Reviewerly/exHarmony huggingface dataset:
Video Super-Resolution via a Spatio-Temporal Alignment Network

Deep convolutional neural network based video super-resolution (SR) models have achieved significant progress in recent years. Existing deep video SR methods usually impose optical flow to wrap the neighboring frames for temporal alignment. However, accurate estimation of optical flow is quite difficult, which tends to produce artifacts in the super-resolved results. To address this problem, we propose a novel end-to-end deep convolutional network that dynamically generates the spatially adaptive filters for the alignment, which are constituted by the local spatio-temporal channels of each pixel. Our method avoids generating explicit motion compensation and utilizes spatio-temporal adaptive filters to achieve the operation of alignment, which effectively fuses the multi-frame information and improves the temporal consistency of the video. Capitalizing on the proposed adaptive filter, we develop a reconstruction network and take the aligned frames as input to restore the high-resolution frames. In addition, we employ residual modules embedded with channel attention as the basic unit to extract more informative features for video SR. Both quantitative and qualitative evaluation results on three public video datasets demonstrate that the proposed method performs favorably against state-of-the-art super-resolution methods in terms of clearness and texture details.
Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review

In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed interesting results regarding the diagnostic efficiency of AI methods in differentiating Multiple sclerosis (MS) patients from healthy controls or other demyelinating diseases. There is a great lack of a comprehensive systematic review study on the role of AI in the diagnosis of MS. We aimed to perform a systematic review to document the performance of AI in MS diagnosis.A systematic search was performed using four databases including PubMed, Scopus, Web of Science, and IEEE on August 2021. All original studies which focused on deep learning or AI to analyze any modalities with the purpose of diagnosing MS were included in our study.Finally, 38 studies were included in our systematic review after the abstract and full-text screening. A total of 5433 individuals were included, including 2924 cases of MS and 2509 healthy controls. Sensitivity and specificity were reported in 29 studies which ranged from 76.92 to 100 for sensitivity and 74 to 100 for specificity. Furthermore, 34 studies reported accuracy ranged 81 to 100. Among included studies, Magnetic Resonance Imaging (MRI) (20 studies), OCT (six studies), serum and cerebrospinal fluid markers (six studies), movement function (three studies), and other modalities such as breathing and evoked potential was used for detecting MS via AI.In conclusion, diagnosis of MS based on new markers and AI is a growing field of research with MRI images, followed by images obtained from OCT, serum and CSF biomarkers, and motor associated markers. All of these results show that with advances made in AI, the way we monitor and diagnose our MS patients can change drastically.
The emergence of a concept in shallow neural networks

We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset made of blurred copies of definite but unavailable "archetypes" and we show that there exists a critical sample size beyond which the RBM can learn archetypes, namely the machine can successfully play as a generative model or as a classifier, according to the operational routine. In general, assessing a critical sample size (possibly in relation to the quality of the dataset) is still an open problem in machine learning. Here, restricting to the random theory, where shallow networks suffice and the "grandmother-cell" scenario is correct, we leverage the formal equivalence between RBMs and Hopfield networks, to obtain a phase diagram for both the neural architectures which highlights regions, in the space of the control parameters (i.e., number of archetypes, number of neurons, size and quality of the training set), where learning can be accomplished. Our investigations are led by analytical methods based on the statistical-mechanics of disordered systems and results are further corroborated by extensive Monte Carlo simulations.
Blockchain-based random auditor committee for integrity verification

With the popularity of cloud storage, integrity verification for outsourced data has attracted more and more attention. The majority of existing research works rely on a unique third-party auditor (TPA) or fixed TPAs to audit the integrity of outsourced data, and thus they are vulnerable to DoS and collusion attacks as the TPA is not completely trusted. To conquer this problem, a blockchain-based random auditor committee is proposed to replace the fixed TPAs for the integrity verification (call BRAC scheme for short). The proposed blockchain consensus algorithm utilizes a verifiable random function (VRF) to select the third-party auditor committee (TPAC) which performs contract verification. The leader of TPAs (LTPA) packs the verification proof into a blockchain called verification chain. The main idea is employing the output of a VRF for leader election to make it probabilistic, such that specific nodes cannot be targeted in advance and a certain fraction of the nodes being malicious can be tolerated. We present rigorous security analysis to demonstrate the security of the proposed scheme. Meanwhile, the comprehensive performance evaluation shows that the proposed scheme improves the performance of data integrity verification effectively.
Geometric imbalanced deep learning with feature scaling and boundary sample mining

• This paper considers the geometric distributions of samples and scale the features by the radius of hypersphere. • This paper explores the relationships between samples, and then constrains the inter-class and the intra-class distances. • The proposed model is evaluated on the three benchmark datasets and achieves the superior performance over previous methods. Data imbalance is a significant factor affecting classification performance in computer vision. In particular, data imbalance is harmful to classification learning and representation learning. To address this issue, this paper proposes a geometric deep learning framework combined with Feature Scaling Module (FSM) and Boundary Samples Mining Module (BSMM). Considering the geometric information in sample distributions of training samples, FSM is proposed to scale the features by hypersphere radius of each class, which improves the representation ability of minority classes. Meanwhile, it is noteworthy that the relationships and information between samples are essential for classification. Therefore, BSMM is proposed to mine the boundary samples by Gabriel Graph that takes the relationships into account. Finally, a loss scheduler is designed to adjust the training process of these two modules. With the scheduler, the model first learns representation and then focuses more on minority classes gradually. Extensive experiments on three benchmark datasets demonstrate the advantages of the proposed learning framework over the state-of-the-art models for solving the imbalance problem.
Traffic Sign Recognition Based on Semantic Scene Understanding and Structural Traffic Sign Location

Traffic sign recognition (TSR) plays an important role in driving assistance system and traffic safety insurance. However, existing methods focus on extracting features of traffic signs and ignore the constraints of spatial positional relationships between traffic signs and other objects in the scene. This way results in incorrectly detecting other similar objects as traffic signs and failing to detect very small traffic signs. A TSR method based on semantic scene understanding and structural traffic sign location is proposed in this study to solve the aforementioned problems. A scene structure model based on the constraints of spatial positional relationships between traffic signs and other objects is proposed to establish trusted search regions. An improved Light-weight RefineNet is used to analyze and understand a scene semantically and accurately and then segment objects in complicated environments precisely. A new network multiscale densely connected object detector (MDCOD) based on densely connected style, multiscale feature fusion, and improved K-means++ algorithms is proposed to recognize very small traffic signs. The trusted traffic signs are found by filtering false candidates outside the scene structure model. The proposed method is tested on Tsinghua-Tencent 100K and German Traffic Sign Detection Benchmark datasets and achieves accuracies of 92.8% and 99.90%, respectively, outperforming the existing methods.
A Hybrid Algorithm Incorporating Vector Quantization and One-Class Support Vector Machine for Industrial Anomaly Detection

Anomaly detection plays an important role in industry, especially in ensuring system safety and product quality. Due to the unavailability of anomalous data in many practical cases, anomaly detection is usually solved by one-class classification (OCC) methods using only normal data. As a classical OCC method, one-class support vector machine (OCSVM) is a popular discriminative approach for anomaly detection, which detects abnormal data points by establishing a decision boundary in the kernel space. However, the performance of OCSVM heavily relies on kernel parameters, whose selection is not trivial for anomaly detection problems. Moreover, for some uneven and complex data distributions, different data regions may have quite different densities and shapes, making it difficult for OCSVM to obtain good boundaries in all regions using a global kernel parameter. To address the above two issues, in this article, we propose a hybrid algorithm incorporating vector quantization and OCSVM (VQ-OCSVM). Specifically, vector quantization is used to extract distribution information of normal data, and the results are used to construct an explicit mapping function to map data into a high-dimensional feature space. Then, OCSVM is performed in the feature space to build a classifier. By introducing the explicit mapping into OCSVM, the proposed method can effectively bypass the kernel parameter selection problem of the classical OCSVM method. Furthermore, the constructed mapping carries the data distribution information, and the VQ-OCSVM model can be regarded as an integration of generative learning and discriminative learning. The complementary properties of these two paradigms make the proposed VQ-OCSVM algorithm have better generalization capacity for complex data distribution. Both qualitative and quantitative experimental results demonstrate the effectiveness and advantages of the proposed method.
Optimizing deep neural networks on intelligent edge accelerators via flexible-rate filter pruning

While deep learning has shown superior performance in various intelligent tasks, it is still a challenging problem to deploy sophisticated models on resource-limited edge devices. Filter pruning performs a system-independent optimization, which shrinks a neural network model into a thinner one, providing an attractive solution for efficient on-device inference. Prevailing approaches usually utilize fixed pruning rates for the whole neural network model to reduce the optimization space of filter pruning. However, the filters of different layers may have different sensitivities for model inference and therefore a flexible rate setting of pruning can potentially further increase the accuracy of compressed models. In this paper, we propose FlexPruner, a novel approach for compressing and accelerating neural network models via flexible-rate filter pruning. Our approach follows a greedy-based strategy to select the filters to be pruned and performs an iterative loss-aware pruning process, thereby achieving a remarkable accuracy improvement over existing methods when numerous filters are pruned. Evaluation with state-of-the-art residual neural networks on six representative intelligent edge accelerators demonstrates the effectiveness of FlexPruner, which decreases the accuracy degradation of pruned models by leveraging flexible pruning rates and achieves practical speedups for on-device inference.
Cybersickness and postural stability of first time VR users playing VR videogames

This study investigated symptoms of cybersickness and postural instability experienced by new users of head-mounted display virtual reality (HMD-VR), playing VR videogames over long and repeated sessions, and moderation of these symptoms by previous videogame experience and intensity of videogame stimulus. Cybersickness (SSQ) and postural stability (anterior-posterior path-velocity) of new users of VR (n = 80) was collected PRE-VR, POST-VR and 10 min after completing (POST-RECOVERY) a VR gaming experience. Users comprised of videogamers (n = 40) and non-videogamers (n = 40), who were randomly assigned to play either action (high-intensity stimuli) or adventure (low-intensity stimuli) games in VR for 30 min and repeated twice, one week apart. All participants, irrespective of gaming status and genre of game, experienced significant cybersickness after 30 min in VR using current-generation HMD-VR technology, and did not adapt (POST-VR) after two sessions. However videogamers were able to recover (POST-RECOVERY) from cybersickness induced in VR significantly better than non-videogamers. All participants experienced significantly better postural stability after 30 min in VR, irrespective of gaming experience or genre of game. Developers should create VR experiences that minimise negative symptoms of cybersickness and postural instability experience by new users of VR.
Rethinking Training Strategy in Stereo Matching

In stereo matching, various learning-based approaches have shown impressive performance in solving traditional difficulties on multiple datasets. While most progress is obtained on a specific dataset with a dataset-specific network design, the performance on the single dataset and cross dataset affected by training strategy is often ignored. In this article, we analyze the relationship between different training strategies and performance by retraining some representative state-of-the-art methods (e.g., geometry and context network (GC-Net), pyramid stereo matching network (PSM-Net), and guided aggregation network (GA-Net), etc.). According to our research, it is surprising that the performance of networks on single or cross datasets is significantly improved by pre-training and data augmentation without any particular structure acquirement. Based on this discovery, we improve our previous non-local context attention network (NLCA-Net) to NLCA-Net v2 and train it with the novel strategy and rethink the training strategy of stereo matching concurrently. The quantitative experiments demonstrate that: 1) our model is capable of reaching top performance on both the single dataset and the multiple datasets with the same parameters in this study, which also won the 2nd place in the stereo task of the ECCV Robust vision Challenge 2020 (RVC 2020); and 2) on small datasets (e.g., KITTI, ETH3D, and Middlebury), the model's generalization and robustness are significantly affected by pre-training and data augmentation, even exceeding the network structure's influence in some cases. These observations present a challenge to the conventional wisdom of network architectures in this stage. We expect these discoveries to encourage researchers to rethink the current paradigm of "excessive attention on the performance of a single small dataset" in stereo matching.
Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid the stragglers effect in synchronous FL and adapts to scenarios with vast participants. Both staleness and non-IID data in asynchronous FL would reduce the model utility. However, there exists an inherent contradiction between the solutions to the two problems. That is, mitigating the staleness requires to select less but consistent gradients while coping with non-IID data demands more comprehensive gradients. To address the dilemma, this paper proposes a two-stage weighted $K$ asynchronous FL with adaptive learning rate (WKAFL). By selecting consistent gradients and adjusting learning rate adaptively, WKAFL utilizes stale gradients and mitigates the impact of non-IID data, which can achieve multifaceted enhancement in training speed, prediction accuracy and training stability. We also present the convergence analysis for WKAFL under the assumption of unbounded staleness to understand the impact of staleness and non-IID data. Experiments implemented on both benchmark and synthetic FL datasets show that WKAFL has better overall performance compared to existing algorithms.
Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)

Breast cancer (BC) classification has become a point of concern within the field of biomedical informatics in the health care sector in recent years. This is because it is the second-largest cause of cancer-related fatalities among women. The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Proper detection and monitoring contribute immensely to the survival of BC patients, which is largely dependent on the analysis of pathological images. Automatic detection of BC based on pathological images and the use of a Computer-Aided Diagnosis (CAD) system allow doctors to make a more reliable decision. Recently, Deep Learning algorithms like Convolution Neural Network have been proven to be reliable in detecting BC targets from pathological images. Several research efforts have been undertaken in the binary classification of histopathological images. However, few approaches have been proposed for the multi-classification of histopathological images. The classification accuracy produced by these approaches are inefficient since they considered only texture-based extracted features and they used some techniques that cannot extract some of the main features from the images. Also, these techniques still suffered from the issue of overfitting. In this work, handcrafted feature extraction techniques (Hu moment, Haralick textures, and color histogram) and Deep Neural Network (DNN) are employed for breast cancer multi-classification using histopathological images on the BreakHis dataset. The features extracted using the handcrafted techniques are used to train the DNN classifiers with four dense layers and Softmax. Further, the data augmentation method was employed to address the issue of overfitting. The results obtained reveal that the use of handcrafted approach as feature extractors and DNN classifiers had a better performance in breast cancer multi-classification than other approaches in the literature. Moreover, it was also noted that augmentation of data plays a key role in further improvement of classification accuracy. The proposed method achieved an accuracy score of 97.87% for 40x, 97.60% for 100x, 96.10% for 200x, and 96.84% for 400x for the magnification-dependent histopathological images classification. The results also showed that the proposed method for using the Handcrafted feature extraction method with DNN classifier had a better performance in multi-classification of breast cancer using histopathological images than most of the related works in the literature.
Engineering Trustable and Auditable Choreography-based Systems Using Blockchain

A key challenge in engineering distributed systems consists in the integration into their development of a decentralised infrastructure allowing the system participants to trust each other. In this article, we face such a challenge by proposing a model-driven methodology and a related framework to support the engineering of trustable and auditable systems. The approach is based on choreography diagrams specified in the Business Process Model and Notation standard, describing the interactions that should occur among the distributed components of systems. We support the whole lifecycle of choreographies, from their modelling to their distributed execution and auditing. The framework, based on blockchain technology, is named ChorChain. More specifically, ChorChain takes as input a BPMN choreography model and automatically translates it into a Solidity smart contract. The smart contract permits us to enforce the interactions among the cooperating components as prescribed by the choreography model. By leveraging on the auditability of blockchain, ChorChain also supports the activity of auditors continuously. In such a way, ChorChain enables auditors to get some degree of assurance on what happens simultaneously with, or shortly after, information disclosure. We assess the feasibility and effectiveness of the proposed methodology and framework through experiments conducted on the Rinkeby Ethereum Testnet.
Performance of hyperspectral data in predicting and mapping zinc concentration in soil

Reflectance spectroscopy in visible, near-infrared, and short-wave infrared (VNIR-SWIR) region has been recognized as a promising alternative for prediction of heavy metal concentration in soil. Compared with VNIR-SWIR reflectance spectroscopy, VNIR reflectance spectroscopy is less affected by atmospheric water vapor and has relatively high signal to noise ratio. The performances of VNIR and VNIR-SWIR hyperspectral data in predicting and mapping heavy metal concentration in soil were explored. In this study, laboratory spectra of soil samples collected from an agricultural area and Advanced Hyperspectral Imaging (AHSI) remote sensing imagery were used to predict and map zinc (Zn) concentration with genetic algorithm and partial least squares regression (GA-PLSR). The entire spectral regions of VNIR-SWIR and VNIR and spectral subsets extracted from the entire spectral regions were used in the prediction. For the laboratory spectra, the combination of the spectral bands extracted from the absorption features at 500 nm and in 600-800 nm obtained the highest prediction accuracy with the root mean square error (RMSE) and coefficient of determination (R2) values of 8.90 mg kg-1 and 0.72. For soil spectra from AHSI remote sensing imagery, the highest prediction accuracy was achieved by using the spectral bands extracted from the absorption feature in 600-800 nm with the RMSE and R2 values of 9.02 mg kg-1 and 0.75. Soil Zn concentration maps were generated with the established prediction models using AHSI remote sensing imagery. Analysis on the Zn concentration maps shows that the prediction model established using the spectral bands extracted from the absorption feature in 600-800 nm has a better performance in mapping Zn concentration. The results indicate that VNIR hyperspectral data outperforms VNIR-SWIR hyperspectral data in predicting and mapping Zn concentration in soil, which provides an alternative to the application of hyperspectral data in soil science.