Reviewerly/exHarmony huggingface dataset:
Transport map Bayesian parameter estimation for dynamical systems

Abstract Accurate online state and parameter estimation of uncertain non‐linear dynamical systems is a demanding task that has been traditionally handled by adopting non‐linear Kalman Filters or particle filters. However, in case of Kalman filters the system needs to be linearised and for particle filters the computational demand can be high. Recent advances in optimal transport theory and the application to Bayesian model updating pave the way for other approaches to system and parameter identification. They also provide a way of formulating the problem in such a way that efficient online estimation for complex systems is possible. In this work, we investigate the properties of the transport map approach when compared to standard Markov Chain Monte Carlo in an off‐line setting as a first step towards on‐line parameter estimation. We apply both approaches to an analytical exponential model and a dynamical system with seven unknown parameters subjected to ground displacement. Details on the theory of transport maps and on the used MCMC algorithm are also given.
The application of artificial intelligence techniques in credit card fraud detection: a quantitative study

Credit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the application of artificial intelligence techniques as a fraud detection mechanism that can effectively and efficiently detect credit card fraud and identify fraudulent financial transactions. The data was acquired from 100 respondents across the accounting and finance industry and analysed using SPSS. Researcher analysed the data using regression analysis, Pearson correlation coefficient, and reliability analysis. Findings revealed that the three artificial intelligence techniques machine learning, data mining, and fuzzy logic have a significant positive relationship with credit card fraud detection. However, fuzzy logic was discovered to be the least utilized by experts due to its low accuracy/precision in comparison with machine learning and data mining. Based on these findings, our study concludes that the application of artificial intelligence techniques provides experts with better accuracy and efficiency in detecting fraudulent transactions. Therefore, it is recommended that fraud examiners, auditors, accountants, bankers, and organizations should implement and apply artificial intelligence techniques in order to spot anomalies faster and identify fraudulent financial transactions effectively and efficiently.
Micro-expression Recognition Based on DCBAM-EfficientNet Model

Abstract To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression features to be highlighted, and the CBAM attention is improved into a DCBAM model, where the large convolution kernel in the spatial attention module of CBAM is replaced by a dilated convolution with the same receptive field, reducing the network parameters while better preserving the spatial features of the image. The integration of the DCBAM model into the main structure of EfficientNet enables better integration of contextual information. Data enhancement is used to process the micro-expression dataset to decrease the occurrence of overfitting and improve the generalization ability of the model. The results demonstrate that the optimized model DCBAM-EfficientNet can effectively promote the recognition accuracy of micro-expressions, significantly reduce the quantity and volume of model parameters, and provide a reference for the deployment of mobile micro-expression recognition models.
Consistency enhancement of model prediction on document-level named entity recognition

Biomedical named entity recognition (NER) plays a crucial role in extracting information from documents in biomedical applications. However, many of these applications require NER models to operate at a document level, rather than just a sentence level. This presents a challenge, as the extension from a sentence model to a document model is not always straightforward. Despite the existence of document NER models that are able to make consistent predictions, they still fall short of meeting the expectations of researchers and practitioners in the field. To address this issue, we have undertaken an investigation into the underlying causes of inconsistent predictions. Our research has led us to believe that the use of adjectives and prepositions within entities may be contributing to low label consistency. In this article, we present our method, ConNER, to enhance a label consistency of modifiers such as adjectives and prepositions. By refining the labels of these modifiers, ConNER is able to improve representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets. On three datasets, we achieve a higher F1 score than the previous state-of-the-art model. Our method shows its efficacy on two datasets, resulting in 7.5%-8.6% absolute improvements in the F1 score. Our findings suggest that our ConNER method is effective on datasets with intrinsically low label consistency. Through qualitative analysis, we demonstrate how our approach helps the NER model generate more consistent predictions.Our code and resources are available at https://github.com/dmis-lab/ConNER/.
Federated Learning in Intelligent Traffic Management System

The data generated by the intelligent traffic management system (ITMS) is large containing videos and images of the traffic. The machine learning (ML) based ITMS requires the transmission of all the generated data to the centralized server which involves the transmission cost and time. The drawbacks of these kinds of systems are expensive communication, heterogeneous devices and location, and privacy user. Federated Learning (FL) is the technique that provides the solution to deal with these challenges. Focusing on the use of FL in ITMS, this paper includes a brief overview of FL. This also includes the discussion on the ITMS application for which FL can provide an efficient and better solution. A brief discussion on the benefits of FL in ITMS is included followed by the conclusion.
Guarding Against Bots with Art: NST-based Deep Learning Approach for CAPTCHA Verification

CAPTCHAs have become less effective in preventing automated bots from accessing internet services, owing to the increasing advancements in computer vision and machine learning. To overcome this challenge, this paper proposes a novel approach using Neural Style Transfer (NST) to generate complex and secure CAPTCHAs using VGG-16. Our approach produces visually challenging CAPTCHAs that are difficult for bots to identify and understand, thereby enhancing the security of internet services. The proposed approach utilizes a grid-like design, with 9 randomly selected images that are stylized using Neural Style Transfer, where the user is prompted to choose pictures that are visually similar to the separate images displayed. This not only makes the CAPTCHA more secure but also more user-friendly. We provide implementation details of our approach, including training and validation, and present experimental data demonstrating its efficacy in thwarting automated attacks. The proposed CAPTCHA system is not susceptible to similarity based attacks. The similarity index results returned the following values, MSE: 12934.04 RMSE:113.72 PSNR: 7.013. Our solution has the potential to provide internet services with higher levels of security against automated bots.
MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images

Ship detection in aerial images remains an active yet challenging task due to its arbitrary object orientation and various aspect ratios from the bird’s-eye perspective. Most existing oriented objection detection methods rely on angular prediction or predefined anchor boxes, making these methods highly sensitive to unstable angular regression and excessive hyper-parameter setting. To address these issues, we replace the angular-based object encoding with an anchor-and-angle-free paradigm, and propose a novel detector deploying a center and four midpoints for encoding each oriented object, namely MidNet. Moreover, MidNet designs a novel symmetrical deformable convolution for enhanceing the features of midpoints, then the center and midpoints for an identical ship are adaptively matched by predicting corresponding centripetal shift and matching radius. Finally, a concise analytical geometry algorithm is proposed to calculate the ship orientation and refine the keypoints step-wisely for building precise oriented bounding boxes. On two public ship detection datasets, HRSC2016 and FGSD2021, MidNet outperforms the state-of-the-art detectors by achieving APs of 90.52% and 86.50%.
Predicting Aging-Related Bugs Using Network Analysis on Aging-Related Dependency Networks

Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and removal before software release, ARB prediction was proposed. Based on the prediction results, software teams can allocate limited testing resources to ARB-prone modules. Previous research has proposed several methods for both within-project and cross-project ARB prediction. However, they are based on the same set of metrics focusing on the contents of a single module, and only six metrics are aging-related. In this paper, we develop aging-related network measures by constructing an aging-related dependency network to model the flow of aging-related information in the software. Our evaluation on three commonly used open-source projects reveals that aging-related network measures show an inconsistent association with ARB-proneness in three projects, and the performance of aging-related network measures varies under different ARB prediction settings.
An effective heterogeneous information network representation learning framework

Given the heterogeneity of real-world networks and the low efficiency of directly mining networks, heterogeneous information network (HIN) representation learning, which learns low-dimensional embeddings of nodes to represent various structural and semantic information in HINs, becomes a very crucial topic. Existing HIN based representation learning methods either omit the global information of networks, or only consider the first-order neighbors of each node. The two problems reflect the insufficient extraction and exploitation of network information for HIN embedding. To address these problems, we propose a novel Heterogeneous Graph SAmple and aggreGatE framework, named HGSAGE, for learning low-dimensional similarity-preserved embeddings of nodes in HINs. This framework consists of three mechanisms, i.e., the gravity model based on the meta-path reachable graphs mechanism to capture the global information of HINs, the node-level sampling and aggregating mechanism to sample and incorporate features from immediate and mediate neighbors of nodes, and the semantic-level aggregating mechanism to combine embeddings with respect to different meta-paths. Extensive experiments on three real-world heterogeneous networks of different types and scales for multiple tasks show that the proposed framework significantly outperforms the baselines. Moreover, HGSAGE has important application values in this research field.
Security Comparison of Blockchain and Cloud-based Identity Management: Considering the Scalability Problem

Identity management (IdM) is used in every facet of our electronic society. The need for IdM and the problems these systems face grow as our electronic identities grow and become more complex. When IdMS is concerned with identifying and controlling the individual and access controls in the system, IdMS faces a new level of complexity as the number of users increases, making these systems vulnerable to various threats and attacks. However, while the concepts of attacker and target are well established in the field of information security, when it comes to privacy, these two concepts can be very limited. Personal information can be used for various purposes, and it is not always the scenario that the privacy threat comes from what is considered an attacker from the beginning. Here comes the Cloud and Blockchain 's role in providing more secure and scalable IdMS. This study aims to cover existing solutions by describing their level of security through the risk assessment process to provide a concise picture of the differences.
Readability Classification with Wikipedia Data and All-MiniLM Embeddings

Evaluating the readability of text has been a critical step in several applications, ranging from text simplification, learning new languages, providing school children with appropriate reading material to conveying important medical information in an easily understandable way. A lot of research has been dedicated to evaluating readability on larger bodies of texts, like articles and paragraphs, but the application on single sentences has received less attention. In this paper, we explore several machine learning techniques - logistic regression, random forest, Naive Bayes, KNN, MLP, XGBoost - on a corpus of sentences from the English and simple English Wikipedia. We build and compare a series of binary readability classifiers using extracted features as well as generated all-MiniLM-L6-v2-based embeddings, and evaluate them against standard classification evaluation metrics. To the authors' knowledge, this is the first time this sentence transformer is used in the task of readability assessment. Overall, we found that the MLP models, with and without embeddings, as well as the Random Forest, outperformed the other machine learning algorithms.
Pragmatics in argumentation research

In this editorial, I give a brief review of the relationship between pragmatics and argumentation, and, in particular, of the importance of pragmatic concepts in theoretical argumentation research. I discuss some of the authors currently working on arguments and the ways in which pragmatics has influenced their work, as well as describing the work of research groups combining linguistics and argumentation, and listing some of the many recent publications dedicated to this interface. I then introduce the five research papers which make up this virtual special issue and give a short summary of their main findings. I conclude with some observations on the importance of the cooperation between scholars in the respective fields and mention certain areas where cooperation promises to be especially fruitful.
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms

Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution images but also achieve image compression at a limited bit rate. The image compression ratio reaches 2.03%; the structural difference value is higher than 0.91, and our average detection speed reaches 13.62 fps. Furthermore, we have packaged our model as a functional package of the ROS operating system, which can be easily used in actual robotic arm operations. Our solution can be fully applied to other work of robots to promote the development of the field of robotics.
Sweepstakes or Strategy?

This chapter demonstrates that the Webometrics ranking of universities in Kenya is an inevitable product of strategic actions and not luck. The chapter specifically demystifies the Webometrics approach to ranking universities; analyses the trends of Webometrics ranking of Kenyan universities since 2013 when the old and new universities were chartered following the enactment of the Universities Act (2012); examines the extent to which stakeholders rely on Webometrics ranking to select universities to join, fund or collaborate with; and proposes strategic actions universities in Kenya can take to enhance their Webometrics ranking. The chapter relies on empirical research data collected from scholars, students and policymakers in Kenya collected through in-depth key informant interviews. Documentary analysis was used to collect additional data on the ranking of universities. This chapter contributes new knowledge to the understanding and application of the philosophy, approach and efficacy of Webometrics ranking of universities to enhance the scholarly performance of universities.
“It’s like the Wild West”: User Experience (UX) Designers on Ethics and Privacy in Aotearoa New Zealand

The degree to which User Experience (UX) designers unfairly steer users’ behavior through the use of “dark patterns” is a topical and contentious issue. Scholarship has largely assumed that designers are complicit in manipulating the user and undermining their privacy. In this paper, we investigate privacy dark patterns and report on interviews conducted with UX practitioners, describing three findings: (a) designers feel motivated to act ethically due to their “moral compasses”; (b) designers are restricted in their ability to act ethically due to commercial pressures and a limited purview of the project; (c) designers’ understanding of the ethics of their practice do not currently match determinations made by international privacy and design scholars and demonstrate a limited understanding of how user behavior can be shaped. We conclude by outlining the benefits of independent regulation and progressive ethics education in UX.
Predicting the Application of Internet of Things (IoT) a New Wave of Technology for Smart Health Care

Healthcare is a fundamental aspect of human life. Furthermore, receiving high-quality medical care is highly essential. India is now facing various healthcare challenges due to a lack of resources. To define the concept of “smart healthcare,” this study discusses about the primary technologies before describing how it is presently being implemented across several key industries. Then, this study discusses about the challenges currently faced by smart healthcare. Finally, the future possibilities for smart healthcare are examined. The objective of this study is to comprehend the utilization of the Internet of Things, a new wave technology, to deal with healthcare related challenges. Finally, different IoT-based healthcare monitoring systems are comparatively analyzed. The limitations of IoT in healthcare, such as authentication, security, and wearability are examined, and ideas for future methodological approaches are presented.
Improved Traffic Sign Detection Algorithm for YOLOv5s

Traffic sign detection is an integral part of advanced assisted driving systems and intelligent transportation systems. Based on the TT100K dataset, this paper conducts a study on the improved YOLOv5s traffic sign detection algorithm. Firstly, to address the problem of low detection accuracy due to unbalanced sample size among different categories in the TT100K dataset, the replacement method, the paste method and the conventional data expansion method were successively used on the dataset, resulting in an increase in traffic sign detection accuracy from 80.9% to 91.2%. In order to further improve the application value of the traffic sign detection algorithm, this paper reduces the downsampling layer of the backbone in YOLOv5s to obtain a detection head with higher resolution, thus getting the model YOLOv5s-strong, whose detection accuracy is 4.9% higher than YOLOv5s. Even surpasses the detection accuracy and inference time of YOLOv7 with less number of parameters and calculations
ExPLoit: Extracting Private Labels in Split Learning

Split learning is a popular technique used to perform vertical federated learning, where the goal is to jointly train a model on the private input and label data held by two parties. To preserve privacy of the input and label data, this technique uses a split model trained end-to-end, by exchanging the intermediate representations (IR) of the inputs and gradients of the IR between the two parties. We propose ExPLoit - a label-leakage attack that allows an adversarial input-owner to extract the private labels of the label-owner during split-learning. ExPLoit frames the attack as a supervised learning problem by using a novel loss function that combines gradient-matching and several regularization terms developed using key properties of the dataset and models. Our evaluations on a binary conversion prediction task and several multi-class image classification tasks show that ExPLoit can uncover the private labels with near-perfect accuracy of up to 99.53%, demonstrating that split learning provides negligible privacy benefits to the label owner. Furthermore, we evaluate the use of gradient noise as a defense and show that the protection against our attack comes at the cost of a significant loss in model utility. Our findings underscore the need for better privacy-preserving training techniques for vertically split data.
Enhancement of Round Robin Algorithm with Dynamic Quantum in Cloud Computing

Load Balancing is the process of distributing the computing load equally among the available resources which are pooled. With the rapidly advancing information technology industry, load balancing becomes a necessity. The load balancer equally distributes the load between the available resources, the lack of load balancing makes the single resources to bare huge amounts of requests. It also makes the resources highly available for the users. Load Balancing is used in both on-premises and off-premises environments. The load balancing mainly depends on a certain algorithm for the operation. There are many load-balancing algorithms used. The most widely used algorithm is the load balancing algorithm is Round Robin. Each task in the Round-Robin Algorithm is carried out based on the time quantum. Although widely used, the Round Robin algorithm has some issues that are trying to solve using this research study. Some of the major issues focused here are the lack of dynamic time quantum allocation and more context switching and not prioritizing tasks. This research study initially reviews the cloud computing and its deployment strategies. Then, this research study presents a complete overview on the importance of load balancing, the most widely used load balancing algorithm with cloud environment, and its shortcomings. Finally, a research idea has been proposed for the enhancement of Round Robin load balancing algorithm.