Reviewerly/exHarmony huggingface dataset:
Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

With the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies.The purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored.The data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original Bidirectional Encoder Representations from Transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods.We found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 69.2795315, respectively. Our composite abutting joint model (RoBERTa-WWM-ext + CNN) had a macro-F1 value of 70.55936311, showing that our model outperformed the other models in the task.The accuracy of the original model can be greatly improved by giving priority to WWM and replacing the word-based mask with unit to classify and label medical entities. Better results can be obtained by effectively optimizing the downstream tasks of the model and the integration of multiple models later on. The study findings contribute to the translation of online consultation information into machine-readable information.
An Improved Efficient Certificateless Hybrid Signcryption Scheme for Internet of Things

The Internet of Things (IoT) is a vast perceptual network formed by a variety of IoT devices connected to each other. In IoT, various devices cooperate with each other to collect and transmit private data of IoT and sustain the efficient and orderly operation of IoT. However, in the complex security situation of IoT, due to the resources of IoT devices being limited, it is difficult for IoT devices to afford the heavy resource consumption of sophisticated data encryption and decryption mechanism, which brings certain security risk to data transmission of IoT. To ensure safe and efficient data transmission, we propose an improved efficient certificateless hybrid signcryption scheme for IoT, which satisfies confidentiality, unforgeability, forward security, public verification, and known session-specific temporary information security; meanwhile, we prove them in random oracle model (ROM). In addition, through simulation experiment, we find that our scheme has higher communication efficiency and computational efficiency compared with existing schemes.
A Prolog application for reasoning on maths puzzles with diagrams

Despite the indisputable progresses of artificial intelligence, some tasks that are rather easy for a human being are still challenging for a machine. An emblematic example is the resolution of mathematical puzzles with diagrams. Sub-symbolical approaches have proven successful in fields like image recognition and natural language processing, but the combination of these techniques into a multimodal approach towards the identification of the puzzle’s answer appears to be a matter of reasoning, more suitable for the application of a symbolic technique. In this work, we employ logic programming to perform spatial reasoning on the puzzle’s diagram and integrate the deriving knowledge into the solving process. Analysing the resolution strategies required by the puzzles of an international competition for humans, we draw the design principles of a Prolog reasoning library, which interacts with image processing software to formulate the puzzle’s constraints. The library integrates the knowledge from different sources, and relies on the Prolog inference engine to provide the answer. This work can be considered as a first step towards the ambitious goal of a machine autonomously solving a problem in a generic context starting from its textual-graphical presentation. An ability that can help potentially every human–machine interaction.
A deep learning model for Ottoman <scp>OCR</scp>

Abstract The Ottoman OCR is an open problem because the OCR models for Arabic do not perform well on Ottoman. The models specifically trained with Ottoman documents have not produced satisfactory results either. We present a deep learning model and an OCR tool using that model for the OCR of printed Ottoman documents in the naksh font. We propose an end‐to‐end trainable CRNN architecture consisting of CNN, RNN (LSTM), and CTC layers for the Ottoman OCR problem. An experimental comparison of this model, called Osmanlica.com , with the Tesseract Arabic, the Tesseract Persian, Abby Finereader, Miletos, and Google Docs OCR tools or models was performed using a test data set of 21 pages of original documents. With 88.86% raw text , 96.12% normalized text , and 97.37% joined text character recognition accuracy, the Osmanlica.com Hybrid model outperforms the others with a marked difference. Our model outperforms the next best model by a clear margin of 4% which is a significant improvement considering the difficulty of the Ottoman OCR problem, and the huge size of the Ottoman archives to be processed. The hybrid model also achieves 58% word recognition accuracy on normalized text which is the only rate above 50%.
Research on Vehicle Detection and Recognition Based on Infrared Image and Feature Extraction

Vehicle detection and identification and safe distance keeping technology have become the main content of current intelligent transportation system research. Among them, vehicle detection and recognition is one of the most important research contents, and it is also crucial to the safe driving of vehicles. Real-time detection and recognition of current vehicles can effectively prevent the occurrence of malignant traffic accidents such as rear-end collision. Because the infrared image has some shortcomings such as poor contrast, loud noise, and blurred edge, this paper mainly studies the color space preprocessing of the image and uses threshold segmentation method and infrared image enhancement to segment the front vehicle and background. That is to say, by analyzing the infrared image captured by infrared CCD, we use median filter to remove noise from the collected infrared image and then use the improved histogram equalization to enhance the contrast of the image. Vertical Sobel operator is selected to enhance the vertical edge of the image, and the image is segmented by binary segmentation method. Finally, vehicle detection and recognition are realized by vertical edge symmetry, aspect ratio, and gray-scale symmetry. The experimental image and experimental data analysis results show that the image processing technology studied in this paper has achieved the intended research purpose.
Automated Focal Plane Merging From a Stack of Gonioscopic Photographs Using a Focus-Stacking Algorithm

The purpose of this study was to investigate the utility of automated focal plane merging with the collection of gonio-photographs with different depths of field (DOF) using an established focus-stacking algorithm.A cross-sectional study was conducted at Shimane University Hospital, Izumo, Japan. Sixteen eyes from 16 subjects from the glaucoma clinic were included in this study. Image processing was performed for the images of 16 eyes from 16 angle sector following the successful gonio-photography. The 256 sets of focus-stacked and best-focused images were prepared in random order and were compared for the DOF and informativeness to diagnose angle pathology by masked observers in each set as the subjective assessments. Moreover, the energy of the Laplacian (average |ΔI|), which is an indicator of image sharpness between the photographs with and without the focus-stacking processing was also analyzed with the Laplacian filter as the objective assessment.The automated image processing was successfully performed in all stacks of images. The significant deepening of DOF and improvement of informativeness achieved in 255 (99.6%) and 216 (84.4%) images (P < 0.0001 for both, sign test) and the energy of the Laplacian also significantly increased in 243 (94.9%) images (P < 0.0001, sign test).Focal plane merging by the automated algorithm can make the gonio-images deeper focus compared with the paired best-focused images subjectively and objectively, which would be useful for angle pathological assessment in clinical practice.Focal plane merging algorithm for the automated gonio-photography can facilitate the angle assessment by providing informative deep-focus image, which would be useful for glaucoma care.
Analysis of Texture Synthesis Algorithms in Computer Graphics

The use of computer graphics methods is associated with the ongoing problem of texture synthesis. There are two groups of approaches to this problem. The first one is the creating textures by a digital artist using such software tools as Substance Painter. The second group includes automatic methods based on specialized algorithms. Some examples include synthesis using non-parametric sampling, as well as machine learning algorithms based on PSGAN. This paper provides a comparative analysis based on quality and performance criteria of texture synthesis algorithms. It is worth to mention that the difficulty of assessing the quality of synthesis is caused by the lack of a formal quantitative metric. The authors made conclusions about the preferred fields for application of the analyzed algorithms. The results of this survey can be used to optimize the production activities of digital art studios in terms of the time it takes to make textures.
Initial Seeds Selection for K-means Clustering Based on Outlier Detection

K-means clustering is a widely used algorithm in cluster analysis. However, the selection of initial seeds determines the results of K-means clustering. The conventional K-means algorithm usually adopts the random strategy to select initial seeds, which is unable to generate an ideal clustering result in many cases. To solve the problem of the existing initial seeds selection (abbreviated to ISS) strategies for K-means clustering, we propose a novel initial seeds selection algorithm, called ISS_OD, based on outlier detection. In ISS_OD, we select the initial seeds of K-means clustering by calculating the distance outlier factor of every object, the weighted density of every object and the weighted distances between objects. Experimental results on several UCI datasets demonstrate the effectiveness of our algorithm for the ISS of K-means clustering.
Dynamic and Static Features-Aware Recommendation with Graph Neural Networks

Recommender systems are designed to deal with structured and unstructured information and help the user effectively retrieve needed information from the vast number of web pages. Dynamic information of users has been proven useful for learning representations in the recommender system. In this paper, we construct a series of dynamic subgraphs that include the user and item interaction pairs and the temporal information. Then, the dynamic features and the long- and short-term information of users are integrated into the static recommendation model. The proposed model is called dynamic and static features-aware graph recommendation, which can model unstructured graph information and structured tabular data. Particularly, two elaborately designed modules are available: dynamic preference learning and dynamic sequence learning modules. The former uses all user-item interactions and the last dynamic subgraph to model the dynamic interaction preference of the user. The latter captures the dynamic features of users and items by tracking the preference changes of users over time. Extensive experiments on two publicly available datasets show that the proposed model outperforms several compelling state-of-the-art baselines.
Toward Understanding the Effects of Visual and Tactile Stimuli to Reduce the Sensation of Movement with XR Mobility Platform

This paper investigates a reduction method for passenger&#x0027;s movement sensation with the XR mobility platform mounted on an autonomous vehicle to improve passenger comfort during auto-driving. We investigate a reduction method that controls passenger&#x0027;s sense of movement by controlling visual and tactile perception using a multimodal XR mobility platform which consists of an immersive display and a motion platform with a tilting seat. The result of 30 subjects shows the sense of movement perceived by the passenger was reduced significantly when both visual acceleration and tactile acceleration control method was activated inside a moving autonomous vehicle.
Virtual Reality-Based Stimuli for Immersive Car Clinics: A Performance Evaluation Model

This study proposes a model to evaluate the performance of virtual reality-based stimuli for immersive car clinics. The model considered Attribute Importance, Stimuli Efficacy and Stimuli Cost factors and the method was divided into three stages: we defined the importance of fourteen attributes relevant to a car clinic based on the perceptions of Marketing and Design experts; then we defined the efficacy of five virtual stimuli based on the perceptions of Product Development and Virtual Reality experts; and we used a cost factor to calculate the efficiency of the five virtual stimuli in relation to the physical. The Marketing and Design experts identified a new attribute, Scope; eleven of the fifteen attributes were rated as Important or Very Important, while four were removed from the model due to being considered irrelevant. According to our performance evaluation model, virtual stimuli have the same efficacy as physical stimuli. However, when cost is considered, virtual stimuli outperform physical stimuli, particularly virtual stimuli with glasses. We conclude that virtual stimuli have the potential to reduce the cost and time required to develop new stimuli in car clinics, but with concerns related to hardware, software, and other definitions.
Feasibility of Training Elite Athletes for Improving their Mental Imagery Ability Using Virtual Reality

The goal of imagery training for athletes is to create realistic images in their minds and to familiarize them with certain procedures, environments, and other aspects related to competition. Traditional imagery training methods use still images or videos, and athletes study the pictures or watch the videos in order to mentally rehearse. However, factors such as distractions and low realism can affect the training quality. In this paper, we present a VR solution and a study that explores our hypotheses that 1) high-fidelity VR systems improve mental imagery skills, and that 2) the presence of elements such as an audience or photographers in the VR environment result in better mental imagery skill improvement.
STARE: Semantic Augmented Reality Decision Support in Smart Environments

The Internet of Things facilitates real-time decision support within smart environments. Augmented Reality allows for the ubiquitous visualization of IoT-derived data, and AR visualization will simultaneously permit the cognitive and visual binding of information to the physical object(s) to which they pertain. Essential questions exist about efficiently filtering, prioritizing, determining relevance, and adjudicating individual information needs in real-time decision-making. Therefore, this paper proposes a novel AR decision support framework (STARE) to support immediate decisions within a smart environment by augmenting the user&#x0027;s focal objects with assemblies of semantically relevant IoT data and corresponding suggestions.
An image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree

This paper proposes an image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree. Firstly, other programming software is used to perform the middle order traversal, and the plaintext image is sorted according to the middle order traversal sequence on the permutation. Secondly, the chaotic sequence is generated using the coupled map lattice to set the chaotic interference value. Finally, the XOR operation between the adjacent pixel values of the replacement image is completed to generate the ciphertext matrix. The simulation and experimental results show that the proposed algorithm can resist typical attacks and has good robustness.
Infrared Image Analysis for Human Face Recognition

Thermal infrared images have potential to captured the images without being affected by illumination. In this paper, we proposed a face recognition method using infrared images. The Convolution Neural Network are used to extract feature from the images with the combination of Haar Cascade, and Local Binary Pattern to indicate the face area. At the same time, images were acquired by using infrared camera and webcam. Compared between infrared and visible images, the experimental result from infrared images by using Convolutional Neural Network in combined with Haar Cascade show superiority in the measurement result of several parameters used with the highest score in accuracy up to 98&#x0025; which outperforms the other experiment in this paper.
Light weight object detector based on composite attention residual network and boundary location loss

The object detector based on deep learning has received extensive attention, but the high computational cost has become an obstacle to its large-scale application. It is a great challenge for object detection to further reduce the hardware requirements on the premise of ensuring high detection accuracy. We propose a one-stage lightweight object detector and a new regression loss. In this method, ResNet is improved and combined with attention mechanism to ensure the maximum integrity of feature information with fewer parameters; The multi-scale feature fusion network is improved to reduce the reasoning complexity of the structure. In addition, the bounding box regression loss is improved, and the specific position of the bounding box is adjusted by considering the balance of multiple factors in the regression process. The experimental results show that: 1) the combination of most detectors and improved loss can further improve the performance of detectors; 2) As a whole, our improved network and loss can give consideration to both speed and accuracy on Pascal VOC and COCO; 3) in the addition of other new training tricks such as DropBlock and Mosaic, we can achieve better overall performance on the coco test development set, 38.42 AP (average accuracy) at 40.3 FPS.
Extension of elliptic curve Qu–Vanstone certificates and their applications

In public key infrastructure, a certificate, issued by a certificate authority (CA), is used to guarantee the connection between a user and her/his public key. In order to improve the efficiency, the concept of implicit certificate protocol is introduced by Girault and Gönther. In the existing implicit certificate protocol, a user must issue a certificate request to the CA for each key pair. However, in certain applications (e.g., IoT, sensor networks, and cryptocurrency), a user (or a device) will have multiple public/private key pairs that are related to the same identity. Therefore, the communication cost will be linearly related to the number of key pairs the user has. Furthermore, the storage cost of a large number of certificates is not an ideal property in practice. In this paper, to address the above issues, we proposed two schemes from the most widely used elliptic curve Qu–Vanstone implicit certificate scheme (ECQV). In our first scheme, called M-ECQV I, an ECQV certificate holder, who obtains an ECQV certificate issued by the certificate authority, can further issue multiple credentials with the same identity as ECQV certificate holder and the corresponding key pairs from the ECQV certificate. In our second scheme, called M-ECQV II, it not only supports the comparable functionality of M-ECQV I, but the verifier can ensure that the credentials are only used by the ECQV certificate holder (i.e., these credential are "self-use") to be suitable to different scenarios. In addition, the security models are well-defined and the rigorous security proofs are also given. Experimental results show that our schemes not only greatly improve the performance, but also reduce the storage cost.
Novel Physical Fitness Fuzzy Evaluation Model for Individual Health Promotion

Physical fitness level plays a significant role in health promotion. Cardiorespiratory endurance, muscular endurance, muscle power, and flexibility are the four key indicators of physical fitness level, listed as one of the important fields of preventive medicine. Some studies targeted at students, based on statistical inference, have put forward a set of physical fitness evaluation methods to see whether they have reached the level of healthy physical fitness. Testing and monitoring of individual physical fitness takes up little time and requires a small sample dataset; this paper hence proposed an evaluation and analysis model that suits individual physical fitness by means of a fuzzy evaluation method suitable for evaluating small sample datasets. This paper developed the evaluation model based on the upper confidence limit of the physical fitness evaluation index so that it could reduce the risk of misjudgment caused by sampling error. At the same time, a simple and easy-to-use fuzzy evaluation form was developed as an evaluation interface, which can present the whole picture of all evaluation indicators as well as have good and convenient management performance. Accordingly, it can help every individual simultaneously monitor multiple physical fitness indicators to ensure that each physical fitness index can meet the requirement of healthy physical fitness.
A novel ensemble feature selection method through Type I fuzzy

These days, one of the needed methods in machine learning is feature selection. In other words, in this manner, the most fitting features are picked. Nevertheless, there are various feature selection methods, getting the most suitable features still is a complex problem. Lately, applying several feature selection methods rather than a unique feature selection method is more efficient. In this paper, a new ensemble feature selection method based upon fuzzy Type-I named EFSF is presented. First, three different individual feature selection methods are applied to determine the rank of features separately. Next, Type-I fuzzy handles feature selections' uncertainty and decrease noise to give each feature the best rank. To validate the act of EFSF, it is compared with some ensemble methods and several advanced feature selection methods. EFSF is assessed based on Accuracy, Precision and Recall, metrics. The outcomes verify that the EFSF is better than its competitors. The source code of EFSF is here.