Reviewerly/exHarmony huggingface dataset:
An Effective Medical Image Classification: Transfer Learning Enhanced by Auto Encoder and Classified with SVM

The count of white blood cells is vital for disease diagnosis, which is exploited to identify many diseases like infections and leukemia. COVID-19 is another critical disease which should be detected and cured immediately. These diseases are better diagnosed using radiological and microscopic imaging. A clinical experience is required by a physician, to identify and classify the Chest X-rays or the microscopic blood cell images. In this study a novel approach is proposed for classifying medical images by using transfer learning method which is ResNet-50 where features are reduced with Auto Encoder (AE) and classified with a Support Vector Machine (SVM) instead of softmax classifier which is tested with different optimizers. The proposed method is compared with VGG-16 and ResNet-50, Inception-V3 which use softmax classifiers. Experimental results indicated that the proposed method possess 97.3% and 99% accuracy on WBC and COVID-19 datasets respectively which are higher than compared methods for each dataset.
Robust 2D and 3D images zero - watermarking using dual Hahn moment invariants and Sine Cosine Algorithm

In this paper, we initially provide significant improvements on the computational aspects of dual Hahn moment invariants (DHMIs) in both 2D and 3D domains. These improvements ensure the numerical stability of DHMIs for large-size images. Then, we propose an efficient method for optimizing the local parameters of dual Hahn polynomials (DHPs) when computing DHMIs using the Sine-Cosine Algorithm (SCA). DHMIs optimized via SCA are used to propose new and robust zero-watermarking scheme applied to both 2D and 3D images. On one hand, the simulation results confirm the efficiency of the proposed computation of 2D and 3D DHMIs regarding the numerical stability and invariability. Indeed, the proposed computation method of 2D DHMIs allows to reach a relative error (RE) of the order ≈10−10 for images of size 1024 × 1024 with an execution time improvement ratio exceeds 70% (ETIR ≥ 70%), which validates the efficiently of the proposed computation method. On the other hand, the simulation and comparison outcomes clearly demonstrate the robustness of the proposed zero-watermarking scheme against various geometric attacks (translation, rotation, scaling and combined transformations), as well as against other common 2D and 3D image processing attacks (compression, filtering, noise addition, etc.).
In-depth analysis of the impact of OCR errors on named entity recognition and linking

Abstract Named entities (NEs) are among the most relevant type of information that can be used to properly index digital documents and thus easily retrieve them. It has long been observed that NEs are key to accessing the contents of digital library portals as they are contained in most user queries. However, most digitized documents are indexed through their optical character recognition (OCRed) version which include numerous errors. Although OCR engines have considerably improved over the last few years, OCR errors still considerably impact document access. Previous works were conducted to evaluate the impact of OCR errors on named entity recognition (NER) and named entity linking (NEL) techniques separately. In this article, we experimented with a variety of OCRed documents with different levels and types of OCR noise to assess in depth the impact of OCR on named entity processing. We provide a deep analysis of OCR errors that impact the performance of NER and NEL. We then present the resulting exhaustive study and subsequent recommendations on the adequate documents, the OCR quality levels, and the post-OCR correction strategies required to perform reliable NER and NEL.
Motion Planning Solution with Constraints Based on Minimum Distance Model for Lane Change Problem of Autonomous Vehicles

Lane change is one of the important operations in motion of an autonomous vehicle. When encountering obstacles or wanting to overtake the vehicle ahead, the autonomous vehicle will make a decision and choose the best path to control the trajectory of motion to perform lane change. In this article, we will present solutions for lane change trajectories, including general path setting, building nonlinear models with states of vehicle speed, acceleration and jerk; building a constraint set to avoid collisions with a minimum safe distance model, which takes into account the potentially collision angle positions during lane change. Simulation results are performed in Matlab simulation environment to demonstrate an effective proposed solution and addressed the disadvantages in the modeling process for lane-changing operations, in order to improve the proactive safety of the motion planning for autonomous vehicles.
Diffeomorphic transforms for data augmentation of highly variable shape and texture objects

Background and objective: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures. This means that currently available databases are too small and not suitable to train deep learning models from scratch. To deal with this problem, data augmentation techniques are commonly used to increase the dataset size. However, typical data augmentation methods introduce artifacts or apply distortions to the original image, which instead of creating new realistic samples, obtain basic spatial variations of the original ones. Methods: We propose a novel data augmentation procedure which generates new realistic samples, by combining two samples that belong to the same class. Although the idea behind the method described in this paper is to mimic the variations that diatoms experience in different stages of their life cycle, it has also been demonstrated in glomeruli and pollen identification problems. This new data augmentation procedure is based on morphing and image registration methods that perform diffeomorphic transformations. Results: The proposed technique achieves an increase in accuracy over existing techniques of 0.47%, 1.47%, and 0.23% for diatom, glomeruli and pollen problems respectively. Conclusions: For the Diatom dataset, the method is able to simulate the shape changes in different diatom life cycle stages, and thus, images generated resemble newly acquired samples with intermediate shapes. In fact, the other methods compared obtained worse results than those which were not using data augmentation. For the Glomeruli dataset, the method is able to add new samples with different shapes and degrees of sclerosis (through different textures). This is the case where our proposed DA method is more beneficial, when objects highly differ in both shape and texture. Finally, for the Pollen dataset, since there are only small variations between samples in a few classes and this dataset has other features such as noise which are likely to benefit other existing DA techniques, the method still shows an improvement of the results.
User Model-Based Personalized Recommendation Algorithm for News Media Education Resources

Traditional recommendations for news and media education resources usually ignore the importance of sequential patterns in user check-in behavior and fail to effectively capture the complex and dynamically changing interests of users. As a result, this study provides a recommendation model for news and media education materials based on a user model. To capture changes in users’ interests, the model can represent and fuse short-term and long-term preferences separately. For short-term preferences, a long- and short-term memory network incorporating spatiotemporal contextual information is proposed to learn complex sequential transfer patterns in users’ check-in behaviors and further extract short-term preferences accurately through a goal-based attention mechanism. A user attention-based approach is utilized to capture fine-grained links between users and interest points for long-term preferences. Finally, experimental simulations are conducted on two datasets, Foursquare and Gowalla. The results show that the proposed user model-based recommendation model for news media education resources has better performance compared with the mainstream recommendation methods on different evaluation criteria, which validates the effectiveness of the proposed model.
Realization of Tunable Highly-Efficient Quantum Routing in Chiral Waveguides

The chiral interaction between single photons in waveguides and quantum emitters has gained considerable attention. Here, we proposed a tunable quantum routing scheme with a chiral quantum system by coupling an emitter to two chiral waveguides. Conventional quantum routers can only be achieved with each port output probability no larger than 25 % . But our scheme can transfer quantum information arbitrarily from an input port to another, and each port’s output probability is 100 % . Besides, we investigated the influence of the Purcell factor in quantum routing properties. No matter how to change the size of the directionalities S j or set a specific value to the dissipation of the emitter, we always found that the quantum routing has very high efficiency. Moreover, we also used a superconducting qubit coupled to two resonators to show the present scheme is pretty feasible for experimental implementation.
Neural Topic Modeling with Deep Mutual Information Estimation

The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models are difficult to retain representative information of the documents within the learnt topic representation. Fortunately, Deep Mutual Information Estimation (DMIE), which maximizes the mutual information between input data and the hidden representations to learn a good representation of the input data. DMIE provides a new paradigm for neural topic modeling. In this paper, we propose a neural topic model which incorporates deep mutual information estimation, i.e., Neural Topic Modeling with Deep Mutual Information Estimation (NTM-DMIE). NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation. To learn robust topic representation, we incorporate the discriminator to discriminate negative examples and positive examples via adversarial learning. Moreover, we use both global and local mutual information to preserve the rich information of the input documents in the topic representation. We evaluate NTM-DMIE on several metrics, including accuracy of text clustering, with topic representation, topic uniqueness and topic coherence. Compared to the existing methods, the experimental results show that NTM-DMIE can outperform in all the metrics on the four datasets.
Curtain-type phase unwrapping algorithm

A curtain-type phase unwrapping algorithm is proposed. First, the 2 × 2 closed curve method is used to find out the residual point of the wrapped phase to form the residual template, and the Otsu threshold method is used to binary the modulation of the deformed pattern to form the shadow template. Second, the effective phases of up-down symmetrical points about the starting point are simultaneously unwrapped in turn until all the points in the column are completed. Third, the starting point is taken as the center of left-right symmetry, the effective phases of the nearest symmetric points are simultaneously unwrapped, and the corresponding columns are unwrapped in the same way as above. Fourth, in this way, the effective phases in the corresponding symmetric columns are successively unwrapped in the form of curtain opening until the whole phases are completely unwrapped. In the previous procedure, the shadow template and residual template guide the curtain-type phase unwrapping to avoid error diffusion. Finally, the 8-neighborhood mean algorithm and the cubic b-spline algorithm are employed to unwrap the phase values of residual points and shadow areas, respectively. The proposed method realizes the whole phase unwrapping without phase error diffusion. Experimental results show that the efficiency of the proposed method is 26% higher than that of the diamond algorithm, and its accuracy is significantly improved.
A Hybrid Method for 3D Reconstruction of MR Images

Three-dimensional surface reconstruction is a well-known task in medical imaging. In procedures for intervention or radiation treatment planning, the generated models should be accurate and reflect the natural appearance. Traditional methods for this task, such as Marching Cubes, use smoothing post processing to reduce staircase artifacts from mesh generation and exhibit the natural look. However, smoothing algorithms often reduce the quality and degrade the accuracy. Other methods, such as MPU implicits, based on adaptive implicit functions, inherently produce smooth 3D models. However, the integration in the implicit functions of both smoothness and accuracy of the shape approximation may impact the precision of the reconstruction. Having these limitations in mind, we propose a hybrid method for 3D reconstruction of MR images. This method is based on a parallel Marching Cubes algorithm called Flying Edges (FE) and Multi-level Partition of Unity (MPU) implicits. We aim to combine the robustness of the Marching Cubes algorithm with the smooth implicit curve tracking enabled by the use of implicit models in order to provide higher geometry precision. Towards this end, the regions that closely fit to the segmentation data, and thus regions that are not impacted by reconstruction issues, are first extracted from both methods. These regions are then merged and used to reconstruct the final model. Experimental studies were performed on a number of MRI datasets, providing images and error statistics generated from our results. The results obtained show that our method reduces the geometric errors of the reconstructed surfaces when compared to the MPU and FE approaches, producing a more accurate 3D reconstruction.
“Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review

Sentiment Analysis (SA) is a novel branch of Natural Language Processing (NLP) that measures emotions or attitudes behind a written text. First applications of SA in healthcare were the detection of disease-related emotional polarities in social media. Now it is possible to extract more complex attitudes (rank attitudes from 1 to 5, assign appraisal values, apply multiple text classifiers) or feelings through NLP techniques, with clear benefits in cardiology; as emotions were proved to be veritable risk factors for the development of cardiovascular diseases (CVD). Our narrative review aimed to summarize the current directions of SA in cardiology and raise the awareness of cardiologists about the potentiality of this novel domain. This paper introduces the readers to basic concepts surrounding medical SA and the need for SA in cardiovascular healthcare. Our synthesis of the current literature proved SA's clinical potential in CVD. However, many other clinical utilities, such as the assessment of emotional consequences of illness, patient-physician relationship, physician intuitions in CVD are not yet explored. These issues constitute future research directions, along with proposing detailed regulations, popularizing health social media among elders, developing insightful definitions of emotional polarity, and investing research into the development of powerful SA algorithms.
Survey on Efficient Training of Large Neural Networks

Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models don’t fit one GPU device or can be trained using only a small per-GPU batch size. This survey provides a systematic overview of the approaches that enable more efficient DNNs training. We analyze techniques that save memory and make good use of computation and communication resources on architectures with a single or several GPUs. We summarize the main categories of strategies and compare strategies within and across categories. Along with approaches proposed in the literature, we discuss available implementations.
Effect of Deep Learning on College Students’ Career Planning

It is difficult for college students to find jobs after graduation, which is the most important problem to be solved now. This paper chooses the statistical analysis method to analyze the career planning of college students under different circumstances. Four aspects are analyzed, which are decision-making action, current situation evaluation, career exploration, and self-understanding level. The main conclusions of this paper are as follows. In this study, the gender differences of college students have a certain impact on their career. Generally speaking, the career planning level of boys is higher than that of girls. The job-hunting needs of college graduates are students who enter social work. Family factors affect the level of college students’ career planning. It is found that students’ school experience is the most important factor affecting the level of career planning, and school experience is also reflected in whether students have class committee experience.
Pair-wise aspect and opinion terms extraction as graph parsing via a novel mutually-aware interaction mechanism

The pair-wise aspect and opinion term extraction (PAOTE) task aims to extract aspect terms and opinion terms from reviews in the form of opinion pairs, which provides a global profile for reviews of goods or users. Up-to-date studies ignore the interaction between term detection and term pairing, which may be crucial for the PAOTE task. Other studies use syntactic dependency structures to enhance their models, which cannot better provide task-specific structural information. In this work, we design an aspect-to-opinion graph and transform PAOTE into a graph parsing task. To exploit the interaction between term detection and pairing, we propose a novel mutually-aware interaction network (MAIN), which interactively updates the representations for term detection and pairing via graph sampling and convolution. Further, the word-word graph learned during training can be iteratively refined and gradually approaches the aspect-to-opinion graph. Experimental results on four benchmark datasets show that our proposed method significantly outperforms strong baselines with state-of-the-art performance and achieves a maximum increase of 2.01 points on the F1 metric. Further analysis demonstrates the advance of the aspect-to-opinion graph and the effectiveness of the mutually-aware interaction mechanism.
Some remarks on activation function design in complex extreme learning using Schwarz lemma

Processing of complex valued data has become a challenge issue in classification problems where artificial neural networks are used as the classifier. This issue particularly arises in design of complex valued activation functions. To address this problem, a complex valued activation function which is obtained by using Schwarz lemma is proposed in this study and a complex-valued extreme learning classifier is utilized to analyse its classification performance. Accordingly, three inequalities have been presented first by considering the different versions of the boundary Schwarz lemma for Nα class and then, the proposed activation function has been obtained by performing extremal analyses of these inequalities. During simulations, complex extreme learning machine has been used to compare the classification performances of the proposed and other frequently-used activation functions. In classification step, three multi-class and four binary-class datasets have been utilized. In addition, proposed activation function has been considered for two exemplary function approximation problems. According to simulation results, proposed activation function outperforms other activation functions in term of classification accuracy for all considered datasets. It has also been observed that the proposed activation function gives a lower root mean square error than other trigonometric functions in function approximation problem.
Automatic generation of the complete vocal tract shape from the sequence of phonemes to be articulated

Articulatory speech synthesis requires generating realistic vocal tract shapes from the sequence of phonemes to be articulated. This work proposes the first model trained from rt-MRI films to automatically predict all of the vocal tract articulators' contours. The data are the contours tracked in the rt-MRI database recorded for one speaker. Those contours were exploited to train an encoder–decoder network to map the sequence of phonemes and their durations to the exact gestures performed by the speaker. Different from other works, all the individual articulator contours are predicted separately, allowing the investigation of their interactions. We measure four tract variables closely coupled with critical articulators and observe their variations over time. The test demonstrates that our model can produce high-quality shapes of the complete vocal tract with a good correlation between the predicted and the target variables observed in rt-MRI films, even though the tract variables are not included in the optimization procedure.
InterCriteria Analysis Applied on Air Pollution Influence on Morbidity

Human health is reflected in all spheres of life and the economy. One of the main causes of morbidity and early mortality is polluted air. Ambient air pollution is a serious source of disease and mortality across the world. Cities are notorious for their high levels of air pollution and sickness. However, the precise degree of the health impacts of air pollution at the municipal level are still largely unclear. One of the main reasons for increased morbidity is the presence of particulate matter. The aim of our study is to show the relationship between elevated levels of particulate matter in the air and certain diseases. In this paper, we apply InterCriteria Analysis (ICrA) to find the correlation between the level of air pollution and the number of people seeking medical help. This is a new approach for the problem. The results show the affect of air pollution on certain diseases with a short exposure on polluted air and when the exposure is prolonged. We observed that some diseases are exacerbated by brief exposure to polluted air, while in others, exacerbation occurs after prolonged exposure.