Reviewerly/exHarmony huggingface dataset:
A generic deep learning framework to classify thyroid and breast lesions in ultrasound images

Breast and thyroid cancers are the two common cancers to affect women worldwide. Ultrasonography (US) is a commonly used non-invasive imaging modality to detect breast and thyroid cancers, but its clinical diagnostic accuracy for these cancers is controversial. Both thyroid and breast cancers share some similar high frequency ultrasound characteristics such as taller-than-wide shape ratio, hypo-echogenicity, and ill-defined margins. This study aims to develop an automatic scheme for classifying thyroid and breast lesions in ultrasound images using deep convolutional neural networks (DCNN). In particular, we propose a generic DCNN architecture with transfer learning and the same architectural parameter settings to train models for thyroid and breast cancers (TNet and BNet) respectively, and test the viability of such a generic approach with ultrasound images collected from clinical practices. In addition, the potentials of the thyroid model in learning the common features and its performance of classifying both breast and thyroid lesions are investigated. A retrospective dataset of 719 thyroid and 672 breast images captured from US machines of different makes between October 2016 and December 2018 is used in this study. Test results show that both TNet and BNet built on the same DCNN architecture have achieved good classification results (86.5% average accuracy for TNet and 89% for BNet). Furthermore, we used TNet to classify breast lesions and the model achieves sensitivity of 86.6% and specificity of 87.1%, indicating its capability in learning features commonly shared by thyroid and breast lesions. We further tested the diagnostic performance of the TNet model against that of three radiologists. The area under curve (AUC) for thyroid nodule classification is 0.861 (95% CI: 0.792–0.929) for the TNet model and 0.757–0.854 (95% CI: 0.658–0.934) for the three radiologists. The AUC for breast cancer classification is 0.875 (95% CI: 0.804–0.947) for the TNet model and 0.698–0.777 (95% CI: 0.593–0.872) for the radiologists, indicating the model’s potential in classifying both breast and thyroid cancers with a higher level of accuracy than that of radiologists.
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-identification

Unsupervised domain adaptive person Re-IDentification (ReID) is challenging because of the large domain gap between source and target domains, as well as the lackage of labeled data on the target domain. This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. The local one-hot classification assigns images in a training batch with different person IDs, then adopts a Self-Adaptive Classification (SAC) model to classify them. The global multi-class classification is achieved by predicting labels on the entire unlabeled training set with the Memory-based Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering both visual similarity and temporal consistency to ensure the quality of label prediction. The two classification models are combined in a unified framework, which effectively leverages the unlabeled data for discriminative feature learning. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks. For example, under unsupervised setting, our method outperforms recent unsupervised domain adaptive methods, which leverage more labels for training.
Image-Based Table Recognition: Data, Model, and Evaluation

Important information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g. Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style. To facilitate image-based table recognition with deep learning, we develop and release the largest publicly available table recognition dataset PubTabNet ( https://github.com/ibm-aur-nlp/PubTabNet .), containing 568k table images with corresponding structured HTML representation. PubTabNet is automatically generated by matching the XML and PDF representations of the scientific articles in PubMed Central™ Open Access Subset (PMCOA). We also propose a novel attention-based encoder-dual-decoder (EDD) architecture that converts images of tables into HTML code. The model has a structure decoder which reconstructs the table structure and helps the cell decoder to recognize cell content. In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric. The experiments demonstrate that the EDD model can accurately recognize complex tables solely relying on the image representation, outperforming the state-of-the-art by 9.7% absolute TEDS score.
Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.
Post-Quantum TLS Without Handshake Signatures

We present KEMTLS, an alternative to the TLS 1.3 handshake that uses key-encapsulation mechanisms (KEMs) instead of signatures for server authentication. Among existing post-quantum candidates, signature schemes generally have larger public key/signature sizes compared to the public key/ciphertext sizes of KEMs: by using an IND-CCA-secure KEM for server authentication in post-quantum TLS, we obtain multiple benefits. A size-optimized post-quantum instantiation of KEMTLS requires less than half the bandwidth of a size-optimized post-quantum instantiation of TLS 1.3. In a speed-optimized instantiation, KEMTLS reduces the amount of server CPU cycles by almost 90% compared to TLS 1.3, while at the same time reducing communication size, reducing the time until the client can start sending encrypted application data, and eliminating code for signatures from the server's trusted code base.
Conditional variance penalties and domain shift robustness

Abstract When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can divide latent features into (i) ‘core’ or ‘conditionally invariant’ features $$C$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> whose distribution $$C\vert Y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>C</mml:mi><mml:mo>|</mml:mo><mml:mi>Y</mml:mi></mml:mrow></mml:math> , conditional on the class Y , does not change substantially across domains and (ii) ‘style’ features $$S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi></mml:math> whose distribution $$S\vert Y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi><mml:mo>|</mml:mo><mml:mi>Y</mml:mi></mml:mrow></mml:math> can change substantially across domains. Examples for style features include position, rotation, image quality or brightness but also more complex ones like hair color, image quality or posture for images of persons. Our goal is to minimize a loss that is robust under changes in the distribution of these style features. In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable. We do assume that we can sometimes observe a typically discrete identifier or “ $$\mathrm {ID}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ID</mml:mi></mml:math> variable”. In some applications we know, for example, that two images show the same person, and $$\mathrm {ID}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ID</mml:mi></mml:math> then refers to the identity of the person. The proposed method requires only a small fraction of images to have $$\mathrm {ID}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ID</mml:mi></mml:math> information. We group observations if they share the same class and identifier $$(Y,\mathrm {ID})=(y,\mathrm {id})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>Y</mml:mi><mml:mo>,</mml:mo><mml:mi>ID</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mo>(</mml:mo><mml:mi>y</mml:mi><mml:mo>,</mml:mo><mml:mi>id</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> and penalize the conditional variance of the prediction or the loss if we condition on $$(Y,\mathrm {ID})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>Y</mml:mi><mml:mo>,</mml:mo><mml:mi>ID</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> . Using a causal framework, this conditional variance regularization (C o R e ) is shown to protect asymptotically against shifts in the distribution of the style variables in a partially linear structural equation model. Empirically, we show that the C o R e penalty improves predictive accuracy substantially in settings where domain changes occur in terms of image quality, brightness and color while we also look at more complex changes such as changes in movement and posture.
Fast Video Object Segmentation Using the Global Context Module

We developed a real-time, high-quality semi-supervised video object segmentation algorithm. Its accuracy is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest template-matching method with sub-optimal accuracy. The core component of the model is a novel global context module that effectively summarizes and propagates information through the entire video. Compared to previous approaches that only use one frame or a few frames to guide the segmentation of the current frame, the global context module uses all past frames. Unlike the previous state-of-the-art space-time memory network that caches a memory at each spatio-temporal position, the global context module uses a fixed-size feature representation. Therefore, it uses constant memory regardless of the video length and costs substantially less memory and computation. With the novel module, our model achieves top performance on standard benchmarks at a real-time speed.
American sign language recognition and training method with recurrent neural network

Though American sign language (ASL) has gained recognition from the American society, few ASL applications have been developed with educational purposes. Those designed with real-time sign recognition systems are also lacking. Leap motion controller facilitates the real-time and accurate recognition of ASL signs. It allows an opportunity for designing a learning application with a real-time sign recognition system that seeks to improve the effectiveness of ASL learning. The project proposes an ASL learning application prototype. The application would be a whack-a-mole game with a real-time sign recognition system embedded. Since both static and dynamic signs (J, Z) exist in ASL alphabets, Long-Short Term Memory Recurrent Neural Network with k-Nearest-Neighbour method is adopted as the classification method is based on handling of sequences of input. Characteristics such as sphere radius, angles between fingers and distance between finger positions are extracted as input for the classification model. The model is trained with 2600 samples, 100 samples taken for each alphabet. The experimental results revealed that the recognition rate for 26 ASL alphabets yields an average of 99.44% accuracy rate and 91.82% in 5-fold cross-validation with the use of leap motion controller.
A novel stochastic fractal search algorithm with fitness-Distance balance for global numerical optimization

Stochastic Fractal Search (SFS) is a new and original meta-heuristic search (MHS) algorithm with strong foundations. As with many other MHS methods, there are problems in effectively balancing the exploitation-exploration in the SFS algorithm. In order to achieve this balance, it is necessary to improve its diversity capability. This article presents the studies that were carried out to strengthen the diversity and balanced search capabilities of the SFS algorithm. For this purpose, the diversity operator of the SFS algorithm was designed with a novel method called Fitness-Distance Balance (FDB), which more effectively mimics the way fractals occur in nature. Thus, the FDBSFS algorithm, which has a much stronger search performance, was developed. Comprehensive experimental studies were conducted to test and validate the developed FDB-based SFS algorithm (FDBSFS). Thirty-nine novel and powerful MHS algorithms, eighty-nine unconstrained test functions and five constrained engineering problems were used. Two nonparametric tests, the Wilcoxon signed rank test and the Friedman test, were used to analyze the results obtained from the experimental studies. The results of the analysis showed that the problem of premature convergence had been largely eliminated by the application of the FDB method and that the exploitation-exploration balance was also effectively provided. Moreover, the proposed FDBSFS algorithm ranked first among the thirty-nine competing algorithms.
3D PBV-Net: An automated prostate MRI data segmentation method

Prostate cancer is one of the most common deadly diseases in men worldwide, which is seriously affecting people's life and health. Reliable and automated segmentation of the prostate gland in MRI data is exceptionally critical for diagnosis and treatment planning of prostate cancer. Although many automated segmentation methods have emerged, including deep learning based approaches, segmentation performance is still poor due to the large variability of image appearance, anisotropic spatial resolution, and imaging interference. This study proposes an automated prostate MRI data segmentation approach using bicubic interpolation with improved 3D V-Net (dubbed 3D PBV-Net). Considering the low-frequency components in the prostate gland, the bicubic interpolation is applied to preprocess the MRI data. On this basis, a 3D PBV-Net is developed to perform prostate MRI data segmentation. To illustrate the effectiveness of our approach, we evaluate the proposed 3D PBV-Net on two clinical prostate MRI data datasets, i.e., PROMISE 12 and TPHOH, with the manual delineations available as the ground truth. Our approach generates promising segmentation results, which have achieved 97.65% and 98.29% of average accuracy, 0.9613 and 0.9765 of Dice metric, 3.120 mm and 0.9382 mm of Hausdorff distance, and average boundary distance of 1.708, 0.7950 on PROMISE 12 and TPHOH datasets, respectively. Our method has effectively improved the accuracy of automated segmentation of the prostate MRI data and is promising to meet the accuracy requirements for telehealth applications.
Motivation, engagement, and performance across multiple virtual reality sessions and levels of immersion

Abstract This study investigated changes in learners' motivation, engagement, performance, and spatial reasoning over time and across different levels of virtual reality (VR) immersion. Undergraduate participants explored a virtual solar system via a moderately immersive or highly immersive VR platform over three sessions. In a third condition, participants initially learned with moderate immersion and transitioned to higher immersion after the second session. Following research on novelty effects, we explored whether subjective experiences and performance would decline over time (e.g., decreasing motivation or performance) as participants became familiar with the virtual environment and tools. However, we hypothesized that transitional immersion (i.e., switching from moderate to higher immersion) might lead to a renewed sense of novelty. Results suggested that both moderate and higher levels of immersion were motivating, engaging, and supportive of learning. In contrast to predictions based on novelty effects, these outcomes did not decline overall as learners gained familiarity with the systems. However, transitional immersion emerged as a promising and testable pedagogical approach for future VR education. All participants also showed gains in spatial reasoning.
Face mask detection using MobileNet and Global Pooling Block

Coronavirus disease is the latest epidemic that forced an international health emergency. It spreads mainly from person to person through airborne transmission. Community transmission has raised the number of cases over the world. Many countries have imposed compulsory face mask policies in public areas as a preventive action. Manual observation of the face mask in crowded places is a tedious task. Thus, researchers have motivated for the automation of face mask detection system. In this paper, we have presented a MobileNet with a global pooling block for face mask detection. The proposed model employs a global pooling layer to perform a flatten of the feature vector. A fully connected dense layer associated with the softmax layer has been utilized for classification. Our proposed model outperforms existing models on two publicly available face mask datasets in terms of vital performance metrics.
Virtual reality for fire safety training: Influence of immersion and sense of presence on conceptual and procedural acquisition

A fire can have serious economic and human consequences. However, in many cases, rapid intervention and appropriate behavior can significantly reduce this threat. For this it is important that people are properly trained. Faced with the economic, ecological and organizational requirements and constraints linked to fire safety training, virtual training environments appear to be a judicious alternative to traditional training. However, before committing companies to invest in expensive devices, it is necessary to ensure the effectiveness of such devices. The literature is rich and divided on this subject, and it appears that certain characteristics of the system and of the learners are decisive. In this context, the objective of our work was to study the effects of immersion, the feeling of presence as well as the interaction between these 2 factors on the performance of conceptual and procedural learning. Certain variables were also controlled such as gender, tendency to immersion, previous experience in computers and video games. Our work shows that immersion promotes procedural but not conceptual learning, and that neither the sense of presence, nor the interaction between immersion and the sense of presence affect these two types of learning in our training task. Apart from the knowledge of fire which potentiates the effect of immersion on procedural learning, the variables considered in our study (computer experience, video game experience and genre) had no impact on performance. Understanding the impact of the technical device or the characteristics of users on conceptual and procedural learning is a major challenge in helping trainers to develop pedagogical devices in order to better exploit the opportunities offered by new technological approaches.
A blockchain-based code copyright management system

With the increasing number of open-source software projects, code plagiarism has become one of the threats to the software industry. However, current research on code copyright protection mostly focuses on the approach for code plagiarism detection, failing to fundamentally solve the problem of copyright confirmation and protection. This paper proposes a blockchain-based code copyright management system. Firstly, an Abstract Syntax Tree-based code originality verification model is constructed. The originality of the uploaded code is determined through its similarity to other original codes. Secondly, the Peer-to-Peer blockchain network is designed to store the copyright information of the original code. The nodes in the blockchain network can verify the originality of the code based on the code originality verification model. Then, through the construction of blocks and legitimacy validation and linking of blocks, the blockchain-based code copyright management structure is built. The whole process guarantees that the copyright information is traceable and will not be tampered with. According to the experiments, the accuracy and processing time of the code originality verification model are shown to meet code originality verification requirements. The experiment also shows that the best storage type of the code copyright information is the code fingerprint which is a 256bits hash value converted from code eigenvalues. It performs better in both response speed and storage efficiency. Moreover, because of the uniqueness and irreversibility of the result from the SHA256 algorithm, the code fingerprint storage yields a better level of storage security. In summary, this paper proposes a blockchain-based code copyright management system which provides better response speed and storage efficiency.
A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.