Reviewerly/exHarmony huggingface dataset:
Incomplete and possibly selective recording of signs, symptoms, and measurements in free text fields of primary care electronic health records of adults with lower respiratory tract infections

<h2>Abstract</h2><h3>Objectives</h3> To assess the completeness of recording of relevant signs, symptoms, and measurements in Dutch free text fields of primary care electronic health records (EHR) of adults with lower respiratory tract infections (LRTI). <h3>Study Design and Setting</h3> Retrospective cohort study embedded in a prediction modeling project using routine health care data of the Julius General Practitioners' Network of adult patients with LRTI. Free text fields of 1,000 primary care consultations of LRTI episodes between 2016 and 2019 were manually annotated to retrieve data on the recording of sixteen relevant signs, symptoms, and measurements. <h3>Results</h3> For 12/16 (75%) of the relevant signs, symptoms, and measurements, more than 50% of the values was not recorded. The patterns of recorded values indicated selective recording of positive or abnormal values. Recording rates varied across consultation type (physical consultation vs. home visit), diagnosis (acute bronchitis vs. pneumonia), antibiotic prescription issued (yes vs. no), and between practices. <h3>Conclusion</h3> In EHR of primary care LRTI patients, recording of signs, symptoms, and measurements in free text fields is incomplete and possibly selective. When using free text data in EHR-based research, careful consideration of its recording patterns and appropriate missing data handling techniques is therefore required.
Dataset construction to detect human behavior with the help of emotions, sentiments and mood for Roman Urdu

Roman Urdu and English are often used together as a hybrid language for communication on social media. Because writers don't worry about spelling when utilising the English alphabet to write Urdu during texting, it becomes challenging to interpret mixed codes for emotions. There are over 14,000 emotion lexicons in this dataset, each of which lists nine different emotions and their polarities. The NRC emotion lexicons [8] provided in Urdu have been transliterated into Roman Urdu. To verify that the provided translation is accurate, we used three online dictionaries of Urdu. A Python script that transliterates words from Urdu to Roman Urdu has been used to develop Roman Urdu transliteration. Sentiment and mood, depending on the emotion lexicon, are also provided. The textual data has been annotated using the unigram feature and distance estimation among strings and lexicons. Approximately 10,000 sentences from the baseline sample have been automatically annotated.
An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery

This study aimed to present a novel markerless augmented reality (AR) system using automatic registration based on machine-learning algorithms that visualize the facial region and provide an intraoperative guide for facial plastic and reconstructive surgeries. This study prospectively enrolled 20 patients scheduled for facial plastic and reconstructive surgeries. The AR system visualizes computed tomographic data in three-dimensional (3D) space by aligning with the point clouds captured by a 3D camera. Point cloud registration consists of two stages: the preliminary registration gives an initial estimate of the transformation using landmark detection, followed by the precise registration using Iterative Closest Point algorithms. Computed Tomography (CT) data can be visualized as two-dimensional slice images or 3D images by the AR system. The AR registration error was defined as the cloud-to-cloud distance between the surface data obtained from the CT and 3D camera. The error was calculated in each facial territory, including the upper, middle, and lower face, while patients were awake and orally intubated, respectively. The mean registration errors were 1.490 ± 0.384 mm and 1.948 ± 0.638 mm while patients were awake and orally intubated, respectively. There was a significant difference in the errors in the lower face between patients while they were awake (1.502 ± 0.480 mm) and orally intubated (2.325 ± 0.971 mm) when stratified by facial territories (p = 0.006). The markerless AR can accurately visualize the facial region with a mean overall registration error of 1–2 mm, with a slight increase in the lower face due to errors arising from tube intubation.
A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation

Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.
Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network

A large global health issue is cancer, wherein early diagnosis and treatment have proven to be life-saving. This holds true for oral cancer, thus emphasizing the significance of timely intervention. Deep learning techniques have gained traction in early cancer detection, exhibiting promising outcomes in accurate diagnosis. However, collecting a substantial amount of training data poses a challenge for deep learning models in cancer diagnosis. To address this limitation, this study proposes an oral cancer diagnosis approach based on a few-shot learning framework that circumvents the need for extensive training data. Specifically, a prototypical network is employed to construct a diagnostic model, wherein two feature extractors are utilized to extract prototypical features and query features respectively, departing from the conventional use of a single feature extraction function in prototypical networks. Moreover, a customized loss function is designed for the proposed method. Rigorous experimentation using a histopathological image dataset demonstrates the superior performance of our proposed approach over comparison methods.
An empirical study of counterfactual visualization to support visual causal inference

Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations.
A Parameter-independent Algorithm of Finding Maximum Clique with Seidel Continuous-Time Quantum Walks

The maximum clique (MC) problem holds significance in network analysis. Quantum-based algorithms have recently emerged as promising approaches for this problem. However, these algorithms heavily depend on parameters of quantum system and vary significantly for different graphs. In order to tackle this issue, we initially demonstrate that continuous-time quantum walks (CTQW) driven by the Seidel matrix offer valuable insights into the clique structure of graphs, outperforming the CTQW driven by adjacency matrix. Specifically, we conduct an in-depth analysis for CTQW of 4 types of graphs, meticulously calculating the amplitudes associated with different vertices. Our findings consistently reveal that vertices belonging to MC exhibit the highest intensity at the largest frequency component of the probability amplitude for these types of graphs. Considering the varying intensities, we propose a parameter-independent algorithm for determining the MC. We compare our algorithm with a typical quantum-based algorithm, the results indicate that our algorithm exhibits greater stability.
Squeezing as a resource for time series processing in quantum reservoir computing

Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.
Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

<h2>Abstract</h2> The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.
Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

Designing effective AI models becomes a challenge when dealing with imbalanced/skewed class distributions in datasets. Addressing this, re-sampling techniques often come into play as potential solutions. In this investigation, we delve into the male fertility dataset, exploring 14 re-sampling approaches to understand their impact on enhancing predictive model performance. The research employs conventional AI learners to gauge male fertility potential. Notably, five ensemble AI learners are studied, their performances are compared, and their results are evaluated using four measurement indices. Through comprehensive comparative analysis, we identify substantial enhancement in model effectiveness. Our findings showcase that the LightGBM model with SMOTE-ENN re-sampling stands out, achieving an efficacy of 96.66% and an F1-Score of 95.60% through 5-fold cross-validation. Interestingly, the CatBoost model, without re-sampling, exhibits strong performance, achieving an efficacy of 86.99% and an F1-Score of 93.02%. Furthermore, we benchmark our approach against state-of-the-art methods in male fertility prediction, particularly highlighting the use of re-sampling techniques like SMOTE and ESLSMOTE. Consequently, our proposed model emerges as a robust and efficient computational framework, promising accurate male fertility prediction.
Center extraction method for reflected metallic surface fringes based on line structured light

Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal’s surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region’s grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025 mm, and the repeatability of the measurement accuracy is 0.026 mm.
A collection of 157 individual neuromelanin-sensitive images accompanied by non-linear neuromelanin-sensitive atlas and a probabilistic locus coeruleus atlas

The current dataset aims to support and enhance the research reliability of neuromelanin regions in the brainstem, such as locus coeruleus (LC), by offering raw neuromelanin-sensitive images. The dataset includes raw neuromelanin-sensitive images from 157 healthy individuals (8-64 years old). In addition, leveraging individual neuromelanin-sensitive images, a non-linear neuromelanin-sensitive atlas, generated through an iterative warping process, is included to tackle the common challenge of a limited field of view in neuromelanin-sensitive images. Finally, the dataset encompasses a probabilistic LC atlas generated through a majority voting approach with pre-existing multiple atlas-based segmentations. This process entails warping pre-existing atlases onto individual spaces and identifying voxels with a majority consensus of over 50 % across the atlases. This LC probabilistic atlas can minimize uncertainty variance associated with choosing a specific single atlas.
Lexicon dataset for the Hausa language

This paper presents a comprehensive augmented lexicon sentiment analysis dataset for the Hausa language. The dataset was created by adopting words and phrases from a Hausa Language dictionary and then using the data augmentation method to expand the quantity of the dataset. The researchers manually annotated each phrase/sentence with positive, negative, or neutral polarity. The dataset consists of 14,663 rows, with 4,154 positives, 4,310 negatives, and 6,199 neutrals. The dataset is valuable because it contributes to the available resources for sentiment analysis, especially for Hausa, which is a low-resource language. The dataset will benefit researchers in sentiment analysis who want to develop a model to analyze Hausa posts on social media or product reviews in the Hausa language.
Feature-exponence mapping in language contact

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Model and data-centric machine learning algorithms to address data scarcity for failure identification

The uneven occurrence of certain types of failures in optical networks results in a scarcity of data for less frequent failures, leading to imbalanced datasets for training machine learning (ML) models. This poses a significant bottleneck in terms of reliability and practical implementation of ML for failure management. Existing research works often overlook this aspect while demonstrating high accuracies by utilizing sufficiently balanced training datasets collected in controlled laboratory setups and simulations. However, this approach does not reflect a realistic network scenario. To address this issue, different model-centric and data-centric approaches have been investigated in this work to determine their potential for improving the learning of ML models, specifically neural networks (NNs), on less frequent failures with such imbalanced training datasets. For failure identification, the obtained results suggest that data-centric approaches tend to perform better in terms of classification accuracy, with an improvement of up to 5.5% in F1-score observed on less frequent failures compared to a baseline NN (i.e., without any model-centric or data-centric treatment). However, some data-centric approaches may also have significant additional computational complexity associated with them, and, therefore, a suitable approach should be chosen based on the desired classification performance and available computational resources.
CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.
Perfect single-photon sources

Abstract We introduce the gapped coherent state in the form of a single-photon source (SPS) that consists of uncorrelated photons as a background, except that we demand that no two photons can be closer in time than a time gap $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . While no obvious quantum mechanism is yet identified to produce exactly such a photon stream, a numerical simulation is easily achieved by first generating an uncorrelated (Poissonian) signal and then for each photon in the list, either adding such a time gap or removing all photons that are closer in time from each other than $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . We study the statistical properties of such a hypothetical signal, which exhibits counter-intuitive features. This provides a neat and natural connection between continuous-wave (stationary) and pulsed single-photon sources, with also a bearing on what it means for such sources to be perfect in terms of single-photon emission.