Reviewerly/exHarmony huggingface dataset:
CTBANet: Convolution transformers and bidirectional attention for medical image segmentation

Authors:
raw_author_name: Li Pan
raw_affiliation_string: Faculty of Engineering, Technology and Built Environment, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia

Abstract: In the last few years, Transformer has revolutionized the area of medical image segmentation. Several similar studies have used the UNet architecture to combine convolutional neural networks with transformers. However, these approaches fail to account for the speed at which segmentation occurs and the ability to extract features within the Transformer. They fail to consider the fact that changing the shape of the feature maps in a subtle way can be used for rapid extraction of local and global information. To solve the above problems, CTBANet (Convolutional Transformer and Bidirectional Attention Based for Medical Image Segmentation) is proposed, which has two prominent components, CTblock (Convolutional Combined Transformer module) and BAblock (Bidirectional Attentionblock). CTblock integrates the strengths of CNNs and Transformers, enabling it to extract spatial details and global data. In order to improve the speed and accuracy of the model, multi-scale pyramid pooling is embedded into PAM, named APAM (Asymmetric PAM), and strip convolution is embedded into CAM, named ACAM (Asymmetric CAM). Medical image segmentation is a critical issue in the medical field, and the experimental results of the benchmarks show that our model is obviously more accurate and faster than the other methods in segmenting medical images.
Performance comparison of multiple scoring algorithms: A study of automatic scoring of English text

Authors:
raw_author_name: Lingling Hu

Abstract: Objective:In order to save teachers’ correcting time, improve the accuracy and efficiency of English composition grading.Methods: This paper briefly introduces the algorithm of deep sentence smoothness and text semantic matching based on graph neural network, and then designs an automatic scoring algorithm for English text. Result: The experimental data was collected from 12,000 essays written by international students in the United States in the Pratt &amp; Whitney Foundation’s Automated Student Value Assessment Project (ASAP), and these data were graded through a comparative experiment,Through comparative tests, the automatic scoring algorithm designed in this paper can achieve better scoring results and better handle automatic essay scoring problems. Among all the experimental mean values of evaluation methods, the experimental mean value of the algorithm designed in this paper is 0.790, the smoothness algorithm is 0.768, and the text matching vector is 0.759. The experimental mean values of the other two traditional automatic scoring algorithms are 0.710 and 0.712 respectively, and the results are lower than the algorithm designed in this paper. Conclusion: According to the experimental results, it can be concluded that good feature selection can give good scoring performance to the algorithm and cope with the problem of automatic scoring. At the same time, it also confirms the feasibility of the algorithm designed in this paper, which can be effectively applied in practical English composition scoring.
EGLR: Two-staged Explanation Generation and Language Reasoning framework for commonsense question answering

Authors:
raw_author_name: Wei Liu
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 201114, China
raw_author_name: Zheng Huang
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 201114, China
raw_author_name: Chao Wang
raw_affiliation_string: Institute of Artificial Intelligence, Shanghai University, Shanghai, 200444, China
raw_author_name: Yan Peng
raw_affiliation_string: Institute of Artificial Intelligence, Shanghai University, Shanghai, 200444, China
raw_author_name: Shaorong Xie
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

Abstract: In the field of natural language processing, strong commonsense reasoning abilities are often required for intelligent systems to excel in the task of commonsense question answering (QA). To enhance the interpretability of QA systems, a natural approach is to generate textual explanations in addition to the predicted answers, ensuring the understandability of the results. Recent efforts address this by using a prompted language model (LM) with frozen parameters to generate explanations in a few-shot manner. These explanations are then used as additional context to guide a finetuned LM in making the final decision. However, these methods still underutilize the semantic information embedded in the explanatory text. Consequently, the reasoning models tend to rely on word co-occurrence and the knowledge stored in the model itself rather than fully exploiting the explanations. Therefore, we propose a two-staged Explanation Generation and Language Reasoning Framework (EGLR), Our framework takes advantage of the in-context learning capability of LMs to generate explanations and reformulates the reasoning task based on explanations as a semantic matching problem. Through joint prompting and training, our model can select the most appropriate explanation by comparing multiple explanations. Experimental results on three public datasets demonstrate that our framework achieves superior performance on the full dataset while maintaining performance on out-of-domain scenarios.
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation

Authors:
raw_author_name: Suraj Rajendran
raw_affiliation_string: Tri-Institutional Computational Biology & Medicine Program, Cornell University, Ithaca, NY, USA
raw_author_name: Weishen Pan
raw_affiliation_string: Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
raw_author_name: Mert R. Sabuncu
raw_affiliation_string: Cornell Tech, Cornell University, New York, NY, USA
raw_author_name: Yong Chen
raw_affiliation_string: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
raw_author_name: Jiayu Zhou
raw_affiliation_string: Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
raw_author_name: Fei Wang
raw_affiliation_string: Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA

Abstract: In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C4) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C4 approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C4 in health care, including its present stage, potential opportunities, and associated challenges.
Branch-cut algorithm with second-order residues in phase unwrapping

Authors:
raw_author_name: Meiqi Fang
raw_affiliation_string: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong Universityhttps://ror.org/017zhmm22, Xi'an 710049, Shaanxi, China

Abstract: This Letter presents a novel, to the best of our knowledge, branch-cut algorithm for unwrapping phase maps acquired through fringe projection systems. The algorithm exposes second-order residues with vortex phase structures, computed from the original wrapped phase distribution using a second-order derivative that considers the wrap cycle. Remarkably, it reveals a consistent vorticity direction associated with these vortex structures, significantly facilitating branch-cut connections between residues, regardless of whether they carry the same or opposite charges. This innovative approach challenges a longstanding misconception in conventional branch-cut methods, which traditionally connected matched residues only when they had opposite charges. As a result, the algorithm provides a highly accurate methodology for explaining how phase behavior relates to surface characteristics. Its effectiveness is demonstrated through both computer simulations and practical experiments.
Utilisation of Service Robots in Accommodation Businesses From the Perspective of the Technology Acceptance Model

Authors:
raw_author_name: Burhan Sevim

Abstract: The advancement of technology and increased sales of service robots have led to discussions about their usage. Recently, the use of service robots in accommodation businesses has become a topic of examination. This study aims to explore the behavioural intentions of accommodation business managers towards using service robots from the perspective of the technology acceptance model. As part of this project-supported study, the 'Sanbot Elf' service robot was rented for 14 days and demonstrated in six accommodation businesses in Kastamonu. Semi-structured interviews were conducted with managers and unit supervisors after the demonstration, and the process was also monitored through participant observation. The data were analysed using content analysis and participant observation methods. It was determined that participants' opinions on service robots were generally positive. Participants stated that personalised service presentations could be provided with service robots and that these robots could be used in different departments and marketing processes. The study concluded that service robots could positively impact service quality, are an exciting technology for accommodation businesses and the use of service robots could increase in the future. Participants' attitudes towards using service robots in accommodation businesses were positive in this context.
Blockchain aware decentralized identity management and access control system

Authors:
raw_author_name: Aarti Amod Agarkar
raw_affiliation_string: Marathawada Mitra Mandal's College of Engineering, Karvenagar, Pune, Maharashtra, 411030, India
raw_author_name: Mandar Karyakarte
raw_affiliation_string: Vishwakarma Institute of Information Technology, Kondhwa (Budruk), Pune, Maharashtra, 411048, India
raw_author_name: Lalit Kulkarni
raw_affiliation_string: Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India

Abstract: Identity Management Systems (IMS) manage individual profiles, user authorization, authentication, and access privileges. Paper-based profiles and defacement are vulnerable to spoofing, loss, and theft. Digital platforms with efficient IMS boost administrative processes and bring transparency and interoperability among establishments. The traditional paper-based identity systems are slow and rigid. Conventional centralized IMS lack transparency and are vulnerable to single-point failure. This paper presents a blockchain-aware decentralized identity management and access control (BADIMAC) model that mitigates these barriers. BADIMAC allows users to keep their digital identity within their control (i.e., self-sovereign identity). On the other hand, with the help of BADIMAC, service providers across enterprise boundaries can verify their identity without depending on any central authority. BADIMAC offers transparency and better security. The decentralized consensus-based design of BADIMAC enables the verifier to validate claims and establish proof of digital identity with process effectiveness.
Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond

Authors:
raw_author_name: Debin Wei
raw_affiliation_string: Communication and Network Laboratory, Dalian University, Dalian 116622, China
raw_author_name: Hongji Xie
raw_affiliation_string: Communication and Network Laboratory, Dalian University, Dalian 116622, China
raw_author_name: Zengxi Zhang
raw_affiliation_string: Dalian University of Technology, Dalian 116024, China

Abstract: Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
Continuous-variable quantum key distribution robust against environmental disturbances

Authors:
raw_author_name: Huanxi Zhao
raw_author_name: Tao Wang
raw_affiliation_string: Shanghai Research Center for Quantum Sciences
raw_author_name: Yuehan Xu
raw_author_name: Lang Li
raw_author_name: Zicong Tan
raw_author_name: Peng Huang
raw_affiliation_string: Shanghai Research Center for Quantum Sciences
raw_author_name: Guihua Zeng
raw_affiliation_string: Shanghai Research Center for Quantum Sciences

Abstract: Continuous variable quantum key distribution (CV-QKD) can guarantee that two parties share secure keys even in the presence of an eavesdropper. However, the polarization direction of the coherent state transmitted in CV-QKD is susceptible to environmental disturbances during channel transmission, making it difficult to share keys consistently over long periods of time. Therefore, a CV-QKD system that can resist environmental disturbance is very urgent. In this paper, we propose a new optical architecture for CV-QKD based on the Faraday-Michelson interference (FMI) structure, and finally form an all-single-mode (SM) fiber-based stable CV-QKD system which employs transmitted local oscillator (TLO) scheme and discrete modulation coherent state (DMCS) protocol. Specifically, since the Faraday mirror rotates the polarization direction of light by 90 o , the birefringence effect of light can be effectively dealt with, thus ensuring the same polarization state of light before and after reflection. The final simulation results show that the theoretical secret key rate of this scheme can reach 139 kbps at 70 km, which can further improve the stability and robustness of CV-QKD in the real environment, and provide technical support for the next-generation high-stability QKD system.
Exploring a context-gated network for effective image deraining

Authors:
raw_author_name: Pengpeng Li
raw_affiliation_string: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
raw_author_name: Shumin Fan
raw_affiliation_string: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
raw_author_name: Jiyu Jin
raw_affiliation_string: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
raw_author_name: Guiyue Jin
raw_affiliation_string: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
raw_author_name: Lei Fan
raw_affiliation_string: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China

Abstract: The existing deraining methods have obtained noteworthy improvements, but it is a challenging problem to extend the methods for complicated rain conditions where rain streaks exhibit different distribution densities, sizes, shapes, etc. The main challenges are the ability to fully explore and utilize the multi-scale context information of rain streaks that maintain both global structure completeness and local detail accurateness. To this end, this paper proposes an Exploring Context-Gated Network, known as ECGNet. To adequately explore the richer context information, the proposed method consists of two key elements: context-enhanced feature block (CEFB) and multi-scale-gated aggregation block (MGAB). Specifically, the various scale features can be captured by CEFB with the multi-scale operation, to better remove the rain streaks and effectively restore the local detail textures. Subsequently, the captured features from different spaces are sent to MGAB, to aggregate and transmit these different scale features from the encoder to the decoder and reduce the transmission loss of information. Massive experiments on the commonly used benchmarks have demonstrated that the proposed method obtains more appealing performances against other competitive methods.
Reality Technologies (AR, VR, MR, XR) in Tourism

Authors:
raw_author_name: Sarp Tahsin Kumlu
raw_author_name: Emrah Özkul

Abstract: The change in the technological environment within the macro-environment factors in recent years affects states, businesses, societies and individuals and concerns not only technology-based sectors but also many fields. In particular, trends such as artificial intelligence, metaverse, robotics, advanced connectivity, the Internet of Things, big data, small data, blockchain, cloud technologies and reality technologies, which are called new technology, are developing very quickly compared to the past and expanding their global usage areas. Creating strategies and policies without considering these factors creates problems in many areas. These problems are marketing, competition, cost, efficiency and productivity.Reality technologies, which are the research area in this chapter and enable users to interact with the digital world, have a wide application area in the tourism industry. With technological tools such as smartphones and virtual reality (VR) glasses; personalisation, interactive experience, information gathering and decision-making; many different solutions are produced in areas such as education, service and security. Along with its many advantages, the disadvantages of reality technologies and the negative outputs of this transformation are significant for the understanding and future of the subject.
Voice Control and Voice Searching

Authors:
raw_author_name: Seden Dogan

Abstract: Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.
Low-Order Automatic Domain Splitting Approach for Nonlinear Uncertainty Mapping

Authors:
raw_author_name: Matteo Losacco
raw_affiliation_string: Postdoctoral researcher, DCAS,10 Av. Edouard Belin, Toulouse, France
raw_author_name: Roberto Armellin
raw_affiliation_string:  PhD candidate, DCAS,10 Av. Edouard Belin, Toulouse, France.;  University of Auckland, Auckland, 1010, New Zealand; ISAE-SUPAERO, Toulouse, 31400, France

Abstract: This paper introduces a novel method for the automatic detection and handling of nonlinearities in a generic transformation. A nonlinearity index that exploits second-order Taylor expansions and polynomial bounding techniques is first introduced to estimate the Jacobian variation of a nonlinear transformation. This index is then embedded into a low-order automatic domain splitting algorithm that accurately describes the mapping of an initial uncertainty set through a generic nonlinear transformation by splitting the domain whenever nonlinearities grow above a predefined threshold. The algorithm is illustrated in the critical case of orbital uncertainty propagation, and it is coupled with a tailored merging process that limits the growth of the domains in time by recombining them when nonlinearities decrease. The low-order automatic domain splitting algorithm is then combined with Gaussian mixture models to accurately describe the propagation of a probability density function.
Visual Observer-Based State-Feedback Autonomous Motion Control on SE(3)

Authors:
raw_author_name: Tong Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario, N9B3P4, Canada
raw_author_name: Xiang Chen
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario, N9B3P4, Canada

Abstract: This paper introduces a visual observer-based state-feedback control tailored for autonomous motion on SE(3) to address challenges posed by modeling uncertainties and measurement noise. The proposed approach unfolds in two fundamental phases. In the initial stage, the visual observer, based on modeling information, visual sensors, and pre-deployed landmarks, along with the state-feedback controller, are developed independently, underpinning their individual semi-global practical asymptotic (SPA) stability. This modular approach ensures a robust foundation for the subsequent synthesis. The latter stage applies the well-established Small Gain Principle to regulate observer and controller parameters to guarantee the SPA stability of the closed-loop system with the visual-observer based state feedback control. The effectiveness of the proposed method is validated through simulation and experiments.