Reviewerly/exHarmony huggingface dataset:
Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates

Background Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. Objective Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. Methods We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. Results In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. Conclusions Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.
Talkin’ ‘Bout AI Generation: Copyright and the Generative AI Supply Chain

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Sentiment Analysis in Information Systems Research: Extending the Task-Technology Fit Theory

Sentiment analysis and related technologies including emotion detection and electronic word-of-mouth analysis have been increasingly used in information systems (IS) research as textual data becomes more available. Studies of such technologies from an algorithmic perspective have focused more on how the sentiment analysis methods and functions are implemented; whereas from a managerial perspective, the final representations that impart sentiment information for other downstream tasks, rather than how the representations are produced, are more important and are critical for the application success of such methods. In this article, we address the unawareness of the importance of sentiment representations and the arbitrary choices of them in current IS research. To solve the problem, we first propose a categorization of sentiment analysis methods, not based on implementation details, but based on five different types of final representations. Moreover, we propose a framework for selecting an appropriate sentiment analysis method for given IS tasks based on an extended interpretation of the task-technology fit (TTF) theory. The framework validity is supported by a comprehensive analysis of IS studies employing sentiment analysis. By investigating two misfitted cases, we show that our framework guidelines further add value to the analysis results. Our study shows that the connotation of "technology" in the TTF theory is not limited to traditional IT, but also extends to algorithms and AI models.
Cybercrime on the Ethereum Blockchain

We propose a taxonomy of cybercrime on the Ethereum blockchain and examine how cybercrime impacts victims' risk-taking and returns. Our difference-in-differences analysis of a sample of victims and matched non-victims suggests that victims increase their long-term total risk-taking and earn lower risk-adjusted returns in the post-cybercrime period. Victims' long-term total risk-taking increases because they increase diversifiable risk in the long term. The increased diversifiable risk correlates with victims' withdrawal from altcoins after cybercrime. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most-affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least-affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addresses' probability of being involved in cybercrime, both on the victim and the cybercriminal side.
Stealing Digital Assets: Evidence From Cryptocurrency Exchange Hacks

We show that ironically, despite the use of advanced cryptography, digital assets such as cryptocurrencies are highly susceptible to theft. Analyzing every known hack and theft from centralized cryptocurrency exchanges, we show that over $5 billion has been stolen from cryptocurrency exchanges (over $40 billion in current prices). In a typical year, thefts amount to about 0.71% of market capitalization – a much higher rate than in traditional assets and currencies. We find that investors respond to increased theft risk by shifting assets to private wallets and offline “cold storage” that is less susceptible to theft. Key-splitting technologies and non-custodial markets are two solutions to reduce the substantial costs associated with digital asset theft.
A Brief Review on Attribute-Based Encryption Approaches

Attribute-Based Encryption (ABE) has emerged as a promising cryptographic technique that enables fine-grained access control and secure data sharing in various applications. This paper presents a comprehensive review of attribute-based encryption approaches, focusing on their underlying principles, key features, and practical implementations. The paper begins by introducing the fundamental concepts and motivations behind attribute-based encryption. It explores the different types of attributes used in ABE, such as user attributes, data attributes, and policy attributes, and discusses their roles in defining access policies and enforcing data confidentiality. Next, the paper surveys the major categories of attributebased encryption schemes, including key-policy ABE (KP-ABE) and ciphertext-policy ABE (CP-ABE). It examines the strengths and limitations of each scheme, highlighting their suitability for different use cases and security requirements. Furthermore, the paper investigates the advancements in attribute-based encryption techniques, such as multi-authority ABE, fuzzy ABE, and outsourced ABE. It explores how these extensions address the challenges of scalability, expressiveness, and efficiency in attribute-based encryption systems. The review also discusses the security properties of attribute-based encryption, including confidentiality, integrity, and attribute privacy. It examines the potential vulnerabilities and attacks against ABE schemes and presents the countermeasures proposed in the literature. Moreover, the paper provides an overview of the practical applications of attribute-based encryption, including secure cloud storage, data sharing in healthcare systems, and access control in Internet of Things (IoT) environments. It discusses the benefits and challenges of deploying ABE in real-world scenarios and highlights the ongoing research efforts in this area. Finally, the paper concludes with a summary of the key findings and identifies the open research challenges and future directions in attribute-based encryption. It emphasizes the need for standardized frameworks, efficient implementations, and enhanced security models to promote the widespread adoption of ABE in various domains. Overall, this comprehensive review serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art attribute-based encryption approaches, their applications, and the potential avenues for further advancements in this field
Overcoming Limitations In Chat GPT And Wolfram Alpha With Peter Chew Method For Solution Of Triangle

This article discusses the limitations of Chat GPT and Wolfram Alpha specifically in terms of their restricted knowledge and their inability to answer some Solution of Triangle questions. Chat GPT's knowledge is confined to data up until 2021. However, these limitations can be addressed by incorporating future knowledge, Peter Chew Method for Solution of Triangle into Education 4.0 Calculator. This can be effective in let student interest in using technology while learning mathematics especially when analogous covid- 19 issues arise in the future.By utilizing Peter Chew Method into the Education 4.0 calculator, significant advancements can be achieved. This innovative tool allows us for finding the value of third side when given 2 sides and a non included angle. The integration of Education 4.0 provides a promising solution to the existing limitations in Chat GPT and Wolfram Alpha.By programming the Education 4.0 calculator with Peter Chew Method, these systems can enhance their capabilities and offer simple solution , more comprehensive and up-to-date answers to all triangle problem. This approach demonstrates the potential of leveraging advanced mathematical concepts to overcome the limitations posed by restricted knowledge in AI systems like Chat GPT and Wolfram Alpha.By incorporating Peter Chew Method and Rule for Solution of triangle and leveraging advanced mathematical tools, AI systems can expand their capabilities and offer simple solution, more accurate and comprehensive responses in the domain of Solution of Triangle.
Object detection algorithm based on mixed dilated convolution pyramid

To address the problem that FPN in the one-stage object detection algorithm loses a large amount of semantic information after upsampling and downsampling fusion, leading to difficulties in detecting small objects in dense scenes. This paper proposes a new FPN structure, Residual dilated FPN (RdFPN), compared to the traditional FPN, which uses multiple Dilated Block instead of the standard convolution in the FPN horizontal structure to mix the receptive fields of different layers in the feature map and expand the object size range of the feature map, while improving the network's ability to extract features at different scales. Experimental results show that RdFPN improves the mAP of the one-stage object detection algorithms RetinaNet, FCOS, and GFL by 0.9%, 0.7%, and 0.8% on the MS COCO dataset, enough to improve the overall performance of the detector.
Co-teaching module for multiple instance learning-based weakly supervised object detection network

Weakly-supervised object detection (WSOD) is the process of detecting instances with bounding boxes in an image using only image level labels. Multiple instance learning (MIL) based networks are one of the fundamental frameworks of WSOD methods. To convert object detection to MIL proposals classification, proposal methods such as selective search windows (SSW) interpret the image as a bag of proposals. While WSOD reduces the requirement for datasets, the lack of instance-level supervision makes it challenging to constrain the proposals, which poses a considerable challenge to both the training process and the final detection accuracy. In order for training to proceed smoothly, the model often has to ignore targets with complex spatial overlap and obscure features, and focus on learning regions with distinct features. This can lead to serious problems of object missing and wrong detections. To address the aforementioned issues, this paper proposed a co-teaching structure to improve the adaptability of the model to complex scenes. This approach involves analyzing the different performance of different categories of proposals in the training process, training two deep neural networks with mutual supervision simultaneously, and controlling the back-propagation loss function while ensuring the learning ability of the original MIL network. Experimental results have demonstrated that this structure can effectively enhance the accuracy of detection and can be seamlessly integrated into other models that perform the same MIL processing.
Multi-source image matching using improved ORB algorithm

Image matching is an essential technique in computer vision for many applications (e.g., image understanding). In order to improve the adaptability of previous matching algorithms to multi-source images (i.e., perspective and panoramic images) and increase the matching accuracy, we propose an improve Oriented FAST and Rotated BRIEF (ORB) matching algorithm. We first use the K-Nearest Neighbor (KNN) algorithm to roughly match the feature points extracted from the uniformly partitioned image grids and calculate their matching scores to obtain high scoring matching pairs. Then, we utilize the regional local consistency constraint and affine transformation verification of the Adaptive Local Affine Matching (AdaLAM) method to further refine the matching pairs. Finally, we perform matching experiments on both perspective and panoramic images and show better matching results than most previous matching approaches.
Dynamic hand gesture classification based on attention mechanism and transformer

In the era of rapid development of deep learning, the popular transformer in recent years has played an outstanding role in many fields, but most of it focuses on the development of language models, and is still in the development stage in computer vision. Gesture recognition is a relatively popular module in computer vision, but the mainstream method still uses CNN for gesture recognition, and there are few articles combining gesture recognition with attention mechanism and transformer. In this paper, we propose a dynamic gesture recognition model based on attention mechanism and Transformer. In order to extract the valid information in each frame, we add the attention mechanism to the feature extraction network, followed by passing it into the transformer to predict the hand gesture through the self-attention mechanism. The model has been tested on the IsoGD dataset, achiving good experimental results. Not only do we confirm that the attention mechanism can improve the recognition accuracy through ablation study, but also prove that transformer is feasible to process and identify the temporal information among frames in dynamic gestures and perform recognition.
An improved Apriori algorithm based on influence weight

Aiming at the problems in the actual mining process of the Apriori algorithm: it does not take into account the relationship between the influence of the item, and cannot mine the association rules of some small probability events, frequently scan the database and generate a large number of candidate itemsets, an improved Apriori algorithm based on the influence weight is proposed. Firstly, according to the impact of items on affairs and the relationship between items, a weight distribution method based on item influence is used to weight items, so as to extract more hidden and valuable associated information. Secondly, in order to reduce the scanning times of the database and the overhead of I/O ports, the weight vector and compression matrix are introduced to represent the transaction database. Finally, it is compared with the classical Apriori algorithm and the Apriori algorithm based on compressed matrix. Experimental results show that the improved Apriori algorithm has higher efficiency, and can mine out frequent patterns with high influence.
MLAGG-Net: Multi-level aggregation and global guidance network for pancreatic lesion segmentation in histopathological images

Pancreatic cancer is one of the most aggressive and lethal malignancies in the world. Automatic and accurate segmentation of lesion regions from histopathological images is essential for disease diagnosis and analysis. Nevertheless, it remains a challenging task due to the complicated pathological manifestations of the lesion, including the ambiguity of boundaries, the heterogeneity of textures, and the large variations in morphology, size and location. To address these problems, we propose a multi-level aggregation and global guidance network. Specifically, we utilize a multi-level feature fusion and distribution module that aims to alleviate the scale diversity and complex boundary problems in lesions by aggregating fine-grained information of low-level features and semantic information of high-level features. The cross-attention fusion module is proposed to perform the aggregation of hierarchical multi-level features, which can absorb effective information and suppress redundant information from them. This paper also introduces a global information fusion module to guide the network to locate the lesion areas with inconsistent distribution between the internal and boundary regions more accurately for more complete segmentation results. We conduct experiments on our PANC dataset and the public GlaS challenge dataset to verify the effectiveness of the proposed network. The experimental results show that the proposed method can achieve better segmentation performance with 90.02% Dice and 82.07% Jaccard compared to other state-of-the-art methods on our PANC dataset while achieving competitive results on the GlaS challenge dataset.
Optimization of communication performance in wireless seismic monitoring system

In order to improve the communication performance between different equipment in the seismic detection system, a wireless network system with MIMO (Multiple Input and Multiple Output) characteristics was designed and applied in the paper. On the basis of 5G communication, a new data conversion and transmission model was built, which can improve the rate of signal transmission and reduce the bit error rate and power consumption. For the purpose of power optimization, the overall framework of the network system was improved, of which power characteristics and performance were verified. The conjugate beam was normalized, and the cumulative distribution function under different downlink rates was obtained based on the maximum minimum power control scheme. In order to verify the energy-saving effect of the design, the ADC (Analog-to-Digital Conversion) power model is introduced. Through simulation analysis, the change rules of spectrum efficiency and energy efficiency under different network nodes were obtained. The results show that the normalized conjugate beam can be less affected by the transmission nodes, and the power control is more reliable under the same conditions. The research and application of this subject can significantly improve the wireless communication performance of the seismic monitoring system, reduce the power consumption of network equipment, improve the data transmission efficiency, and provide strong technical support for seismic prediction and data analysis. Through the test of artificial earthquake, it is verified that the communication scheme designed in this paper has higher transmission efficiency and lower bit error rate, and is very suitable for the long-distance transmission of seismic signals.
Research on efficient image semantic segmentation algorithm for glass transparent containers based on improved DeepLabV3+

Aiming at the problems of slow segmentation speed and excessive computational complexity in practical engineering applications when using existing models to segment glass transparent containers, an efficient image semantic segmentation algorithm based on improved DeepLabV3+for glass transparent containers is proposed. The proposed algorithm uses the MobileNetV3 network to replace the backbone feature extraction network Xception of the original model, effectively reduces the number of parameters of the semantic segmentation model, improves the ASPP module, introduces the strip pooling module (SPM) and depthwise separable atrous convolution (DCAC), and uses the densely connected multiple receptive field cascade (MRFC) to obtain multi-scale feature information. In the decoding structure, multi-level feature fusion (MLFF) is used to recover the details and levels of features lost during the down sampling process. Adding the spatial attention mechanism module SAM helps refine the target boundaries. The proposed model reasoning speed is 1.5 times faster than DeepLabV3+, and FLOPs are reduced by 71.4%. In addition to a 0.14% decrease in the cuvette's M<sub>iou</sub>, the beaker and reagent bottle's M<sub>iou</sub> increased by 0.19% and 0.18%, respectively. From the experimental results, we have improved the reasoning speed without completely losing accuracy, achieved the segmentation effect that is comparable to the original model, and well considered the accuracy, computational complexity, and reasoning speed, achieving a good balance between various aspects.
Fine-grained image recognition based on multi-branch and multi-scale learning

To address the problems of accurate region localization and effective data enhancement, this paper proposes a bilinear convolutional neural network model based on multiple branches and multiple scales. The method uses three branching networks to supervise the image recognition process, which enables the network model to learn fine-grained image features of different scales and different parts. The experimental results show that the bilinear convolutional neural network model based on multi-branch and multi-scale proposed in this paper has practical application value.
Research on smoke removal of industrial scene image based on an improved dark channel prior

In response to the challenge of obtaining clear monitoring images in non-ferrous metal smelters, particularly under complex atmospheric conditions and varying levels of dust and fog concentrations, we have designed an indoor dust and fog image clarification algorithm in this paper. This algorithm utilizes a concentration evaluation to obtain fog weights and construct a refined transmittance in dense fog regions. Transmittance values for non-dense fog regions are calculated using the a priori dark channel and ambient light matrix of the region. The Alpha blending technique is used to fuse ambient light and transmittance matrices of different regions, while bootstrap filtering is utilized to suppress generated noise and preserve image edge information. Finally, the global ambient light and transmittance values are obtained and substituted into the atmospheric scattering model to recover low-illumination dust and fog images. Experimental results demonstrate that the proposed algorithm achieves excellent performance in non-ferrous metal smelting environments by effectively reducing fog concentration in images, improving image illumination, and preserving image edge information.