Reviewerly/exHarmony huggingface dataset:
An efficient feature pyramid attention network for person re-identification

Authors:
raw_author_name: Qian Luo
raw_affiliation_string: The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China; Xihua University, Chengdu 610039, China
raw_author_name: Jie Shao
raw_affiliation_string: Xihua University, Chengdu 610039, China
raw_author_name: Wanli Dang
raw_affiliation_string: Civil Aviation Electronic Technology Co., Ltd, Chengdu 611430, China; The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China
raw_author_name: Chao Wang
raw_affiliation_string: Civil Aviation Electronic Technology Co., Ltd, Chengdu 611430, China; The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China
raw_author_name: Libo Cao
raw_affiliation_string: Civil Aviation Electronic Technology Co., Ltd, Chengdu 611430, China; The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China
raw_author_name: Tao Zhang
raw_affiliation_string: Civil Aviation Electronic Technology Co., Ltd, Chengdu 611430, China; The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China

Abstract: For person re-identification, occlusion, appearance similarity and background clutter have always been challenges. In order to effectively address the challenges, we propose an efficient feature pyramid attention network (FPA-Net), which combines visual features from different levels to focus on both detail features and information. Specifically, we embed a pair of attention mechanisms that complement each other in the backbone network to focus on the discriminant features of person areas. In addition, we designed a novel feature pyramid structure, which propagates the feature information from the cross-level through the top feature to the bottom feature and from the bottom feature to the top feature to supplement the detail information of the feature. Finally, we integrate features form different scales through a lightweight transition block to generate more discriminant features. Our method performed experimental analysis on four datasets: Market-1501, DukeMTMC-ReID, CUHK03(Detected) and MSMT17. A large number of experimental results prove that the performance of the method is significantly better than the existing state-of-the-art methods.
Adopting the metaverse in the luxury hotel business: a cost–benefit perspective

Authors:
raw_author_name: Mustafeed Zaman
raw_author_name: Riad Shams
raw_author_name: K. Mohamed Jasim

Abstract: Purpose This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations. Design/methodology/approach An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed. Findings The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel). Practical implications Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse. Originality/value This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.
Crossing the Andes: Challenges and opportunities for digital pathology in Latin America

Authors:
raw_author_name: Emilio A.C.P. Assis
raw_affiliation_string: CIDAP, Juiz de Fora, Minas Gerais, Brazil
raw_author_name: Leonard Medeiros da Silva
raw_affiliation_string: Grupo Oncoclínicas, São Paulo, SP, Brazil
raw_author_name: Mauro Saieg
raw_affiliation_string: Grupo Fleury, São Paulo, Brazil; Santa Casa Medical School, São Paulo, SP, Brazil

Abstract: The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.
Software bug localization based on optimized and ensembled deep learning models

Authors:
raw_author_name: Waqas Ali
raw_affiliation_string: School of Information Engineering Yangzhou University Yangzhou China
raw_author_name: Lili Bo
raw_affiliation_string: School of Information Engineering Yangzhou University Yangzhou China; Yunnan Key Laboratory of Software Engineering Kunming Yunnan China
raw_author_name: Xiaobing Sun
raw_affiliation_string: School of Information Engineering Yangzhou University Yangzhou China
raw_author_name: Xiaoxue Wu
raw_affiliation_string: School of Information Engineering Yangzhou University Yangzhou China
raw_author_name: Aakash Ali
raw_affiliation_string: Quaid-e-Awam University of Engineering Science and Technology Nawabshah Nawabshah Pakistan
raw_author_name: Ying Wei
raw_affiliation_string: School of Information Engineering Yangzhou University Yangzhou China

Abstract: Abstract An automated task for finding the essential buggy files among software projects with the help of a given bug report is termed bug localization. The conventional approaches suffer from the challenges of performing lexical matching. Particularly, the terms utilized for describing the bugs in the bug reports are observed to be irrelevant to the terms used in the source code files. To resolve these problems, we propose an optimized and ensemble deep learning model for software bug localization. These features are reduced by the principle component analysis (PCA). Then, they are selected by the weighted convolutional neural network (CNN) model with the support of the Modified Scatter Probability‐based Coyote Optimization Algorithm (MSP‐COA). Finally, the optimal features are subjected to the ensemble deep neural network and long short‐term memory (DNN‐LSTM), with parameter tuning by the MSP‐COA. Experimental results show that the proposed approach can achieve higher bug localization accuracy than individual models.
DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

Authors:
raw_author_name: Jie Qi
raw_affiliation_string: School of Electronic Science and Engineering, Xiamen University, Xiamen, 361005, China
raw_author_name: Shaohua Hong
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, 361005, China

Abstract: During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at smaller number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm is proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose a pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.
Predicting pragmatic functions of Chinese echo questions using prosody: evidence from acoustic analysis and data modeling

Authors:
raw_author_name: Siyi Cao
raw_affiliation_string: School of Foreign Languages, Southeast University, China
raw_author_name: Yizhong Xu
raw_affiliation_string: College of Foreign Languages, Nanjing University of Aeronautics and Astronautics, China
raw_author_name: Anqi Wu

Abstract: Echo questions serve two pragmatic functions (recapitulatory and explicatory) and are subdivided into two types (yes-no echo question and wh-echo question) in verbal communication. Yet to date, most relevant studies have been conducted in European languages like English and Spanish. It remains unknown whether the different functions of echo questions can be conveyed via prosody in spoken Chinese. Additionally, no comparison was made on the diversified algorithmic models in predicting functions by the prosodity of Chinese echo questions, a novel linguistic cognition in nature. This motivated us to use different acoustic cues to predict different pragmatic functions of Chinese echo questions by virtue of acoustic experiment and data modeling. The results showed that for yes-no echo question, explicatory function exhibited higher pitch and intensity patterns than recapitulatory function whereas for wh-echo question, recapitulatory function demonstrated higher pitch and intensity patterns than explicatory function. With regard to data modeling, the algorithm Support Vector Machine (SVM) relative to Random Forest (RF) and Logistic Regression (LR) performed better when predicting different functions using prosodic cues in both yes-no and wh-echo questions. This study from a digitized perspective adds evidence to the cognition of echo questions’ functions on a prosodic basis.
<scp>CerviFormer</scp>: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Authors:
raw_author_name: Bhaswati Singha Deo
raw_affiliation_string: Center for Lasers and Photonics, Indian Institute of Technology Kanpur Kanpur India
raw_author_name: Mayukha Pal
raw_affiliation_string: ABB Ability Innovation Center, Asea Brown Boveri Company Hyderabad India
raw_author_name: Prasanta K. Panigrahi
raw_affiliation_string: Centre for Quantum Science and Technology Siksha ’O’ Anusandhan University  Bhubaneswar Odisha India; Department of Physical Sciences Indian Institute of Science Education and Research Kolkata, Mohanpur Nadia India
raw_author_name: Asima Pradhan
raw_affiliation_string: Center for Lasers and Photonics, Indian Institute of Technology Kanpur Kanpur India

Abstract: Abstract Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer‐a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross‐attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large‐scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3‐state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2‐state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process.
Comprehensive evaluation of intrinsic safety of railway facilities and equipment based on improved cloud model-fuzzy Petri net

Authors:
raw_author_name: Junting Lin
raw_affiliation_string: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China; The Centre of National Railway Intelligent Transportation System Engineering and Technology, Beijing, China
raw_author_name: Yang Wang
raw_affiliation_string: Railway Safety Research Centre, China State Railway Group Co. Ltd, Beijing, China

Abstract: In order to achieve the goal of transforming China into a modernized railway powerhouse, a comprehensive evaluation model of intrinsic safety is constructed in this paper by focussing on the intrinsic safety of railway facilities and equipment. Firstly, the index system for evaluating the intrinsic safety of railway facilities and equipment is divided. Secondly, the fuzzy Petri net (FPN) model for intrinsic safety evaluation is constructed according to the fuzzy generative rules, and the similarity calculation method of the cloud model based on shape and distance is applied to derive the initial state value of the FPN model and carry out the calculation of fuzzy reasoning of the intrinsic safety. Finally, the comprehensive evaluation of intrinsic safety is carried out using the example of railroad facilities and equipment at one China Railway Group bureau’s station section. The results indicate that the intrinsic safety level of the station section falls into Class IV intrinsic safety, exhibiting a few vulnerabilities. However, the risk level is low, aligning with the findings of the actual research. Moreover, the evaluation results based on the FPN, the cloud model’s similarity calculation method and the suggested method are compared and analysed with the findings of the actual research. The comparative results demonstrate the accuracy and scientificity of the proposed method, providing a reference for enhancing the intrinsic safety of railway facilities and equipment.
SR-TTS: a rhyme-based end-to-end speech synthesis system

Authors:
raw_author_name: Tao Liang
raw_affiliation_string: The Department of Biomedical Engineering, Nanjing Medical University, China
raw_author_name: Rui Feng
raw_affiliation_string: The Department of Biomedical Engineering, Nanjing Medical University, China
raw_author_name: Keke Shi
raw_affiliation_string: The Department of Biomedical Engineering, Nanjing Medical University, China
raw_author_name: Junxiao Yu
raw_affiliation_string: Changzhou Medical Center, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Nanjing Medical University, China; Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, China
raw_author_name: Wei Wang
raw_affiliation_string: The Department of Biomedical Engineering, Nanjing Medical University, China
raw_author_name: Jianqing Li
raw_affiliation_string: The Department of Biomedical Engineering, Nanjing Medical University, China

Abstract: Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like human-computer interaction. However, conventional TTS models still face challenges, as the synthesized speeches often lack naturalness and expressiveness. Additionally, the slow inference speed, reflecting low efficiency, contributes to the reduced voice quality. This paper introduces SynthRhythm-TTS (SR-TTS), an optimized Transformer-based structure designed to enhance synthesized speech. SR-TTS not only improves phonological quality and naturalness but also accelerates the speech generation process, thereby increasing inference efficiency. SR-TTS contains an encoder, a rhythm coordinator, and a decoder. In particular, a pre-duration predictor within the cadence coordinator and a self-attention-based feature predictor work together to enhance the naturalness and articulatory accuracy of speech. In addition, the introduction of causal convolution enhances the consistency of the time series. The cross-linguistic capability of SR-TTS is validated by training it on both English and Chinese corpora. Human evaluation shows that SR-TTS outperforms existing techniques in terms of speech quality and naturalness of expression. This technology is particularly suitable for applications that require high-quality natural speech, such as intelligent assistants, speech synthesized podcasts, and human-computer interaction.
An anomaly affected discrete LTI systems: a moving horizon approach for estimating position and temperature measurements

Authors:
raw_author_name: Anees Fathima Bashir
raw_affiliation_string: Department of Electronics &amp; Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
raw_author_name: M. P. Flower Queen
raw_affiliation_string: Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India

Abstract: A real-time system may be subjected to various anomalies that can affect the quality of the observations. The main motivation of the article arises from the need in addressing challenges posed by the presence of anomalies in discrete linear time-invariant (LTI) systems with a focus on the estimation processes, in the context of position and temperature measurements. The proposed approach leverages the properties of discrete LTI systems and takes advantage of the predictive capabilities of the moving horizon strategy (MHS). It operates recursively updating estimates of new measurement while fairly considering its past estimates that occur within the window of the moving horizon. The estimation framework will be designed to handle disturbances and provide robust estimates, to ensure the effectiveness of the system. In order to validate the proposed approach simulation studies were conducted on different and only scenarios in different order LTI system. Comparative studies with different estimation techniques demonstrate the capability of the proposed approach in terms of performance and efficiency. The proposed approach can be applied to systems with changing system dynamics. Future research may be conducted to utilize this strategy in other domains to mitigate anomalies while enhancing performance.
Algorithm design of a combinatorial mathematical model for computer random signals

Authors:
raw_author_name: Benhua Qiu
raw_affiliation_string: Department of Basic Courses, Zhengzhou University of Science and Technology, Zhengzhou, Henan, China

Abstract: To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to its fuzzy function (FF) and the parameters of conventional FHSs are estimated according to the time-frequency distribution corresponding to the KF. Then, simulation experiments are carried out in different types of interference noise environments. The proposed combinatorial mathematical model for computer random signals shows a practical impact, and can effectively improve the effect of random signal combination.
Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm

Authors:
raw_author_name: Lingli Yu
raw_affiliation_string: School of Automation, Central South University, Changsha 410083, China
raw_author_name: Keyi Li
raw_affiliation_string: School of Automation, Central South University, Changsha 410083, China

Abstract: Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments.