Reviewerly/exHarmony huggingface dataset:
A deep neural network learning‐based speckle noise removal technique for enhancing the quality of synthetic‐aperture radar images

Abstract The speckle noise present in synthetic‐aperture radar (SAR) images is responsible for hindering the extraction of the exact information that needs to be utilized for potential remote sensing applications. Thus the quality of SAR images needs to be enhanced by removing speckle noise in an effective manner. In this paper, A Deep Neural Network‐based Speckle Noise Removal Technique (DNN‐SNRT) is proposed that utilizes the benefits of convolution and Long Short Term Memory‐based neural networks to enhance the quality of SAR images. The proposed DNN‐SNRT uses multiple radar intensity images that are archived from the specific area of interest to facilitate the self‐learning of the intensity features derived from the image patches. The proposed DNN‐SNRT incorporates a dual neural network to remove speckle noise and flexibly estimates the thresholds and weights to achieve an effective SAR image quality improvement. The proposed DNN‐SNRT is capable of automatically updating the intensity features of SAR images during the training process. Experimental investigation of the proposed DNN‐SNRT conducted based on TerraSAR‐X images confirmed the superior enhancement of image quality over comparable recent filters. The results of the DNN‐SNRT scheme were also proved that it is able to reduce noise and preserve edges during the image quality enhancement process.
BPAF: Blockchain-Enabled Reliable and Privacy-Preserving Authentication for Fog-Based IoT Devices

The development of IoT and fog computing promotes various kinds of authentication mechanisms for IoT devices. Traditional IoT authentication schemes are based on Public Key Infrastructure (PKI) where a centralized certificate authority is introduced. To mitigate the security, privacy, and reliability issues bring from the centralization, some blockchain-based authentication schemes have been presented to achieve decentralized authentication. Unfortunately, they cannot be directly used under the fog-based IoT environment, which consists of resource-constrained IoT devices. To mitigate these issues, we present a Blockchain-enabled reliable, and Privacy-preserving Authentication for Fog-based IoT devices, named BPAF. BPAF achieves reliable authentication of fog nodes without violating the privacy of authenticated users during the authentication process. Security analysis and experimental evaluations show that BPAF achieves privacy-preserving and reliable authentication with high efficiency for both the fog nodes and full nodes participating in the authentication process.
Cognitive and Socioaffective Predictors of L2 Microdevelopment in Late Adulthood: A Longitudinal Intervention Study

Abstract The question of cognition in second language (L2) acquisition later in life is of importance inasmuch as L2 learning is largely mediated by domain‐general cognitive capacities. While a number of these capacities have been shown to decline with age, individual differences in cognition increase over the lifespan. This microdevelopment study investigates the L2 trajectories of 28 older German‐speaking adults (age 64+) who participated in a combined computer‐assisted and classroom‐instructed 7‐month Spanish training for beginners. We made use of generalized additive mixed models (GAMMs) to quantify linear and nonlinear learner trajectories as well as any predictors thereof. Participants were assessed on a range of behavioral, L2, socioaffective, and background variables. We found a significant (linear and nonlinear) increase across all measures of L2 proficiency. Between‐subject cognitive, socioaffective, and background variables significantly predicted the overall level of L2 proficiency as well as developmental patterns over time. Daily variances in cognitive performance and socioaffect had little impact on fluctuations in L2 performance. Findings are discussed against the backdrop of complex dynamic systems theory and highlight the necessity for dense longitudinal research designs to capture nonlinearity in third‐age L2 learning.
Towards Smart Blockchain-Based System for Privacy and Security in a Smart City environment

Nowadays, the digitalization of urban environments is redefining the public and private sectors. Moreover, Internet of Things (IoT) platforms, cloud computing infrastructure and smart devices are exchanging tremendous amount of data. This harmonious integration of the cyber capabilities of the corresponding devices with the physical world generates new opportunities in many areas; however it raises a lot of security and privacy challenges due to the diversity of sources and stakeholders, the centralized data management and the resulting lack of trust and governance. Hence, we introduce "SmartPrivChain" a Smart Blockchain Based System for preserving privacy and security in a smart city environment. The proposed scheme is different from the existing approaches on many points. The data privacy is preserved by combining data access control and data usage auditing measures based on smart contracts. In addition, the proposed solution is compliant with the main privacy laws and regulations especially the obligations of the European Union General Data Protection Regulation (GDPR). Lastly, we propose an enhanced Proof of Reputation (PoR) consensus scheme using a multidimensional Trust model.
A General Region Nesting-Based Semi-Fragile Reversible Watermarking for Authenticating 3D Mesh Models

Aiming to reduce the distortion in the existing reversible watermarking for 3D mesh models, this paper generalizes two-dimensional (2D) region nesting to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -dimensional space, and proposes a general region nesting based semi-fragile reversible watermarking for authenticating 3D mesh models by combing watermark generation based on vertex projection and mesh topology. For any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -dimensional space, each hypercube space is partitioned into several nested watermark sub-spaces, and the watermark is embedded by placing the original vertex and the mapped vertex on a straight line. The watermark is extracted by acquiring the position of the vertices, and its original coordinates can be restored by inverse mapping. Furthermore, a new coordinate system is built for 3D mesh models based on reference vertices to achieve semi-fragility, and the watermarks generated by vertex projection and mesh topology is utilized for integrity authentication. Experimental results and analysis show that the proposed scheme can significantly reduce the distortion, achieve semi-fragility in RST (Rotation, Scaling and Translation) and vertex reordering, identify the tampering type, and accomplish tampering location at vertex level. It has great potential to be applied for content authentication of 3D mesh models.
Accurate Recognition of Player Identity and Stroke Performance in Table Tennis Using a Smart Wristband

In table tennis, a 40 mm in diameter table tennis ball weighing 2.7g can reach the opponent's table in a very short time when it travels at a speed of 17m/s. That is difficult for new players to hit accurately. The purpose of this work is to recognize hits and misses by distinguishing the action difference between hitting and missing table tennis. The result of recognition can be used to figure out a small deviation from the correct posture and improve the hitting accuracy. Six volunteers participated in the experiment. The volunteers wore wristband sensors on the wrist on which they held the table tennis bat. Each volunteer played the ball 100 times and collected the information through a wristband sensor. The collected information was analyzed by support vector machine (SVM), decision tree (CART), linear discriminant analysis (LDA), K nearest neighbor (KNN), and Naive Bayes (NB) to identify the identities of volunteers and the hit or miss of the ball. The accuracy rate of volunteers' identity recognition is 99%, and the accuracy rate of hits and misses is 95%. The results show that the wristband sensor can accurately identify the volunteers' identities and missing cases through appropriate classification methods. This shows that we can find out the nuances between the stroke postures by hitting or missing the ball, and then use the results to correct the movement of the players, and thus improve the players' skill.
Improved Artificial Potential Field and Dynamic Window Method for Amphibious Robot Fish Path Planning

Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.
Active Learning for Biomedical Text Classification Based on Automatically Generated Regular Expressions

Biomedical text classification algorithms, which currently support clinical decision-making processes, call for expensive training texts due to the low availability of labeled corpus and the cost of manual annotation by specialized professionals.The active learning (AL) approach to classification heavily lessens such cost by reducing the number of labeled documents required to achieve specified performance.This article introduces a query strategy and a stopping criterion that transform CREGEX, a regular-expressionsbased text classification algorithm, in an AL biomedical text classifier.The query strategy samples the training dataset, trading off the greedy learning achieved by the regular expressions classification precision and the conservative learning induced by text sequence alignment classification.The sustained reduction in the variance of the query strategy scores is used as a stopping criterion.The AL classifier was compared with Support Vector Machine (SVM), Naïve Bayes (NB), and a classifier based on Bidirectional Encoder Representations from Transformers (BERT), using three datasets with biomedical information in Spanish on smoking habits, obesity, and obesity types.The learning curve results indicate that AL in CREGEX allowed to efficiently reduce the number of training examples for equal performance than the rest of the classifiers, obtaining areas under the learning curve greater than 85% in all cases.The stopping criterion applied to the AL process allowed to use, on average, approximately 32% to 50% of the total training examples with differences in performance concerning the maximum value of the learning curve not exceeding 2%.This performance demonstrates the effectiveness of using AL in a biomedical text classifier based on regular expressions, which is attributable to such expressions' ability to represent intricate sequential patterns in training texts considered most informative.
Accent modification for speech recognition of non-native speakers using neural style transfer

Abstract Nowadays automatic speech recognition (ASR) systems can achieve higher and higher accuracy rates depending on the methodology applied and datasets used. The rate decreases significantly when the ASR system is being used with a non-native speaker of the language to be recognized. The main reason for this is specific pronunciation and accent features related to the mother tongue of that speaker, which influence the pronunciation. At the same time, an extremely limited volume of labeled non-native speech datasets makes it difficult to train, from the ground up, sufficiently accurate ASR systems for non-native speakers.In this research, we address the problem and its influence on the accuracy of ASR systems, using the style transfer methodology. We designed a pipeline for modifying the speech of a non-native speaker so that it more closely resembles the native speech. This paper covers experiments for accent modification using different setups and different approaches, including neural style transfer and autoencoder. The experiments were conducted on English language pronounced by Japanese speakers ( UME-ERJ dataset). The results show that there is a significant relative improvement in terms of the speech recognition accuracy. Our methodology reduces the necessity of training new algorithms for non-native speech (thus overcoming the obstacle related to the data scarcity) and can be used as a wrapper for any existing ASR system. The modification can be performed in real time, before a sample is passed into the speech recognition system itself.
HistoNet: A Deep Learning-Based Model of Normal Histology

We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small 224 × 224 pixels images (patches) at 6 different levels of magnification. Using 4 studies as training set and 2 studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each magnification level. Among these model architectures, Inception-v3 and ResNet-50 outperformed VGG-16. Inception-v3 identified the tissue from query images, with an accuracy up to 83.4%. Most misclassifications occurred between histologically similar tissues. Investigation of the features learned by the model (embedding layer) using Uniform Manifold Approximation and Projection revealed not only coherent clusters associated with the individual tissues but also subclusters corresponding to histologically meaningful structures that had not been annotated or trained for. This suggests that the histological representation learned by HistoNet could be useful as the basis of other machine learning algorithms and data mining. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining.
CEEP-FL: A comprehensive approach for communication efficiency and enhanced privacy in federated learning

Federated Learning (FL) is an emerging technique for collaboratively training machine learning models on distributed data under privacy constraints. However, recent studies have shown that FL significantly consumes plenty of communication resources during the global model update. In addition, participants' private data can also be compromised by exploiting the shared parameters when uploading the local gradient updates to the central cloud server, which hinders FL to be implemented widely. To address these challenges, in this paper, we propose a novel comprehensive FL approach, namely, Communication Efficient and Enhanced Privacy (CEEP-FL). In particular, the proposed approach simultaneously aims to; (1) minimize the communication cost, (2) protect data from being compromised, and (3) maximize the global learning accuracy. To minimize the communication cost, we first apply a novel filtering mechanism on each local gradient update and upload only the important gradients. Then, we apply Non-Interactive Zero-Knowledge Proofs based Homomorphic-Cryptosystem (NIZKP-HC) in order to protect those local gradient updates while maintaining robustness in the network. Finally, we use Distributed Selective Stochastic Gradient Descent (DSSGD) optimization to minimize the computational cost and maximize the global learning accuracy. The experimental results on commonly used FL datasets demonstrate that CEEP-FL distinctively outperforms the existing approaches.
V-RVO: Decentralized Multi-Agent Collision Avoidance using Voronoi Diagrams and Reciprocal Velocity Obstacles

We present a decentralized collision avoidance method for dense environments based on buffered Voronoi cells (BVC) and reciprocal velocity obstacles (RVO). Our approach is designed for scenarios with a large number of agents in close proximity and provides passive-friendly collision avoidance guarantees. The Voronoi cells are superimposed with RVO cones to compute a suitable direction for each agent, and we use that direction to compute a local collision-free path. Our approach can also satisfy double-integrator dynamics, and we use the properties of the BVC to formulate a simple, decentralized deadlock resolution strategy. We demonstrate the benefits of V-RVO in complex scenarios with tens of agents in close proximity. In practice, V-RVO’s performance is comparable to prior velocity-obstacle methods, and the collision avoidance behavior is significantly less conservative than ORCA.
Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications

Augmented Reality (AR) provides an alternative to the traditional forms of interaction between humans and machines, and facilitates the access to certain technologies to groups of people with special needs like children. For instance, in pediatric healthcare, it is important to help children to feel comfortable during medical procedures and tests that may be performed on them. To tackle such an issue with the help of AR-based solutions, this article presents the design, implementation and evaluation of a novel open-source collaborative framework that enables to develop teaching, training, and monitoring pediatric healthcare applications. Specifically, such a framework allows for building collaborative applications and shared experiences for AR devices, providing functionalities for connecting with other AR devices and enabling real-time visualization and simultaneous interaction with virtual objects. Since all the communications involved in AR interactions are handled by AR devices, the proposed collaborative framework is able to operate autonomously through a Local Area Network (LAN), thus requiring no cloud or external servers. In order to demonstrate the potential of the proposed framework, a practical use case application is presented. Such an application has been designed to motivate pediatric patients and to encourage them to increase their physical activity through AR games. The presented games do not require any previous configuration, as they use ARCore automatic surface detection technology. Moreover, the AR mobile gaming framework allows multiple players to engage in the same AR experience, so children can interact and collaborate among them sharing the same AR content. In addition, the proposed AR system provides a remote web application that is able to collect and to visualize data on patient use, aiming to provide healthcare professionals with qualified data about the mobility and mood of their patients through an intuitive and user-friendly web tool. Finally, to determine the performance of the proposed AR system, this article presents its evaluation in terms of latency and processing time. The results show that both times are low enough to provide a good user experience.
A Passion for Security: Intervening to Help Software Developers

While the techniques to achieve secure, privacy-preserving software are now well understood, evidence shows that many software development teams do not use them: they lack the 'security maturity' to assess security needs and decide on appropriate tools and processes; and they lack the ability to negotiate with product management for the required resources. This paper describes a measuring approach to assess twelve aspects of this security maturity; its use to assess the impact of a lightweight package of workshops designed to increase security maturity; and a novel approach within that package to support developers in resource negotiation. Based on trials in eight organizations, involving over 80 developers, this paper demonstrates that (1) development teams can notably improve their security maturity even in the absence of security specialists; and (2) suitably guided, developers can find effective ways to promote security to product management. Empowering developers to make their own decisions and promote security in this way offers a powerful grassroots approach to improving the security of software worldwide.
9.3 A 40nm 4.81TFLOPS/W 8b Floating-Point Training Processor for Non-Sparse Neural Networks Using Shared Exponent Bias and 24-Way Fused Multiply-Add Tree

Recent works on mobile deep-learning processors have presented designs that exploit sparsity [2, 3], which is commonly found in various neural networks. However, due to the shift in the machine learning community towards using non-sparse activation functions such as Leaky ReLU or Swish for better training convergence, state-of-theart models no longer exhibit the sparsity found in conventional ReLU-based models (Fig. 9.3.1, top). Moreover, contrary to error-tolerant image classification tasks, more difficult tasks such as image super-resolution require higher precision than plain 8b integers not just for training, but for inference without large accuracy degradation (Fig. 9.3.1, bottom). These changes offer new challenges faced by mobile deep-learning processors: they must process non-sparse networks efficiently and maintain higher precision for more challenging tasks.