Reviewerly/exHarmony huggingface dataset:
Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method

AbstractAbstractHeart disease is a leading cause of death worldwide. For decades, cardiologists have attempted to identify heart-disease risk factors to facilitate its prediction, prevention, and treatment. In recent years, electronic health records (EHRs) have become a valuable source for detecting these risk factors (e.g. smoking, obesity, and diabetes). However, challenges persist as EHRs include clinical notes in free-form and unstructured text, making it tedious for cardiologists to retrieve relevant information. To resolve this problem, we devised a deep-learning-based ensemble approach to automatically identify heart-disease risk factors from EHRs. This proposed approach can efficiently extract semantic information from EHRs and automate risk-factor identification with high performance. In particular, this approach does not require any external domain knowledge about the disease because a powerful Bidirectional Encoder Representations from Transformers (BERT) method is implemented to encode the discriminative features of clinical notes. The extracted features are then fed to conditional random fields (CRF) to identify all possible risk-factor indicators. Experimental results show that, in a scenario where no external knowledge is available, the proposed approach achieves state-of-the-art performance.Keywords: Deep learningnatural language processingelectronic health recordheart diseaserisk factors Consent and approval statementThis study has been exempt from the requirement for approval by an institutional review board. The data corpus is publicly available.Disclosure statement of interestThe authors report no conflict of interest.
Method for characterizing small-spot luminance in medical virtual reality headsets

We present an experimental method that utilizes a conic probe attached to an integrating sphere for characterizing the small-spot luminance of virtual reality (VR) head-mounted displays (HMDs). This allows for the measurement of relative luminance in virtual scenes and the assessment of VR displays for visualizing grayscale medical images. We characterized the relative luminance of the VIVE Pro and Oculus Rift by displaying test patterns with a circular dark spot of varying diameter in the bright field and compared the probe performance with commercially available systems. Compared to a conventional flat-panel display, the results show a significant level of veiling glare in the HMDs using both a customized conic probe with improved optical performance and an advanced imaging photometer. We also found no significant change in the relative luminance response with the probe’s working distance for the HMDs due to their unique optical architecture for near-eye viewing conditions. Our results highlight the issue of high veiling glare in HMDs and the importance of small-spot luminance measurements to adequately evaluate the technical performance of HMDs for emerging medical applications.
Image classification model based on large kernel attention mechanism and relative position self-attention mechanism

The Transformer has achieved great success in many computer vision tasks. With the in-depth exploration of it, researchers have found that Transformers can better obtain long-range features than convolutional neural networks (CNN). However, there will be a deterioration of local feature details when the Transformer extracts local features. Although CNN is adept at capturing the local feature details, it cannot easily obtain the global representation of features. In order to solve the above problems effectively, this paper proposes a hybrid model consisting of CNN and Transformer inspired by Visual Attention Net (VAN) and CoAtNet. This model optimizes its shortcomings in the difficulty of capturing the global representation of features by introducing Large Kernel Attention (LKA) in CNN while using the Transformer blocks with relative position self-attention variant to alleviate the problem of detail deterioration in local features of the Transformer. Our model effectively combines the advantages of the above two structures to obtain the details of local features more accurately and capture the relationship between features far apart more efficiently on a large receptive field. Our experiments show that in the image classification task without additional training data, the proposed model in this paper can achieve excellent results on the cifar10 dataset, the cifar100 dataset, and the birds400 dataset (a public dataset on the Kaggle platform) with fewer model parameters. Among them, SE_LKACAT achieved a Top-1 accuracy of 98.01% on the cifar10 dataset with only 7.5M parameters.
IoT-Based Human Activity Recognition for Smart Living

With the advancement in wireless sensing technology, the notion of the Internet of Things (IoT) has become ubiquitous and widely adopted due to its extensive applications in smart living. In that regard, Human Activity Recognition (HAR) is an indispensable part of intelligent systems for continuous supervision of human behavior. The design of effective applications requires accurate and relevant information on people’s activities and behaviors. This chapter presents a comprehensive review of the existing IoT-based HAR systems in the literature that promote smart living. The contribution of this work is to drive interested researchers toward the concept of HAR through existing works. In addition, a hands-on experimental case study is also provided to demonstrate the practical application of the concept, for recognizing and predicting several activities, in real life. Accordingly, here we first study the general architecture of the HAR system along with its principal components in detail. Next, the conglomeration of HAR with IoT and its impact on each other has been intensely explored to provide extensive insight for further research. We discuss the research challenges associated with developing IoT-based HAR systems. Next, the state-of-the-art works in HAR based on wearable sensors are surveyed thoroughly. At the end, we present a case study on HAR, using UCIHAR, a standard benchmark dataset that exhibits the practical implementation of the framework on different Machine Learning as well as Deep Learning models.
DESIGN OF A CONTROL SYSTEM FOR A SAFFLOWER PICKING ROBOT AND RESEARCH ON MULTISENSOR FUSION POSITIONING

This paper discusses the design of a safflower picking robot control system and focuses on a navigation control subsystem based on multisensor fusion. A navigation subsystem, an identification and positioning subsystem, a picking subsystem, and a levelling subsystem are designed. The hardware and software of the navigation subsystem are designed in detail, and a multisensor fusion positioning method based on extended Kalman fusion technology is proposed. The accuracy and stability levels of different combined navigation methods are compared. To test the effectiveness and accuracy of the proposed method, an outdoor test is carried out. The test results show that the outdoor fusion positioning accuracy of the robot is less than 8 cm, and when the satellite signal is lost, the navigation control subsystem can still provide high positioning accuracy. The final positioning result obtained using the integrated positioning method of the wheel odometer + IMU + DGNSS is approximately 52% higher than that of the odometer, approximately 29% higher than that of the wheel odometer + IMU, and approximately 11% higher than that of the IMU + DGNSS.
USING DEEP LEARNING AND VIRTUAL REALITY TO BUILD AN ANIMATION GAME FOR THE HEALTHCARE EDUCATION

This study aimed to create new experiences for the healthcare environment using virtual reality (VR) animation game technology by reviewing the advantages and disadvantages of VR from the literature. Using Aesop’s fables as the background of the game story, a VR animation game was created. The game design incorporated nine factors, and the five related immersion technologies were leveraged to design the game to enhance the state of flow and immersion. Experts tested the game using the present measurement and game experience questionnaires. The game experience was tested based on the following factors: Task fluency, degree of sensory feedback, degree of interactive experience, degree of immersion, and avoidance of virtual side effects. The results are as follows. (1) Providing relevant icons and guiding elements help players take risks on their own. (2) Audio feedback helps players with their virtual environment perception and enhances visual and tactile recognition. (3) Adding character elements, other than the protagonist, provides adventure information, which can improve the follow-up force of the game. (4) Immersion varies according to age, gender, play time, and virtual real-world experiencers that are different. (5) Motion sickness caused by the conflict between vision and perception should be avoided. The developed motion simulator allows players to detect the walking direction and speed on large equipment using a static floor or by wearing joint sensors. These results can be used as a reference for the development of VR animation games for the healthcare environment.
Securing High-Resolution Images From Unmanned Aerial Vehicles With DNA Encoding and Bit-Plane Extraction Method

Unmanned aerial vehicles (UAVs) are getting more popular for deployment in surveillance related operations owing to their flexibility and ability to reach hazardous areas.Moreover, the quality of digital cameras is getting better, and they can capture and store more visual information in high-resolution images.Unfortunately, due to the resource-constrained nature of UAVs, storing such large images can exhaust memory and related resources; whereas, the transmission of these images over the public link can pose several security threats.Securing these critical images during transmission from unauthorized access can be achieved through the use of efficient encryption techniques.This article proposes a novel encryption scheme incorporating both confusion and diffusion for encrypting both grey-scale and color images.In the proposed-encryption scheme, the image blocks are rearranged using a combination of random permutation, rotation, DNA encoding and zigzag pattern.Next, a bit-plane extraction method is used to obtain eight different bit-planes, including the most and least significant ones, from the scrambled image.These extracted bit-planes are then processed using confusion and diffusion techniques with a secret key, which is created using a hyper-chaotic map.The proposed method for encrypting the images taken by unmanned ariel vehicles is evaluated by examining its security level and time complexity using evaluation metrics such as correlation, entropy, energy, histogram analysis, keyspace and key sensitivity.The results and analysis demonstrated that the proposed encryption algorithm is able to effectively secure digital images.Additionally, the proposed work is also found to be superior to existing methods when compared using statistical security metrics.
Inferring Needless Write Memory Accesses on Ethereum Bytecode

Abstract Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts ). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas ). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, , whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by , that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.
RoBERT-Agr: An Entity Relationship Extraction Model of Massive Agricultural Text Based on the RoBERTa and CRF Algorithm

Joint entity recognition and relation extraction are complex in natural language processing. It is essential in information extraction and can be applied to knowledge graph construction question-answering systems. Existing problems in the agricultural text processing field include low text utilization, harrowing entity recognition relationship extraction, and low accuracy. To improve the utilization rate of the agricultural text and implement joint entity recognition and relation extraction of agricultural texts, this study constructs the agricultural text entity-relationship dataset AgriRE by collecting existing agricultural texts from the Internet and defining rules for corpus annotation. The AgriRE dataset sets up the primary entity and six types of relationships: alias, damaged position, genus, family, distribution area, and damaged crops. The dataset contains 177454 data samples, includes 1798 agricultural entities, and 12789 agricultural relationships. Based on the AgriRE dataset, this study proposes a joint entity recognition and relation extraction model named RoBERT-Agr based on the combination of RoBERTa, WWM and CRF algorithms. The model is used to realize the mutual entity recognition and relation extraction. The experimental results show that the method based on the RoBERT-Agr model has the highest F1 score compared with the existing advanced models. The model’s classification accuracy can reach 96.18%, and the F1 score on the AgriRE test set is 95.72% by training, verifying, and testing the model.
Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification

With the development of Internet technology, network platforms have gradually become a tool for people to obtain hot news. How to filter out the current hot news from a large number of news collections and push them to users has important application value. In supervised learning scenarios, each piece of news needs to be labeled manually, which takes a lot of time and effort. From the perspective of semi-supervised learning, on the basis of the non-negative Positive-Unlabeled (nnPU) learning, this paper proposes a novel algorithm, called ’Enhanced nnPU with Focal Loss’ (FLPU), for news headline classification, which uses the Focal Loss to replace the way the classical nnPU calculates the empirical risk of positive and negative samples. Then, by introducing the Virtual Adversarial Training (VAT) of the Adversarial training for large neural LangUage Models (ALUM) into FLPU, another (and better) algorithm, called ’FLPU+ALUM’, is proposed for the same purpose, aiming to label only a small number of positive samples. The superiority of both algorithms to state-of-the-art PU algorithms considered is demonstrated by means of experiments, conducted on two datasets for performance comparison. Moreover, through another set of experiments, it is shown that, if enriched by the proposed algorithms, the RoBERTa-wwm-ext model can achieve better classification performance than the state-of-the-art binary classification models included in the comparison. In addition, a ’Ratio Batch’ method is elaborated and proposed as more stable for use in scenarios involving only a small number of labeled positive samples, which is also experimentally demonstrated.
Make Flows Small Again: Revisiting the Flow Framework

Abstract We present a new flow framework for separation logic reasoning about programs that manipulate general graphs. The framework overcomes problems in earlier developments: it is based on standard fixed point theory, guarantees least flows, rules out vanishing flows, and has an easy to understand notion of footprint as needed for soundness of the frame rule. In addition, we present algorithms for automating the frame rule, which we evaluate on graph updates extracted from linearizability proofs for concurrent data structures. The evaluation demonstrates that our algorithms help to automate key aspects of these proofs that have previously relied on user guidance or heuristics.
Fault Detection Method of Glass Insulator Aerial Image Based on the Improved YOLOv5

Insulators play an important role in supporting and securing live conductors. In the task of insulator fault detection, existing models often miss small targets due to the interference of complex backgrounds. Furthermore, due to the very large aspect ratio of the insulators themselves, most models contain extraneous background in their results. To solve the above problems, this paper makes full use of the characteristics of the insulator itself and optimizes YOLOv5 from four aspects: feature extraction, feature fusion, candidate frame generation, and loss function. We divide the image feature extraction process into two procedures. The first procedure is mainly responsible for extracting insulator images from complex backgrounds, and the second stage is based on the extracted insulator images for fault analysis. At the same time, considering the trouble that the objective is too small due to the shooting distance, we design an attention mechanism that is more suitable for practical needs to enhance the ability to capture small objects. Furthermore, we design a rotated candidate box to improve the quality of the predicted box and reduce irrelevant background. The FAIN_detection dataset is compared with various advanced detection methods. The proposed method is more effective than the previous model by verifying the self-made data. Notably, the model achieved an average accuracy of 98% at 29 frames per second (FPS), laying the foundation for automatic insulator fault detection.
Hybrid Convolution-Transformer models for breast cancer classification using histopathological images

Breast cancer threatens the public health as it is among the leading causes of women death due to unawareness and diagnosis at the late stages. The detection of this cancer in its early stage is decisive to decrease mortality rates . Deep learning techniques are effective in analysis of medical images and achieve high performance in detecting the abnormal features, and classify them. Therefore, these methods are becoming increasingly popular in breast cancer diagnosis. Convolutional Neural Networks (CNNs) are commonly used for medical image analysis, but Vision transformers (ViTs ) are becoming more popular due to their excellent performance. However, ViTs still fall behind state-of-the-art convolutional networks. To overcome these limitations, many researchers have proposed a new approach that combines the advantages of CNNs and Transformers. This new approach overcomes the limitations of each by extracting low-level features, strengthening locality, and establishing long-range dependencies. In this study, the Hybrid Conv-Transformer approach was used to extract features from the BreakHis dataset of histopathological images. Coatnet and ConvMixer models were then used to classify the images into two binary classification based on both magnification-dependent and magnification-independent categories. The findings indicated that the suggested models exceeded prior models and recent deep learning techniques on the BreakHis dataset.
Deep Reinforcement Learning Applied to NLP: A Brief Survey

Natural Language Processing represents the domain at the intersection of computer science, artificial intelligence, and linguistics. It consists of creating computer programs that can process and understand human language. Recently, many researchers who have investigated and used them to solve different obstacles have focused their emphasis on reinforcement learning and deep learning. By examining the benefits and disadvantages of the research in the nexus of deep learning and reinforcement learning, we examine the potential for using deep reinforcement learning algorithms for natural language processing in this work.
Qualitative Assessment of the Educational Use of an Electrical Engineering YouTube Channel

Engineering undergraduates face some specific challenges during their studies, such as an intense presence of abstract concepts in their subjects as well as the inability to connect different technical subjects into the big picture of the degree. This is also common to other STEM (Science, Technology, Engineering and Mathematics) disciplines. Multimedia resources as a complement to education present benefits that might help increasing students motivation and performance. This research focuses on YouTube as a source for those resources, presenting a qualitative analysis of the educational use of an electrical engineering channel, through the evaluation of its audience perceptions. A total of 524 open participations have been collected, coded, and analyzed. Exploratory and cooccurrences analyses show a predominant use of these videos for understanding and gaining knowledge, considering them interesting and of good quality. This study confirms that subjective perception from the channel's audience is mainly positive for its integration in pedagogical processes.
Weighted Lexicon-based Sentiment Analysis for Women Career Traits in Information Technology

In this paper, an unsupervised sentiment analysis model leveraging the Polarity Rank algorithm and sentence dependency graph is proposed to predict the sentiments of women in IT. The primary objective is to identify groups of individuals with common traits (community and job role) who had different sentiments, as well as explore the impact of Diversity Equity & Inclusion (DEI) programs on participants' career satisfaction. The model, based on a 7-point Likert scale, classified responses into Very Negative, Negative, Slightly Negative, Neutral, Slightly Positive, Positive, and Very Positive classes By delving into different demographics and different questions within the survey, it was found that DEI programs have a positive impact on career satisfaction as well as lessening the presence of Anger, Fear, and Sadness. Additionally, it was discovered that Anticipation was a dominant emotion in all responses. This paper provides a foundational look at the sentiments expressed by women in the IT industry.
Adaptive Chaotic Injection to Reduce Overfitting in Artificial Neural Networks

Artificial neural networks (ANNs) have become an integral tool in various fields of research. ANNs are mathematical models which can be trained to perform various prediction tasks. The effectiveness of an ANN can be impacted by overfitting which occurs when the ANN overfits to the training data. As a result, the ANN does not generalize well to novel data. In our research, we assess the feasibility of using a chaotic strange attractor to generate sequences of values to inject into an ANN to reduce overfitting. An adaptive method was developed to scale and inject the values into the neurons throughout training. The chaotic injection (ChI) was tested on three benchmark datasets using different ANN models. The results were compared against the baseline ANN, dropout (DO), and Gaussian noise injection (GNI). The ChI improved the performance of the ANN and converged faster than DO and GNI.