Reviewerly/exHarmony huggingface dataset:
Building Trusted Federated Learning on Blockchain

Federated learning enables multiple users to collaboratively train a global model using the users’ private data on users’ local machines. This way, users are not required to share their training data with other parties, maintaining user privacy; however, the vanilla federated learning proposal is mainly assumed to be run in a trusted environment, while the actual implementation of federated learning is expected to be performed in untrusted domains. This paper aims to use blockchain as a trusted federated learning platform to realize the missing “running on untrusted domain” requirement. First, we investigate vanilla federate learning issues such as client’s low motivation, client dropouts, model poisoning, model stealing, and unauthorized access. From those issues, we design building block solutions such as incentive mechanism, reputation system, peer-reviewed model, commitment hash, and model encryption. We then construct the full-fledged blockchain-based federated learning protocol, including client registration, training, aggregation, and reward distribution. Our evaluations show that the proposed solutions made federated learning more reliable. Moreover, the proposed system can motivate participants to be honest and perform best-effort training to obtain higher rewards while punishing malicious behaviors. Hence, running federated learning in an untrusted environment becomes possible.
Deep learning phase-unwrapping method based on adaptive noise evaluation

To address the problem of phase unwrapping for interferograms, a deep learning (DL) phase-unwrapping method based on adaptive noise evaluation is proposed to retrieve the unwrapped phase from the wrapped phase. First, this method uses a UNet3+ as the skeleton and combines with a residual neural network to build a network model suitable for unwrapping wrapped fringe patterns. Second, an adaptive noise level evaluation system for interferograms is designed to estimate the noise level of the interferograms by integrating phase quality maps and phase residues of the interferograms. Then, multiple training datasets with different noise levels are used to train the DL network to achieve the trained networks suitable for unwrapping interferograms with different noise levels. Finally, the interferograms are unwrapped by the trained networks with the same noise levels as the interferograms to be unwrapped. The results with simulated and experimental interferograms demonstrate that the proposed networks can obtain the popular unwrapped phase from the wrapped phase with different noise levels and show good robustness in the experiments of phase unwrapping for different types of fringe patterns.
Knowledge mapping of research data in China: a bibliometric study using visual analysis

Purpose This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics. Design/methodology/approach The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper. Findings The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality. Originality/value Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.
Custom Hardware Inference Accelerator for TensorFlow Lite for Microcontrollers

In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has been steadily increasing. However, the high computational demand required for Machine Learning (ML) inference on tiny microcontroller-based IoT devices avoids a direct software deployment on such resource-constrained edge devices. Therefore, various custom and application-specific NN hardware accelerators have been proposed to enable real-time Machine Learning (ML) inference on low-power and resource-limited edge devices. Efficient mapping of the computational load onto hardware and software resources is a key challenge for performance improvement while keeping low power and a low area footprint. High performance and yet low power embedded processors may be attained via the usage of hardware acceleration. This paper presents an efficient hardware-software framework to accelerate machine learning inference on edge devices using a modified TensorFlow Lite for Microcontroller (TFLM) model running on a Microcontroller (MCU) and a dedicated Neural Processing Unit (NPU) custom hardware accelerator, referred to as MCU-NPU. The proposed framework supports weight compression of pruned quantized NN models and exploits the pruned model sparsity to reduce computational complexity further. The proposed methodology has been evaluated by employing the MCU-NPU acceleration for various TFLM-based NN architectures using the common MLPerf Tiny benchmark. Experimental results demonstrate a significant speedup of up to 724x compared to a pure software implementation. For example, the resulting runtime for the CIFAR-10 classification is reduced from about 20 sec to only 37 ms using the proposed hardware acceleration. Moreover, the proposed hardware accelerator outperforms all the reference models optimized for edge devices in terms of inference runtime.
An Effective Method for Detection and Recognition of Uyghur Texts in Images with Backgrounds

Uyghur text detection and recognition in images with simple backgrounds is still a challenging task for Uyghur image content analysis. In this paper, we propose a new effective Uyghur text detection method based on channel-enhanced MSERs and the CNN classification model. In order to extract more complete text components, a new text candidate region extraction algorithm is put forward, which is based on the channel-enhanced MSERs according to the characteristics of Uyghur text. In order to effectively prune the non-text regions, we design a CNN classification network according to the LeNet-5, which gains the description characteristics automatically and avoids the tedious and low efficiency artificial characteristic extraction work. For Uyghur text recognition in images, we improved the traditional CRNN network, and to verify its effectiveness, the networks trained on a synthetic dataset and evaluated on the text recognition datasets. The experimental results indicated that the Uyghur text detection method in this paper is robust and applicable, and the recognition result by improvedCRNN was better than the original CRNN network.
Deep image compression based on multi-scale deformable convolution

Deep image compression efficiency has been improved in the past years. However, to fully exploit context information for compressing image objects of different scales and shapes, more adaptive geometric structure of inputs should be considered. In this paper, we novelly introduce deformable convolution and its spatial attention extension into deep image compression task to fully exploit the context information. Specifically, a novel deep image compression network with Multi-Scale Deformable Convolution and Spatial Attention, named MS-DCSA, is proposed to better extract compact and efficient latent representation as well as reconstruct higher-quality images. First, multi-scale deformable convolution is presented to provide multi-scale receptive fields for learning spatial sampling offsets in deformable operations. Subsequently, multi-scale deformable spatial attention module is developed to generate attention masks to re-weight extracted features according to their importance. In addition, the multi-scale deformable convolution is applied to design delicate up/down sampling modules. Extensive experiments demonstrate that the proposed MS-DCSA network achieves improved performance on both PSNR and MS-SSIM quality metrics, compared to conventional as well as competing deep image compression methods.
A Weak-Region Enhanced Bayesian Classification for Spam Content-Based Filtering

This article proposes an improved Bayesian scheme by focusing on the region in which Bayesian may fail to correctly identify labels and improve classification performance by handling those errors. Bayesian method, as a probabilistic classifier, uses Bayes’ theorem to calculate the probability of an instance belonging to a class, where the class label with a maximum probability is assigned to the instance. In a spam detection problem, it can be considered that the prediction of the Bayesian classifier is weak when the probability obtained for classes spam and non-spam are close to each other. Therefore, we define a threshold to determine weak prediction against strong prediction. A hybrid strategy using a two-layer Bayesian approach is presented: basic Bayesian (BBayes) and corrected weak region Bayesian (CWRBayes), which are concerned with strong and weak predictions, respectively. Both techniques, BBayes and CWRBayes, have the same classification mechanism, but they use different feature selection mechanisms. The proposed methods are implemented and evaluated over two datasets of spam e-mails, and the results show that the proposed method has better performance than the baseline of the naïve Bayesian and some other Bayesian variants.
Vision-based fringe projection measurement system for radial vibration monitoring of rotating shafts

• A vision-based fringe projection shaft radial measurement system was proposed. • A novel one-dimensional operation algorithm for the calculation of 2D radial displacements was proposed. • The shaft radial displacements can be simultaneously measured with high accuracy using one camera. Shaft radial vibration signals of rotary machines are crucial dynamic information for condition monitoring and fault diagnosis. A novel vision-based fringe projection vibration measurement system is proposed to monitor the radial displacements of a rotating shaft. The fringe pattern was projected on the surface of the shaft and the deformed fringe was acquired by a high-speed camera. A novel fringe processing algorithm was proposed to extract the radial displacement with good accuracy and efficiency. The vertical displacement could be obtained by locating the symmetric center of the recorded fringe. Meanwhile, the horizontal displacement could be obtained from the shift of the fringe at the symmetric center. The influence factors affecting measurement performance were analyzed. Simulations and comparative experiments were carried out for the verification of the feasibility and reliability of the proposed system. Comparing with the multiple eddy current sensors based system, the proposed method can realize multi-dimensional vibration measurement using one camera. The merits of simple in system, easy in operation, high accuracy and effectiveness in measurement make it attractive for dynamic characterization, condition monitoring and faults diagnosis of rotating machineries in actual applications.
Factors Affecting the Adoption of Remote Auditing During the Times of COVID-19: An Integrated Perspective of Diffusion of Innovations Model and the Technology Acceptance Model

During the COVID-19 pandemic period, external auditors have used technological solutions to complete their job tasks due to the lockdown ramifications. Therefore, many external auditors have opted to work remotely. Nevertheless, the abrupt adoption of remote auditing received mixed feedback from those auditors due to different determining factors. Therefore, this article intends to examine these factors in the Jordanian external auditor context. To do so, a model combining the critical factors from the diffusion of innovation model and technology acceptance model has been utilized. The model was tested using data collected from 109 external auditors from public accounting firms working in Jordan. The results expand our understanding of remote auditing adoption decisions, providing empirical support that perceived usefulness, perceived ease of use, and trialability are the important determinants to adopt remote auditing systems. Based on these results, further discussion, implications, and recommendations have been provided.
Target-driven obstacle avoidance algorithm based on DDPG for connected autonomous vehicles

Abstract In the field of autonomous driving, obstacle avoidance is of great significance for safe driving. At present, in addition to traditional obstacle avoidance algorithms including VFH algorithm, artificial potential field method, a large number of related researches are focused on algorithms based on vision and neural networks. Researches on these algorithms have achieved some results, and some of which have completed real road tests. However, most of algorithms consider only local environmental information which may cause local optimum in complex driving environments. Therefore, it is necessary to consider the environmental information beyond the sensor's perceptual ability for autonomous driving in complex environment. In the network-assisted automated driving system, networked vehicles can obtain road obstacles’ and condition information through roadside sensors and mobile network, so as to gain extra sensing ability. Therefore, network-assisted automated driving is of great significance in obstacle avoidance. Under this background, this paper presents an automatic driving obstacle avoidance strategy combining path planning and reinforcement learning. At first, a global optimal path is planned through global information, then merge the global optimal path and vehicle information into a vector. Use this vector as input of reinforcement learning neural network and output vehicle control signals to follow optimal path while avoiding obstacles.
Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units

Solar energy is a feasible alternative to traditional sources of energy. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the reliable and cost-effective functioning of active distribution systems. Photovoltaic power forecasting is a critical instrument in solar photovoltaic power plants for improving energy delivery quality to the grid and lowering weather-related ancillary expenses. This paper proposes a new hybrid deep learning model called convolutional neural network (CNN) and gated recurrent unit (GRU). Convolutional layers have the potential to learn complicated characteristics from raw data automatically. GRU layers, on the other hand, can learn numerous parallel sequences of input data immediately. The generated prediction model was compared to newly established deep-learning-based algorithms of CNN, GRU, recurrent neural network (RNN), and long short-term memory (LSTM) to test CNN-GRU performance. The photovoltaic power data collected in Alice Springs, Australia, is utilized. In terms of accuracy and efficacy, the values of mean absolute error (MAE), root mean squared error (RMSE), mean bias error (MBE), mean squared error (MSE) and the coefficient of determination (R2) were 0.068 kW, 0.103 kW, 0.032 kW, 0.010 kW, and 99.91%, respectively. The experimental findings show that the proposed model outperforms alternative baseline models.
Target Tracking Subject to Intermittent Measurements Using Attention Deep Neural Networks

This letter presents a novel estimator and predictor framework for target tracking applications that estimates the pose of a mobile target that intermittently leaves the field-of-view (FOV) of a mobile agent’s camera. Specifically, the framework uses an attention deep motion model network (DMMN) to estimate the dynamics of the target when the target is in the agent’s FOV and uses the DMMN to predict the position, orientation, and velocity of the target when the target is outside the agent’s FOV. A Lyapunov-based stability analysis is performed to determine the maximum dwell-time condition on target measurement availability, and experimental results are provided to demonstrate the performance of the proposed framework.
Graph Enhanced Fuzzy Clustering for Categorical Data Using a Bayesian Dissimilarity Measure

Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative relationship among categorical values makes traditional clustering approaches, which are usually developed for numerical data, perform poorly on categorical datasets. To solve this problem and boost the performance of clustering for categorical data, we propose a novel fuzzy clustering model in this article. At first, by approximating the maximum a posteriori (MAP) estimation of a discrete distribution of data partition, a new fuzzy clustering objective function is designed for categorical data. The Bayesian dissimilarity measure is formulated in this objective to tackle the subtle relationships between categorical values efficiently. Then, to further enhance the performance of clustering, a novel Kullback–Leibler divergence-based graph regularization is integrated into the clustering objective to exploit the prior knowledge on datasets, for example, the information about correlations of data points. The proposed model is solved by the alternative optimization and the experimental results on the synthetic and real-world datasets show that it outperforms the classical and relevant state-of-the-art algorithms. We also present the parameter analysis of our approach, and conduct a comprehensive study on the effectiveness of the Bayesian dissimilarity measure and the KL divergence-based graph regularization.
Cognitive Diagnostic Model Made More Practical by Genetic Algorithm

Cognitive diagnosis has attracted increasing attention owing to the flourishing development of online education. As one of the most widely used cognitive diagnostic models, DINA (Deterministic Inputs, Noisy And gate) evaluates students’ knowledge mastery based on their performance of the exercises. However, the traditional DINA model and its variants face the problem of exponential explosion with respect to the number of knowledge components. The running time of these models increases exponentially with the number of knowledge components, limiting their practical use. To make cognitive diagnosis more practical, an effective memetic algorithm composed of a genetic algorithm and a local search operator is applied to DINA to address the exponential explosion problem of the traditional model. Moreover, an improved adaptive local search method without the need of specifying any parameters is proposed to reduce redundant local searches and accelerate the running time. Experiments on real-world datasets demonstrate the effectiveness of the proposed models with respect to both time and accuracy.
Active Few-Shot Learning with FASL

Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train models with good quality. This makes it challenging to quickly develop and deploy new models for real world problems and business needs. Few-shot learning and active learning are two lines of research, aimed at tackling this problem. In this work, we combine both lines into FASL, a platform that allows training text classification models using an iterative and fast process. We investigate which active learning methods work best in our few-shot setup. Additionally, we develop a model to predict when to stop annotating. This is relevant as in a few-shot setup we do not have access to a large validation set.
Reconstruction of wire frame model of complex images using syntactic pattern recognition

Reconstruction of 3D images is always a challenging task for the research community and scientist because 3D image reconstruction describes the quantitative characteristics of the objects i.e., shape, size, texture, and volume. The proposed work represents the wireframe model of 3D reconstruction and introduced a new algorithm to represent three-dimensional images in a textual form. In the proposed work firstly identified the wireframe model and then describes the image characteristics. The syntactic based algorithm extracts the image characteristics in the form of knowledge vectors. The proposed work is having a novelty in form of vector of knowledge that provides a significant contribution in security. In addition, the knowledge vector is used to rebuild the image wireframe model and a single pass is enough for the original image to be reconstructed. In the proposed work two standard publicly available database (PASCAL 3D+ and example-based 3D object arrangements) used for evaluation of the proposed work. The accuracy of the proposed method is 93.21%, 94.66% on PASCAL 3D+ and example-based 3D object arrangements datasets. The algorithm takes 1.01 seconds for the reconstruction of the wireframe model. According to the results obtained, the proposed methodology gives better results than the existing state-of-the-art methods.
On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion

Structure from motion (SfM) has recently been formulated as a self-supervised learning problem, where neural network models of depth and egomotion are learned jointly through view synthesis. Herein, we address the open problem of how to best couple, or link, the depth and egomotion network components, so that information such as a common scale factor can be shared between the networks. Towards this end, we introduce several notions of coupling, categorize existing approaches, and present a novel tightly-coupled approach that leverages the interdependence of depth and egomotion at training time and at test time. Our approach uses iterative view synthesis to recursively update the egomotion network input, permitting contextual information to be passed between the components. We demonstrate through substantial experiments that our approach promotes consistency between the depth and egomotion predictions at test time, improves generalization, and leads to state-of-the-art accuracy on indoor and outdoor depth and egomotion evaluation benchmarks.