Reviewerly/exHarmony huggingface dataset:
Alzheimer&amp;#8217;s Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs

We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM with highway layers using words, word probabilities, disfluency features, pause information, and a variety of acoustic features, achieves an accuracy of 84% and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting cognitive decline is more challenging, our models show improvement using the multimodal approach and word probabilities, disfluency and pause information over word-only models. We show considerable gains for AD classification using multimodal fusion and gating, which can effectively deal with noisy inputs from acoustic features and ASR hypotheses.
Reinforced knowledge distillation: Multi-class imbalanced classifier based on policy gradient reinforcement learning

The real-world datasets often exhibit imbalanced class distribution, which is a common challenge for multi-class classification algorithms. To settle the multi-class imbalanced classification problem of class imbalance learning, a novel reinforced knowledge distillation method is proposed in this paper. In the reinforced knowledge distillation, an improved fine-grained classification architecture based on knowledge distillation strategy and policy gradient reinforcement learning is proposed. In addition, reinforced knowledge distillation uses a newly designed reward signal and a novel sample weights update strategy to train the policies to find the optimal student-network, which makes reinforced knowledge distillation more powerful in handling the multi-class imbalanced classification problem. The effectiveness and practicability of the proposed reinforced knowledge distillation method are verified through its application to a simulated industrial process benchmark and extensive real-world datasets.
Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies With COVID-19 CT, X-Ray, and Cholesterol Data

Machine learning requires a large volume of sample data, especially when it is used in highaccuracy medical applications.However, patient records are one of the most sensitive private information that is not usually shared among institutes.This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations.Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns.The proposed split learning consists of a number of clients and a centralized server.Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer.Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed.We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches.This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels.
Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies

The fast growth of installed photovoltaic capacity is leading to an increasing impact of variable photovoltaic generation on the overall electricity industry, affecting all stakeholders in this sector. As a consequence, the importance of appropriate photovoltaic power forecasts for planning and decision support is rising, to cope with the resulting uncertainty. In particular, probabilistic forecasts are becoming increasingly important to assess the underlying risks, e.g., depicting the effect of adverse combinations. Whereas deterministic forecasts, while having the advantage of being more detailed, suffer from reflecting only an average expectation. Therefore, this paper proposes a comprehensive hybrid approach to generate deterministic and probabilistic photovoltaic power forecasts, while introducing several improvements for intra-day and day-ahead modelling and forecasting applications. In this context, several pre- and post-processing steps have been combined for the deterministic model, while the spatial interrelation of the forecasting errors is taken into account by applying D-vine copulas for the probabilistic forecasts. The reliability of the proposed hybrid approach is validated, using a comprehensive case study with high-resolution numerical weather predictions and real-world measurement data over several years for multiple photovoltaic units. Furthermore, the proposed model is benchmarked against various combinations of a photovoltaic power model (with and without statistical post-processing) and typical probabilistic models. As part of the evaluation the Energy score, Variogram-based score and Diebold–Mariano test are applied to evaluate the proposed model and highlight the strong performance of the proposed hybrid approach. • Benchmark of nonparametric statistical and parametric physical forecasting techniques. • Application of sophisticated model chain with several pre- and post-processing steps. • Comparison on intra-day and day-ahead deterministic and probabilistic forecasts. • Extensive case study benchmarking forecasts for multiple photovoltaic units and years. • Improved modelling of spatially correlated forecasting errors with vine copulas.
Effect of destructive earthquake on the population-economy-space urbanization at county level-a case study on Dujiangyan county, China

Geological hazards such as destructive earthquake severely limit the speed and the scale of urbanization at urban scale. However, whether the historical earthquake exert persistent or temporary effects on the urbanization process at county scale also remain intertwined. The 2008 Wenchuan earthquake was an unparalleled natural disaster in China history, and the Dujiangyan county is one of the ten worst-hit counties. Taking the Dujiangyan county as a case study, we established an indexing systems for urbanization at a county scale. The urbanization level at the county scale was gauged from three dimensions including population, economy and space. The coordinate assessment indicated that the urbanization process at the scale of county had obvious phase characteristics before and after the natural disasters. The suitable post-disaster policies and measures could strengthen the coupling coordination degree among the population, economy and space at the county scale. Triangle analysis indicated the urbanization model of the Dujiangyan county was basically a shift from space-dominated to population-dominated before and after the earthquake. It's indispensable to improve the quality of geological environment to minimize the threats and losses rendered by earthquake and facilitate sustainable urbanization process at county scale.
A Deep Learning Framework of Autonomous Pilot Agent for Air Traffic Controller Training

In this work, a deep learning-based framework is proposed to implement an autonomous pilot agent (APA), which serves as a human pseudo-pilot to assist air traffic controller (ATCO) training. A novel paradigm, including speech recognition, language understanding, pilot repetition generation (PRG), and text-to-speech (TTS), is designed to formulate the framework pipeline, which also incorporates a simulation system interface. We mainly focus on the PRG and TTS models to address the ATC specificities in this work. The neural architecture is proposed to generate the text repetition instruction by using a sequence-to-sequence text mapping. The Transformer block is improved to implement a high-efficient TTS model, in which the nonautoregressive mechanism is applied to achieve the parallel synthesis. A dedicated phoneme vocabulary is designed to cope with the multilingual issue in the ATC domain and address the out-of-vocabulary problem. With the APA framework, a virtual training mode is proposed to complete the training task without the limitation of time and location. Experimental results on a real-world dataset show that the proposed APA framework replaces the human pilot with considerable high confidence in a real-time manner during the simulation training. Most importantly, the APA framework and the virtual training system are able to cope with the dilemma of physical attendance (like COVID-19) and improve the equipment utilization capacity for the ATCO training.
DouBiGRU-A: Software defect detection algorithm based on attention mechanism and double BiGRU

Software defects such as errors, bugs, and failures lead to poor usability and low efficiency, severely degrading the user experience. Bugs in the code are among the key areas of software defects. The exploitability of such vulnerabilities can bring about a series of security problems, such as user information leakage and network attacks. Most traditional solutions in software vulnerability detection rely on practical knowledge and experience for manual labeling and classification. Manual methods can effectively detect vulnerabilities with a high degree of attention, but those with a low degree of attention have relatively high false negative and false positive rates. Solutions based on software defect code data sets are available, which use deep learning to train software vulnerability identification models, reducing the dependence on manual knowledge and experience, but the precision rate (P) of the models and the F1 score are generally low. In this paper, based on the NVD and SARD data sets, we propose a software defect detection algorithm DouBiGRU-A that combines bidirectional gated recurrent unit (BiGRU) and an attention mechanism. In the experimental simulation, comparison with the Li-Method, bilateral long short-term memory (BiLSTM), BiGRU, and BiLSTM&Attention shows that on the CWE-399 data set, the P and F1 scores of DouBiGRU-A are 0.7% and 0.80% higher than the Li-Method, respectively. Moreover, in the CWE-399 data set, the P and F1 scores of DouBiGRU-A are 28.2% and 43.45% higher than the average values for Flawfinder and RATS, respectively. On the CWE-119 data set, the F1 score of DouBiGRU-A is 2.73% higher than the Li-Method; the P and F1 scores of DouBiGRU-A are 63.07% and 53.98% higher than the average values of Flawfinder and RATS, respectively. On the combined CWE-119&CWE-399 data set, the P and F1 scores of DouBiGRU-A are 5.22% and 4.29% higher than Li-Method, respectively. The P and F1 scores of DouBiGRU-A are 59.72% and 46.59% higher than the average values of Flawfinder and RATS, respectively.
A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking

The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.
Medical Image Registration Based on Uncoupled Learning and Accumulative Enhancement

As a basic building block in medical image analysis, image registration has been greatly developed since the emergence of modern deep neural networks. Compared to non-learning-based methods, the latest approaches can learn task-specific features spontaneously, thus generate the registration results with one round of inference. However, when large inter-image distortion occurs, the stability of existing methods can be strongly affected. To alleviate this problem, the iterative framework based on coarse-to-fine strategies has been introduced in recent works. However, their networks at each iteration step are relatively independent, which is not an optimal solution for the reinforcement of image features. What is more, the moving and the fixed images are often concatenated or fed to identical network layers. Consequently, the iterative learning and warping on the moving image can be entangled with the fixed image. In order to address these issues, we present a novel medical image registration framework, namely ULAE-net, to continuously enhance the spatial transformation and establish more profound contextual dependencies under a compact network layout. Extensive experiments on 3D brain MRI data sets demonstrate that our method has greatly improved the registration performance, thereby outperforms state-of-the-art methods under large-scale deformations (https://github.com/wanghaostu/ULAE-net).
Consensus Clustering With Unsupervised Representation Learning

Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment. Bootstrap Your Own Latent (BYOL) is one such representation learning algorithm that has achieved state-of-the-art results in self-supervised image classification on ImageNet under the linear evaluation protocol. However, the utility of the learnt features of BYOL to perform clustering is not explored. In this work, we study the clustering ability of BYOL and observe that features learnt using BYOL may not be optimal for clustering. We propose a novel consensus clustering based loss function, and train BYOL with the proposed loss in an end-to-end way that improves the clustering ability and outperforms similar clustering based methods on some popular computer vision datasets.
Examining the Academic Trends in Neuropsychological Tests for Executive Functions Using Virtual Reality: Systematic Literature Review

In neuropsychology, fully immersive virtual reality (VR) has been spotlighted as a promising tool. It is considered that VR not only overcomes the existing limitation of neuropsychological tests but is also appropriate for treating executive functions (EFs) within activities of daily living (ADL) due to its high ecological validity. While fully immersive VR offers new possibilities of neuropsychological tests, there are few studies that overview the intellectual landscape and academic trends in the research related to mainly targeted EFs with fully immersive VR.The objective of this study is to get an overview of the research trends that use VR in neuropsychological tests and to analyze the research trends using fully immersive VR neuropsychological tests with experimental articles.This review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were searched in three web databases using keywords related to VR, EFs, and cognitive abilities. The study was conducted in two steps, keyword analysis and in-depth systematic review. In the web database search from 2000 to 2019, 1167 articles were initially collected, of which 234 articles in the eligibility phase were used to conduct keyword analysis and a total of 47 articles were included for systematic review.In keyword analysis, the number of articles focused on dementia including the keywords "MCI," "SCD," and "dementia" were highlighted over the period, rather than other symptoms. In addition, we identified that the use of behavioral and physiological data in virtual environments (VEs) has dramatically increased in recent studies. In the systematic review, we focused on the purpose of study, assessment, treatment, and validation of usability and structure. We found that treatment studies and uncategorized studies including presence and cybersickness issues have emerged in the recent period. In addition, the target symptoms and range of participants were diversified.There has been a continuously increasing interest in dealing with neuropsychology by using fully immersive VR. Target cognitive abilities have been diversified, as well as target symptoms. Moreover, the concept of embodied cognition was transplanted in this research area.
Trusted Resource Allocation Based on Smart Contracts for Blockchain-Enabled Internet of Things

By sharing resources between edge servers and end users, edge-end cooperation is one important way to support various applications in Internet of Things, which have critical resource requirements on computing, storage, or bandwidth. How to price these resources and how to evaluate the service quality of edge servers are two key issues to support trusted resource allocation for blockchain-enabled Internet of Things. In this article, we provide a trusted resource allocation mechanism based on smart contracts, in which a group-buying pricing mechanism (GBPM) and a reputation evaluation mechanism (REM) are proposed to effectively address the problems existing in resources pricing and service quality evaluation of edge servers. In the trusted resource allocation mechanism, end users can choose a purchase mode from four pricing schemes in terms of actual demands on delay and price, and smart contracts can match end users with high-reputation edge servers automatically. Moreover, end users can submit reputation evaluations to smart contracts based on the behaviors of edge servers. Simulation results show the GBPM can provide differentiated prices and optimize the utility of end users accordingly, while the REM is more sensitive to edge servers with irregular behaviors and quickly reduces their reputations so that the success rate of transactions is improved.
Few-shot electromagnetic signal classification: A data union augmentation method

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.
Novel hyper chaotic color image encryption based on pixel and bit level scrambling with diffusion

In this paper, a novel method for symmetric image encryption using Lorenz chaotic system is proposed. The main operations performed in the proposed system are keystream generation, permutation, and diffusion. In the permutation phase, both pixel-level and bit-level scrambling are performed to achieve the double scrambling property. The pixel-level scrambling is implemented by a novel approach using two different keystreams. Also, the bit-level scrambling is realized with a novel approach using cyclic shift operation and a keystream. Thus, the double scrambling ensures higher security in the permutation process. The images are encrypted by an enhanced diffusion process using another keystream. The permutation and diffusion processes use different keystreams generated from the Lorenz system with chaotic behavior. A novel method is adopted for the keystream generation phase. The security of the proposed cryptosystem has been assessed by various evaluation metrics like sensitivity analysis on key and cipher images, information entropy analysis, occlusion attack, and diverse statistical explorations such as correlation and histogram analyses. The experimental results show the supremacy and better performance of the proposed cryptosystem compared to several state-of-the-art techniques.
Semi-Supervised Pixel-Level Scene Text Segmentation by Mutually Guided Network

In this paper we present a new data-driven method for pixel-level scene text segmentation from a single natural image. Although scene text detection, i.e. producing a text region mask, has been well studied in the past decade, pixel-level text segmentation is still an open problem due to the lack of massive pixel-level labeled data for supervised training. To tackle this issue, we incorporate text region mask as an auxiliary data into this task, considering acquiring large-scale of labeled text region mask is commonly less expensive and time-consuming. To be specific, we propose a mutually guided network which produces a polygon-level mask in one branch and a pixel-level text mask in the other. The two branches’ outputs serve as guidance for each other and the whole network is trained via a semi-supervised learning strategy. Extensive experiments are conducted to demonstrate the effectiveness of our mutually guided network, and experimental results show our network outperforms the state-of-the-art in pixel-level scene text segmentation. We also demonstrate the mask produced by our network could improve the text recognition performance besides the trivial image editing application.