist against the brute force attack, behavior analysis attack and key recovery attack. The proposed plot has a two-venture calculated map that is an input instrument utilizing a critical factor. Finally, there is a correlation between the proposed conspire and one more form of Baptista type cryptosystem, which shows that the proposed plot is superior to the past ones.
Federated learning aided breast cancer detection with intelligent Heuristic-based deep learning framework

Breast cancer is the second largest cause of female cancer death and one of the most hazardous diseases that leads to a higher mortality rate. One of the eminent medical approaches is breast cancer recognition, which offers scientists and researchers huge complications. Breast cancer detection at an early stage permits the patients to receive suitable treatment, which increases the chances of survival. Thus, this paper utilizes a new form of artificial intelligence training called Federated Learning (FL), especially for breast cancer detection. FL permits individual hospitals to benefit from the rich datasets of multiple non-affiliated hospitals without centralizing the data in one place. Consequently, the mammogram images are collected from the Digital Database for Screening Mammography (DDSM) dataset. Hence, FL utilizes numerous collaborators to build a strong deep-learning model using a large dataset. In this paper, a hybridization of this type of training with meta-heuristic and deep learning is aimed to be proposed for breast cancer diagnosis. This model encloses diverse steps that include (a) image collection, (b) feature extraction, and (c) classification phase. Initially, the mammogram images related to breast cancer are collected with the concept of FL from the affected individuals. Federated learning helps in reducing the processing time and ensures better performance of the proposed model. The obtained images are considered for the feature extraction phase, where the DenseNet architecture is used for extracting the features, which are used into the classification phase with the help of Enhanced Recurrent Neural Networks (E-RNN) for detecting breast cancer. Here, the performance is enhanced by tuning certain parameters in the RNN network using a hybrid optimization algorithm called Hybrid Dragon-Rider Optimization (HDRO) with Dragonfly Algorithm (DA) and Red deer algorithm (RDA) to achieve accurate classification results. Thus, the simulation outcome of the designed method shows 95% and 91% regarding accuracy and MCC. The experimental results demonstrate the effectiveness of the suggested breast cancer diagnosis model through the comparison with conventional approaches using diverse quantitative measures.
Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system

Abstract Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg 1/3 Nb 2/3 O 3 −0.3PbTiO 3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications.
Pedestrian Re-ID based on feature consistency and contrast enhancement

For robust person re-identification(Re-ID), the key is effectively learning the features of body parts and their long-distance dependence. ResNet and Transformer are respectively good at learning local dependence and long-distance dependence between region features due to their respective special structures. In order to fully integrate the advantages of the two models, we propose a novel person Re-ID framework that effectively incorporates pixel-level region features, posture-level relation features and the long-distance dependence of region features. Specifically, we design a Semantic Correction Module (SCM) that corrects pixel-level region features and posture-level relation features in a masked manner to generate discriminative fine-grained features with high pose semantics. Considering the semantic inconsistency between relation features and region features, we propose a Contrastive Association Module (CAM) to interactively enhances the long-distance correlation and local saliency of features in a self-attention way. Finally, to improve the robustness of local and global features, we construct a CAM layer to enhance the representation of features based on their potential relationships. Extensive experiment results on general and occlusion datasets demonstrate that our approach performs favorably against the state-of-the-art methods, e.g. 96% Rank-1 on Market-1501.
Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware

The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for quantum algorithms. We present the first quantum implementation of join ordering, one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes conforming to current quantum annealers. Yet, we also confirm that limits of early-stage technology are quickly reached. Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future QPUs, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and problem size. In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
Big Data Analytics in Healthcare

A vast volume of digitized clinical data has been generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs). These big data in healthcare hold the promise of propelling healthcare to evolve from a proficiency-based art to data-driven science, from a reactive mode to a proactive mode, and from one-size-fits-all medicine to personalized medicine. This chapter first discusses the research background—big data analytics in healthcare, the research framework of big data analytics in healthcare, analysis of the medical process, and the literature summary of diagnosis-treatment pattern mining. Then the challenges for data-driven typical diagnosis-treatment pattern mining are highlighted, including similarity measures between diagnosis and treatment records, typical diagnosis-treatment pattern extraction, prediction, evaluation, and recommendation, when considering the rich temporal and heterogeneous medical information in EMRs. Furthermore, a data-driven unifying diagnosis identification and prediction method (UDIPM) embedding the disease ontology structure is proposed from EMRs to assist in better coding integration of diagnosis. Three categories of typical treatment patterns are mined from doctor order content, duration, and sequence view respectively, which can provide a data-driven guideline to achieve the “5R” goal for rational drug use and clinical pathways.
A Novel Homomorphic Encryption and Consortium Blockchain-Based Hybrid Deep Learning Model for Industrial Internet of Medical Things

Securing Electronic Medical Records (EMRs) is one of the most critical applications of cryptography over the Internet due to the value and importance of data contained in such EMRs. Although blockchain-based healthcare systems can provide security, privacy, and immutability to EMRs, several outstanding security and latency issues are associated with existing schemes. For example, some researchers have used the blockchain as a storage tool which increases delay and adversely affects the blockchain performance since it stores a copy of each transaction. A distributed ledger also requires appropriate space and computational power with increased data size. In addition, existing healthcare-based approaches usually rely on centralized servers connected to clouds, which are vulnerable to denial of service (DoS), distributed DoS (DDoS), and collusion attacks. This paper proposes a novel hybrid-deep learning-based homomorphic encryption (HE) model for the Industrial Internet of Medical Things (IIoMT) to cope with such challenges using a consortium blockchain. Integrating HE with the proposed IIoMT system is a vital contribution of this work. The use of HE while outsourcing to the cloud the storage provides a unique facility to perform any statistical and machine learning operation on the encrypted EMR data, hence providing resistance to collusion and phishing attacks. Our proposed model uses a pre-trained hybrid deep learning model in the cloud and deploys the trained model into blockchain-based edge devices in order to classify and train local models using EMRs. This is further conditioned on the private data of each edge and IoT device connected with the consortium blockchain. All local models obtained are aggregated to the cloud to update a global model, which is finally disseminated to the edge nodes. Our proposed approach provides more privacy and security than conventional models and can deliver high efficiency and low end-to-end latency for users. Comparative simulation analysis with state-of-the-art approaches is carried out using benchmark performance metrics, which show that our proposed model provides enhanced security, efficiency, and transparency.
Multi-Drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue

In a three-dimensional (3D) disaster rescue mission environment, multi-drone mission assignments and path planning are challenging. Aiming at this problem, a mission assignment method based on adaptive genetic algorithms (AGA) and a path planning method using sine–cosine particle swarm optimization (SCPSO) are proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in disaster rescue environments are modeled, including mountains, transmission towers, and severe weather. Third, a cost–revenue function that considers factors such as drone performance, demand for mission points, elevation cost, and threat sources, is formulated to assign missions to multiple drones. Fourth, an AGA is employed to realize the multi-drone mission assignment. To enhance convergence speed and optimize performance in finding the optimal solution, an AGA using both the roulette method and the elite retention method is proposed. Additionally, the parameters of the AGA are adjusted according to the changes in the fitness function. Furthermore, the improved circle algorithm is also used to preprocess the mission sequence for AGA. Finally, based on the sine–cosine function model, a SCPSO is proposed for planning the optimal flight path between adjacent task points. In addition, the inertia and acceleration coefficients of linear weights are designed for SCPSO so as to enhance its performance to escape the local minimum, explore the search space more thoroughly, and achieve the purpose of global optimization. A multitude of simulation experiments have demonstrated the validity of our method.
Aisha: A Custom AI Library Chatbot Using the ChatGPT API

This article focuses on the development of a custom chatbot for Zayed University Library (United Arab Emirates) using Python and the ChatGPT API. The chatbot, named Aisha, was designed to provide quick and efficient reference and support services to students and faculty outside the library’s regular operating hours. The article also discusses the benefits of chatbots in academic libraries, and reviews the early literature on ChatGPT's applicability in this field. The article describes the development process, perceived capabilities and limitations of the bot, and plans for further development. This project represents the first fully reported attempt to explore the potential of a ChatGPT-based bot in academic libraries, and provides insights into the future of AI-based chatbot technology in this context.
Systematic review of data-centric approaches in artificial intelligence and machine learning

Artificial intelligence (AI) relies on data and algorithms. State-of-the-art (SOTA) AI smart algorithms have been developed to improve the performance of AI-oriented structures. However, model-centric approaches are limited by the absence of high-quality data. Data-centric AI is an emerging approach for solving machine learning (ML) problems. It is a collection of various data manipulation techniques that allow ML practitioners to systematically improve the quality of the data used in an ML pipeline. However, data-centric AI approaches are not well documented. Researchers have conducted various experiments without a clear set of guidelines. This survey highlights six major data-centric AI aspects that researchers are already using to intentionally or unintentionally improve the quality of AI systems. These include big data quality assessment, data preprocessing, transfer learning, semi-supervised learning, MLOps, and the effect of adding more data. In addition, it highlights recent data-centric techniques adopted by ML practitioners. We addressed how adding data might harm datasets and how HoloClean can be used to restore and clean them. Finally, we discuss the causes of technical debt in AI. Technical debt builds up when software design and implementation decisions run into "or outright collide with "business goals and timelines. This survey lays the groundwork for future data-centric AI discussions by summarizing various data-centric approaches.
Blockchain and its derived technologies shape the future generation of digital businesses: a focus on decentralized finance and the Metaverse

Without a doubt, the blockchain is one of the most valuable technological advancements to have been introduced over the past decade and has played a significant role in the industrial revolution. The emergence of technologies derived from blockchain, such as decentralized finance (DeFi) and the Metaverse, has fundamentally transformed people's daily lives and profoundly impacted future versions of digital businesses. This study explored the evolution of digital businesses in the near future, with a specific focus on the two primary technologies mentioned above. First, we reviewed DeFi-based technologies, including GameFi, SciFi, SocialFi, and others, which serve as foundational building blocks for future jobs and businesses. Second, we examined Metaverse-based jobs, such as Metaverse-based academies and markets, which are expected to be launched as commonly used businesses. Ultimately, this study provides several guidelines, such as how to use DeFi 2.0 and apply centralized decentralized finance (CeDeFi)-based platforms. Additionally, it offers future directions for launching these businesses, including controlling the progress of artificial intelligence (AI) in practical applications, utilizing cloud-assisted models for the Metaverse, and providing conditional privacy for future Metaverse-based businesses.
Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management

Recently,pharmaceutical corporations are confronting difficulties while tracking their products in the supply chain process, allowing counterfeiters to include their fake medicines into market. Counterfeit drugs were examined as a great challenge for pharmaceutical sector worldwide. Sentiment analysis can be used to analyse customer reviews of drugs to determine overall sentiment towards the drug. Positive reviews can indicate that a drug is effective and well-tolerated, while negative reviews may indicate potential side effects or lack of effectiveness. However, it’s important to note that sentiment analysis is a subfield of natural language processing which uses statistical and machine learning techniques to identify and extract subjective information from source materials. Therefore, this article introduces an Archimedes Optimization with Enhanced Deep Learning based Recommendation System (AOAEDL-RS) for Drug Supply Chain Management. The proposed AOAEDL-RS technique majorly examines the drug reviews for the recommendation of drugs. It follows a three stage process: preprocessing, classification, and parameter tuning. Firstly, the AOAEDL-RS technique performs preprocessing and word2vec embedding processes. Secondly, the context based BiLSTM-CNN (CBLSTM-CNN) model is applied for drug review classification and classification. Thirdly, the AOAEDL-RS technique uses AOA for the optimal hyperparameter tuning of CBLSTM-CNN method. The result analysis of the AOAEDL-RS technique is tested on drug reviews dataset and the outcomes show the improved outcomes of the AOAEDL-RS method.
Professionals' perspectives on ChatGPT in the tourism industry: Does it inspire awe or concern?

The tourism industry has witnessed significant transformations in recent years with the integration of artificial intelligence (AI) technologies. This study explores the echoes surrounding the use of ChatGPT in the tourism industry, focusing on the balance between convenience and challenges. The findings presented in this study are based on interviews conducted with professionals from various fields within the tourism industry. The integration of ChatGPT in the tourism industry brings both convenience and challenges to the forefront. It has become apparent that discussions regarding ChatGPT's advantages, such as value creation for tourism businesses and customers, as well as its disadvantages, such as the lack of human touch and potential data security risks, will persist within the tourism industry.
Learning spatio-temporal discriminative model for affine subspace based visual object tracking

Abstract Discriminative correlation filters (DCF) with powerful feature descriptors have proven to be very effective for advanced visual object tracking approaches. However, due to the fixed capacity in achieving discriminative learning, existing DCF trackers perform the filter training on a single template extracted by convolutional neural networks (CNN) or hand-crafted descriptors. Such single template learning cannot provide powerful discriminative filters with guaranteed validity under appearance variation. To pinpoint the structural relevance of spatio-temporal appearance to the filtering system, we propose a new tracking algorithm that incorporates the construction of the Grassmannian manifold learning in the DCF formulation. Our method constructs the model appearance within an online updated affine subspace. It enables joint discriminative learning in the origin and basis of the subspace, achieving enhanced discrimination and interpretability of the learned filters. In addition, to improve tracking efficiency, we adaptively integrate online incremental learning to update the obtained manifold. To this end, specific spatio-temporal appearance patterns are dynamically learned during tracking, highlighting relevant variations and alleviating the performance degrading impact of less discriminative representations from a single template. The experimental results obtained on several well-known datasets, i.e., OTB2013, OTB2015, UAV123, and VOT2018, demonstrate the merits of the proposed method and its superiority over the state-of-the-art trackers.
Single-shot optical neural network

Analog optical and electronic hardware has emerged as a promising alternative to digital electronics to improve the efficiency of deep neural networks (DNNs). However, previous work has been limited in scalability (input vector length K ≈ 100 elements) or has required nonstandard DNN models and retraining, hindering widespread adoption. Here, we present an analog, CMOS-compatible DNN processor that uses free-space optics to reconfigurably distribute an input vector and optoelectronics for static, updatable weighting and the nonlinearity-with K ≈ 1000 and beyond. We demonstrate single-shot-per-layer classification of the MNIST, Fashion-MNIST, and QuickDraw datasets with standard fully connected DNNs, achieving respective accuracies of 95.6, 83.3, and 79.0% without preprocessing or retraining. We also experimentally determine the fundamental upper bound on throughput (∼0.9 exaMAC/s), set by the maximum optical bandwidth before substantial increase in error. Our combination of wide spectral and spatial bandwidths enables highly efficient computing for next-generation DNNs.