Reviewerly/exHarmony huggingface dataset:
Pronoun interpretation in Italian

Authors:
raw_author_name: Natália Brambatti Guzzo
raw_affiliation_string: Université Laval

Abstract: Abstract We explore potential effects of prosody on pronoun interpretation in Italian, building on previous research which has shown that second language learners/users (L2ers) assign non-target interpretations to overt pronouns. We investigate effects of contrastive stress and pause, proposing that these will result in changes to default antecedent preferences for overt and null pronouns, for L2ers and for native speakers. An experiment was conducted, involving English-speaking L2ers of Italian and Italian native speakers. Participants were presented with auditory stimuli like Lorenzo ha scritto a Roberto quando Ø/lui si è trasferito a Torino ‘Lorenzo wrote to Roberto when (he) moved to Turin’ and indicated their preferred antecedent for the pronoun. Overt versus null pronouns, presence versus absence of stress on overt pronouns, and presence versus absence of pause between clauses were manipulated. The results yielded significant differences for antecedent choices between null and overt pronouns, consistent with earlier literature. In addition, stress was significant for both groups. Implications of a prosodic approach to ambiguity resolution are discussed.
A Literature Survey on Online Voting System using Face Recognition

Authors:
raw_author_name: None Chethan K
raw_affiliation_string: Global Academy of Technology, Bengaluru, India
raw_author_name: None Sachin Kumar A
raw_affiliation_string: Global Academy of Technology, Bengaluru, India

Abstract: This paper presents a creative technique for overseeing modernizing the larger part rule cycle through the mix of face certification improvement into an internet projecting a surveying structure framework. By utilizing facial certification calculations, occupants can safely certify their characters from a decent ways, guaranteeing the uprightness and security of the discretionary association. The proposed structure means to overhaul straightforwardness, accuracy, and straightforwardness in fair strategies, tending to stresses related with character check and fake exercises. Through thorough testing and underwriting, this plan offers a promising development for driving the capacity and reliability of web based projecting a surveying structure systems in contemporary social orders
Exploiting PlanetScope Imagery for Volcanic Deposits Mapping

Authors:
raw_author_name: Gaetana Ganci
raw_affiliation_string: Istituto Nazionale Geofisica e Vulcanologia, Sezione di Catania-Osservatorio Etneo, Piazza Roma 2, 95125 Catania, Italy
raw_author_name: Simona Scollo
raw_affiliation_string: Istituto Nazionale Geofisica e Vulcanologia, Sezione di Catania-Osservatorio Etneo, Piazza Roma 2, 95125 Catania, Italy

Abstract: During explosive eruptions, tephra fallout represents one of the main volcanic hazards and can be extremely dangerous for air traffic, infrastructures, and human health. Here, we present a new technique aimed at identifying the area covered by tephra after an explosive event, based on processing PlanetScope imagery. We estimate the mean reflectance values of the visible (RGB) and near infrared (NIR) bands, analyzing pre- and post-eruptive data in specific areas and introducing a new index, which we call the ‘Tephra Fallout Index (TFI)’. We use the Google Earth Engine computing platform and define a threshold for the TFI of different eruptive events to distinguish the areas affected by the tephra fallout and quantify the surface coverage density. We apply our technique to the eruptive events occurring in 2021 at Mt. Etna (Italy), which mainly involved the eastern flank of the volcano, sometimes two or three times within a day, making field surveys difficult. Whenever possible, we compare our results with field data and find an optimal match. This work could have important implications for the identification and quantification of short-term volcanic hazard assessments in near real-time during a volcanic eruption, but also for the mapping of other hazardous events worldwide.
Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

Authors:
raw_author_name: Haodong Sun
raw_affiliation_string: College of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China
raw_author_name: Pengge Ma
raw_affiliation_string: College of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China
raw_author_name: Zhenghao Li
raw_affiliation_string: Key Laboratory of Digital Earth Science, Institute of Space Information, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhaoyi Ye
raw_affiliation_string: Jacobs School of Engineering, University of California, San Diego, San Diego, CA, United States

Abstract: This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm.
MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model

Authors:
raw_author_name: Michael Leong
raw_affiliation_string: Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA
raw_author_name: Awad Abdelhalim
raw_affiliation_string: Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA
raw_author_name: Jude Ha
raw_affiliation_string: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
raw_author_name: Gabriel L. Pincus
raw_affiliation_string: Washington Metropolitan Area Transit Authority, Washington, DC
raw_author_name: Anthony B. Harris
raw_affiliation_string: Washington Metropolitan Area Transit Authority, Washington, DC
raw_author_name: Michael Eichler
raw_affiliation_string: Washington Metropolitan Area Transit Authority, Washington, DC
raw_author_name: Jinhua Zhao
raw_affiliation_string: Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA

Abstract: Transit riders’ feedback provided in ridership surveys, customer relationship management (CRM) channels, and, in more recent times, through social media, is key for transit agencies to better gauge the efficacy of their services and initiatives. Getting a holistic understanding of riders’ experience through the feedback shared in those instruments is often challenging, mostly because of the open-ended, unstructured nature of text feedback. In this paper, we propose leveraging traditional transit CRM feedback to develop and deploy a transit-topic-aware large language model (LLM) capable of classifying open-ended text feedback to relevant transit-specific topics. First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority. We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM—MetRoBERTa—with classical machine learning approaches utilizing keyword-based and lexicon representations. Our model outperforms those methods across all evaluation metrics, providing an average topic classification accuracy of 90%. Finally, we provide a value proposition of this work demonstrating how the language model, alongside additional text processing tools, can be applied to add structure to open-ended text sources of feedback such as Twitter. The framework and results we present provide a pathway for an automated, generalizable approach for ingesting, visualizing, and reporting transit riders’ feedback at scale, enabling agencies to better understand and improve customer experience.
Quantum Poisson solver without arithmetic

Authors:
raw_author_name: Shengbin Wang
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Zhimin Wang
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Guolong Cui
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Shangshang Shi
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Ruimin Shang
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Jiaxin Li
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Wendong Li
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China
raw_author_name: Zhiqiang Wei
raw_affiliation_string: High Performance Computing Center, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237, China
raw_author_name: Yongjian Gu
raw_affiliation_string: Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

Abstract: Abstract Solving differential equations is one of the most promising applications of quantum computing. The Poisson equation has applications in various domains of physics and engineering, including the simulation of ocean current dynamics. Here, we propose an efficient quantum algorithm for solving the one-dimensional Poisson equation based on the controlled R y rotations. Our quantum Poisson solver (QPS) removes the need for expensive routines such as phase estimation, quantum arithmetic or Hamiltonian simulation. The computational cost of our QPS is 3 n in qubits and 5/3 n 3 in one- and two-qubit gates, where n is the logarithmic of the number of discrete points. An overwhelming reduction of the constant factors of the big-O complexity is achieved, which is critical to evaluate the practicality of implementing the algorithm on a quantum computer. In terms of the error ε , the complexity is log(1/ ε ) in qubits and poly(log(1/ ε )) in operations. The algorithms are demonstrated using a quantum virtual computing system, and the circuits are executed successfully on the IBM real quantum computers. The present QPS could exhibit a potential real-world application for solving differential equations on noisy intermediate-scale quantum (NISQ) devices.
Commonsense Knowledge in Foundation and Large Language Models

Authors:
raw_author_name: None Harsh Bhardwaj
raw_affiliation_string: Dronacharya College of Engineering, Gurugram, India

Abstract: The development and continuous expansion of the transformer deep-learning architecture have produced enormous effects across various domains, including but not limited to natural language processing. The power of deep learning models has sparked a fresh interest in commonsense knowledge, which has been aided by transformer-based language models. Most of the recent research has concentrated on delving into the commonsense already built into these models' pre-trained parameters and finding ways to fill in any gaps in commonsense utilizing knowledge graphs and fine-tuning. In order to broaden a limited commonsense knowledge network that was originally generated solely from visual data, we are building on the demonstrated linguistic understanding of extremely large transformer-based language models. Compared to language models that are fine-tuned on a huge starting corpus, few-shotprompted pre-trained models are able to acquire the context of an initial knowledge graph with less bias. It has also been demonstrated that these models can contribute novel ideas to the visual knowledge networkIt is a new development in the field of commonsense knowledge generation that, as far as we can tell, can lead to a fivefold decrease in cost when compared to the current state of the art. Fuzzy language names assigned to the produced triples are another addition. Applying knowledge graphs as a framework, the procedure is comprehensive. It implies that the triples are expressed in natural language, analyzed, and then added to the commonsense knowledge network as triples again.
Cloud-integrated cyber–physical systems: Reliability, performance and power consumption with shared-servers and parallelized services

Authors:
raw_author_name: Shuyi Ma
raw_affiliation_string: School of Management, Xi’an Jiaotong University, Xi’an, 710049, China
raw_author_name: Jin Li
raw_affiliation_string: School of Management, Xi’an Jiaotong University, Xi’an, 710049, China
raw_author_name: Jianping Li
raw_affiliation_string: School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
raw_author_name: Min Xie
raw_affiliation_string: Department of Systems Engineering, City University of Hong Kong, Hong Kong, China

Abstract: Abstract Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of visualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.
Class imbalance-sensitive approach based on PLMs for the detection of cyberbullying in English and Arabic datasets

Authors:
raw_author_name: Azzeddine Rachid Benaissa
raw_affiliation_string: ENSI, RIADI Laboratory, University of la Manouba, Manouba, Tunisia
raw_author_name: Azza Harbaoui
raw_affiliation_string: ENSI, RIADI Laboratory, University of la Manouba, Manouba, Tunisia
raw_author_name: Hajjami Henda Ben Ghezala
raw_affiliation_string: ENSI, RIADI Laboratory, University of la Manouba, Manouba, Tunisia

Abstract: Social Networking increases allowed the spreading of cyberbullying worldwide. The latter invaded cyberspace, kids and adolescents are no more safe in their virtual playgrounds. Indeed, online bullying is attracting considerable concern due to the societal and health issues it causes, ranging from depression, anxiety, and low self-esteem to sui cide attempts. Automatic cyberbullying detection is becoming a vital factor in protecting individuals' lives. It has received much attention in the last decade. Researchers use machine learning and deep learning models to detect online bullying content. An automatic cyberbullying detection model would flag any bullying text as efficiently as possible. Yet, several challenges lie ahead for the development of such a robust model. Our study discerned class imbalance and bullying text representation as being the major issues concerning cyberbullying classification. In this context, we tried to handle the class imbalance problem through data augmentation, cost-sensitive learning, and lever- aging a Computer Vision loss function for the task. Moreover, we consider a prominent solution for bullying content representation, which consists of fine-tuning Pre-trained Language Models for cyberbullying detection and using these latter as feature extractors for Multichannel ConvNets and Bidirectional LSTMs. The results show the effectiveness of the proposed models, which outperform several past works and provide high Recall values (78%–96%) on English and Arabic datasets.
Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

Authors:
raw_author_name: B Hemalatha
raw_affiliation_string: Bharath Institute of Higher Education and Research
raw_author_name: B Karthik
raw_affiliation_string: Bharath Institute of Higher Education and Research

Abstract: INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine.&#x0D; OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing.&#x0D; METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise.&#x0D; RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images.&#x0D; CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier. The entire system developed using python programming.
Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

Authors:
raw_author_name: Yunan Yao
raw_affiliation_string: School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

Abstract: (1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling.
Vision-Language Model for Generating Textual Descriptions From Clinical Images: Model Development and Validation Study

Authors:
raw_author_name: Yongshuai Hou
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China.
raw_author_name: Xinyu Chen
raw_affiliation_string: Harbin Institute of Technology, Shenzhen, China.
raw_author_name: Youcheng Pan
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China.
raw_author_name: Yang Xiang
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China.

Abstract: Background The automatic generation of radiology reports, which seeks to create a free-text description from a clinical radiograph, is emerging as a pivotal intersection between clinical medicine and artificial intelligence. Leveraging natural language processing technologies can accelerate report creation, enhancing health care quality and standardization. However, most existing studies have not yet fully tapped into the combined potential of advanced language and vision models. Objective The purpose of this study was to explore the integration of pretrained vision-language models into radiology report generation. This would enable the vision-language model to automatically convert clinical images into high-quality textual reports. Methods In our research, we introduced a radiology report generation model named ClinicalBLIP, building upon the foundational InstructBLIP model and refining it using clinical image-to-text data sets. A multistage fine-tuning approach via low-rank adaptation was proposed to deepen the semantic comprehension of the visual encoder and the large language model for clinical imagery. Furthermore, prior knowledge was integrated through prompt learning to enhance the precision of the reports generated. Experiments were conducted on both the IU X-RAY and MIMIC-CXR data sets, with ClinicalBLIP compared to several leading methods. Results Experimental results revealed that ClinicalBLIP obtained superior scores of 0.570/0.365 and 0.534/0.313 on the IU X-RAY/MIMIC-CXR test sets for the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluations, respectively. This performance notably surpasses that of existing state-of-the-art methods. Further evaluations confirmed the effectiveness of the multistage fine-tuning and the integration of prior information, leading to substantial improvements. Conclusions The proposed ClinicalBLIP model demonstrated robustness and effectiveness in enhancing clinical radiology report generation, suggesting significant promise for real-world clinical applications.