Reviewerly/exHarmony huggingface dataset:
Hybrid medical image zero watermarking via discrete wavelet transform-ResNet101 and discrete cosine transform

This paper suggests a way to move forward with medical images based on the discrete wavelet transform (DWT), ResNet101-DCT (discrete cosine transform), and zero watermarking. In addition, we extract deep medical image attributes using ResNet101, a pre-training network. Furthermore, deep features undergo a transformation via DWT and DCT, then a perceptual hash function is employed to generate the feature vector. The watermark is encrypted by chaotic scrambling the original watermark, an exclusive-OR (XOR) gate into the medical image, and generating and storing the logical key vector. Overall, the feature extraction technique is employed to extract the significant characteristics of the medical image under testing and develop the feature vector. Finally, the feature vector is subjected to an XOR maneuver with the logical key vector, deriving the encrypted watermark. Once the encrypted watermark has been obtained and decrypted, the restored watermark can be used to determine the medical image's ownership and watermark information by calculating the normalized correlation (NC) coefficient. Most NC values are greater than 0.50. The algorithm also shows good robustness against conventional and geometric attacks.
Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.
Advancements in Complex Knowledge Graph Question Answering: A Survey

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.
Advances in Blockchain and Crypto Economics

Over the past decade, blockchains and cryptocurrencies have taken a central stage in financial technology (FinTech) innovation. In 2020–2021, as the academic finance and management community began actively investigating this domain, we issued a call for papers for a special issue to encourage interdisciplinary research in this emerging area. This section of Management Science presents the first systematic collection of knowledge, both theoretical and empirical, focusing on blockchain economics, crypto assets, decentralized finance, and Web3 ecosystems. We describe the editorial protocol employed for this special issue (now included in this volume as a special section), summarize what we learn about the field, and introduce the 15 articles included in the special section. We also offer several observations to highlight foundational issues in the new field and to guide future research in this exciting new area at the intersection of technology and finance.
New method of using a convolutional neural network for 2D intraprostatic tumor segmentation from PET images

Abstract Purpose A new method of using a convolutional neural network (CNN) to perform automatic tumor segmentation from two-dimensional transaxial slices of positron emission tomography (PET) images of high-risk primary prostate cancer patients is introduced. Methods We compare three different methods including (1) usual image segmentation with a CNN whose continuous output is converted to binary labels with a constant threshold, (2) our new technique of choosing separate thresholds for each image PET slice with a CNN to label the pixels directly from the PET slices, and (3) the combination of the two former methods based on using the second CNN to choose the optimal thresholds to convert the output of the first CNN. The CNNs are trained and tested multiple times by using a data set of 864 slices from the PET images of 78 prostate cancer patients. Results According to our results, the Dice scores computed from the predictions of the second method are statistically higher than those of the typical image segmentation ( p -value&lt;0.002). Conclusion The new method of choosing unique thresholds to convert the pixels of the PET slices directly into binary tumor masks is not only faster and more computationally efficient but also yields better results.
High-Threshold Codes for Neutral-Atom Qubits with Biased Erasure Errors

The requirements for fault-tolerant quantum error correction can be simplified by leveraging structure in the noise of the underlying hardware. In this work, we identify a new type of structured noise motivated by neutral-atom qubits, biased erasure errors, which arises when qubit errors are dominated by detectable leakage from only one of the computational states of the qubit. We study the performance of this model using gate-level simulations of the XZZX surface code. Using the predicted erasure fraction and bias of metastable Yb171 qubits, we find a threshold of 8.2% for two-qubit gate errors, which is 1.9 times higher than the threshold for unbiased erasures and 7.5 times higher than the threshold for depolarizing errors. Surprisingly, the improved threshold is achieved without bias-preserving controlled-not gates and, instead, results from the lower noise entropy in this model. We also introduce an XZZX cluster state construction for measurement-based error correction, hybrid fusion, that is optimized for this noise model. By combining fusion operations and deterministic entangling gates, this construction preserves the intrinsic symmetry of the XZZX code, leading to a higher threshold of 10.3% and enabling the use of rectangular codes with fewer qubits. We discuss a potential physical implementation using a single plane of atoms and movable tweezers.Received 4 April 2023Revised 10 July 2023Accepted 6 September 2023DOI:https://doi.org/10.1103/PhysRevX.13.041013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum circuitsQuantum computationQuantum error correctionQuantum Information, Science & Technology
Critical success factors for implementing blockchain technology in construction

In recent years, blockchain technology has attracted momentous attention in the construction industry. However, a state-of-the-art review of critical success factors (CSFs) for implementing blockchain technology in the construction industry is unexplored. In addition, there is no stage framework or a common set of CSFs for blockchain technology in the extant literature. Therefore, this review study aims to develop a stage framework and identify a common set of CSFs for successful blockchain technology implementation by analyzing published articles related to the studied domain. This review study adopted a systematic literature review and a science mapping approach to objectively identify a common set of CSFs, research gaps, and future research directions. Focusing on 78 journal articles retrieved from the Scopus database, influential journals, keywords, countries/regions, and documents in the domain of CSFs for blockchain technology in construction were analyzed. The results revealed that countries like China, USA, UK, and Australia have made the most contributions to this domain. Of the 22 CSFs, five main common sets for blockchain technology were (1) decentralized system (protocol), (2) transparency in data information for construction lifecycle processes, (3) ensuring data immutability, (4) increasing data security and reliability, and (5) providing full traceability of prefabrication. In the stage framework of CSFs for blockchain technology, some CSFs play an essential role throughout the entire construction life cycle processes (e.g., CSF#1 decentralized system and CSF#2 transparency in data information for construction life cycle processes). Four key research gaps and future research directions are proposed. They include (1) digital innovation, (2) smart contracts and information management, (3) intelligent construction, and (4) data analytics methods and techniques. Overall, the findings and checklist of CSFs for blockchain technology would be beneficial for successful exploration and practice in this field.
Partial Annotation-based Video Moment Retrieval via Iterative Learning

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the corresponding semantic-consistent moment clip in the video, which is represented as a pair of the start and end timestamps. Although current methods have achieved satisfying performance, training these models heavily relies on the fully-annotated VMR datasets. Nonetheless, precise video temporal annotations are extremely labor-intensive and ambiguous due to the diverse preferences of different annotators.
From Mining to Mitigation: How Bitcoin Can Support Renewable Energy Development and Climate Action

The world is currently facing a major issue of high emissions from fossil-fuel-based energy sources, which contribute to the persistent problem of climate change. A switch to a renewable-powered infrastructure is necessary to mitigate this challenge. However, the shift to renewable energy faces obstacles, such as high costs and economic uncertainties. This work proposes mitigation of climate change by investigating the potential for bitcoin mining to serve as a means of utilizing surplus renewable energy from planned installations before grid integration. The study’s findings indicate the potential for bitcoin to provide economic benefits as an alternative to grid-powered mining at planned renewable installations across the U.S. states. We show that states like Texas have the maximum potential, with 32 planned renewable installations that could generate combined profits of $47M using bitcoin mining during precommercial operation.
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark

In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is about 37.5 × larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes. Considering the privacy concerns and annotation costs, we leverage the off-the-shelf diffusion models to generate the dataset. To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text. As the name implies, APTM contains an attribute prompt learning stream and a text matching learning stream. (1) The attribute prompt learning leverages the attribute prompts for image-attribute alignment, which enhances the text matching learning. (2) The text matching learning facilitates the representation learning on fine-grained details, and in turn, boosts the attribute prompt learning. Extensive experiments validate the effectiveness of the pre-training on MALS, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks. In particular, APTM achieves a consistent improvement of +6.96 %, +7.68%, and +16.95% Recall@1 accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively. The dataset, model, and code are available at https://github.com/Shuyu-XJTU/APTM.
Intelligent Decision Support for Energy Management: A Methodology for Tailored Explainability of Artificial Intelligence Analytics

This paper presents a novel development methodology for artificial intelligence (AI) analytics in energy management that focuses on tailored explainability to overcome the “black box” issue associated with AI analytics. Our approach addresses the fact that any given analytic service is to be used by different stakeholders, with different backgrounds, preferences, abilities, skills, and goals. Our methodology is aligned with the explainable artificial intelligence (XAI) paradigm and aims to enhance the interpretability of AI-empowered decision support systems (DSSs). Specifically, a clustering-based approach is adopted to customize the depth of explainability based on the specific needs of different user groups. This approach improves the accuracy and effectiveness of energy management analytics while promoting transparency and trust in the decision-making process. The methodology is structured around an iterative development lifecycle for an intelligent decision support system and includes several steps, such as stakeholder identification, an empirical study on usability and explainability, user clustering analysis, and the implementation of an XAI framework. The XAI framework comprises XAI clusters and local and global XAI, which facilitate higher adoption rates of the AI system and ensure responsible and safe deployment. The methodology is tested on a stacked neural network for an analytics service, which estimates energy savings from renovations, and aims to increase adoption rates and benefit the circular economy.
The role of chatgpt on customer service in the hospitality industry: An exploratory study of hospitality workers' experiences and perceptions

Purpose – This study explains the impact of ChatGPT, an artificial intelligence (AI) language model, on customer service in the hospitality industry by exploring the experiences and perceptions of hospitality employees who have utilized ChatGPT in their customer interactions. Methodology – A qualitative approach was adopted using in-depth interviews to collect data from a heterogeneous group of 15 individuals consisting of employees, managers, and other stakeholders directly involved in the hospitality industry in Krabi, Thailand. Findings – Integrating ChatGPT into customer service in the hospitality industry has a significant positive impact. It improves employee skills and knowledge, bridges language barriers, provides valuable recommendations, and helps with productivity and workflow management. In conclusion, ChatGPT is a valuable tool for improving customer service in the hospitality industry, ultimately contributing to a better overall guest experience. Originality of the research – Limited research has been conducted on the specific impact of ChatGPT in the hospitality industry, particularly in Krabi, a popular tourist destination in Thailand. This study aims to fill this gap by examining how ChatGPT affects customer service in the hospitality industry of Krabi, Thailand, and shedding light on its impact.
Modeling the measurement precision of Fringe Projection Profilometry

Three-dimensional (3D) surface geometry provides elemental information in various sciences and precision engineering. Fringe Projection Profilometry (FPP) is one of the most powerful non-contact (thus non-destructive) and non-interferometric (thus less restrictive) 3D measurement techniques, featuring at its high precision. However, the measurement precision of FPP is currently evaluated experimentally, lacking a complete theoretical model for guidance. We propose the first complete FPP precision model chain including four stage models (camera intensity, fringe intensity, phase and 3D geometry) and two transfer models (from fringe intensity to phase and from phase to 3D geometry). The most significant contributions include the adoption of a non-Gaussian camera noise model, which, for the first time, establishes the connection between camera's electronics parameters (known in advance from the camera manufacturer) and the phase precision, and the formulation of the phase to geometry transfer, which makes the precision of the measured geometry representable in an explicit and concise form. As a result, we not only establish the full precision model of the 3D geometry to characterize the performance of an FPP system that has already been set up, but also explore the expression of the highest possible precision limit to guide the error distribution of an FPP system that is yet to build. Our theoretical models make FPP a more designable technique to meet the challenges from various measurement demands concerning different object sizes from macro to micro and requiring different measurement precisions from a few millimeters to a few micrometers.
Microsoft Azure

Azure is the Microsoft cloud computing platform that offers over 200 services to consumers in 140 countries across a global fiber optic network. The result is an interconnected system of Azure Data Centers, Geographies, Regions, Region Pairs, and Availability Zones that provides high availability, disaster recovery, and data resiliency. This chapter summarizes the primary Azure service categories of Monitor, Identity, Networking, Compute, Storage, and Database. As was previously accomplished for AWS, in the lab you will create the same fully operational three-tier web architecture that resides and is fully operational on the Azure infrastructure.
The TAO of Blockchain Intelligence for Intelligent Web 3.0

We are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish. [1]–[3]. In this new epoch, the collaboration of data and knowledge, humans and machines, actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation, unveiling an open future full of boundless possibilities. However, the value of dispersed data extends far beyond passive storage and application. Instead, it has become an incentive and driving force that connects different workers in different worlds, forming a more powerful network of knowledge. No longer monopolized by a select few organizations or companies, the community-driven data sharing and exchange enable every individual to participate and contribute [4]–[6]. The diversification, sharing and integration of data create numerous avenues for learning, exploration, and innovation.
A smart e-health framework for monitoring the health of the elderly and disabled

The healthcare sector is experiencing a significant transformation due to the widespread adoption of IoT-based systems, especially in the care of elderly and disabled individuals who can now be monitored through portable and wearable devices. The widespread implementation of IoT-based systems in the healthcare sector holds immense potential for improving care and ensuring the well-being of vulnerable populations. In our paper, we introduce an e-health framework that utilizes real-time data from inertial, ECG, and video sensors to monitor the health and activities of these individuals. Our framework employs edge computing for efficient analysis and generates notifications based on data analysis while prioritizing privacy by activating multimedia sensors only when necessary. To achieve accurate results, we evaluated each component of our framework separately. Using the MHEALTH dataset, our proposed machine learning model achieves accuracies of 99.97% for inertial sensors' human activity recognition (HAR) and 99.14% for ECG sensors. We also evaluated our first-level inconsistency detection-based anomaly proposal mechanism using one user's data, demonstrating its proof-of-concept. Furthermore, our video-based HAR and fall detection module achieve an accuracy of 86.97% on the well-known DMLSmartActions dataset. We successfully deployed our proposed HAR model with inertial sensors in a controlled experimental environment, yielding promising results with an accuracy of 96% and an F1 score of 90.52%. These evaluations validate the effectiveness of our framework in monitoring the health and activities of elderly and disabled individuals.
ChatGPT: Unlocking the future of NLP in finance

This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the ethical and regulatory considerations, as well as potential future research directions in the field. The literature suggests that ChatGPT has the potential to improve NLP-based financial applications, but also raises ethical and regulatory concerns that need to be addressed. The paper highlights the need for research in robustness, interpretability, and ethical considerations to ensure responsible use of ChatGPT technology in finance.