Reviewerly/exHarmony huggingface dataset:
Application research of artificial intelligence English audio translation system based on fuzzy algorithm

With the development of globalization, people’s demand for English audio interaction is increasing. In order to overcome the shortcomings of traditional translation methods in grammatical variables, such as semantic ambiguity, quantifier errors, low translation accuracy, improve the quality and speed of English translation, and get more accurate and speed guaranteed translation, this study proposes an artificial intelligence English audio translation cross language system based on fuzzy algorithm. In this experiment, the collected analog speech signal is converted into a digital speech signal, and then, the speech features are modeled and digitized, and the whole set of speech samples are integrated and modified to eliminate the interference caused by noise as far as possible. After that, the collected voice will be stored in the text format, and then the text will be translated to achieve English audio translation. The DNN-HMM speech recognition model and the traditional GMM-HMM speech recognition model are used to preprocess the original corpus, and the accuracy of the corpus processing is compared. After that, the accuracy and utilization of the fuzzy algorithm are evaluated between the first type TSK and the second type TSK. For speech synthesis in which the corpus lacks language, it is meaningful to explore the least amount of training data for the synthesis of acceptable speech. The experimental results show that the accuracy of the fuzzy algorithm is about 97.34%, and the utilization rate is about 98.14%. The accuracy rate of type 1 and type 2 algorithms are about 85.77% and 76.87% respectively, and the utilization rate is about 83.25% and 78.63% respectively. The fuzzy algorithm based artificial intelligence English audio translation cross language system is obviously better than the other two algorithms.
Modelling Spatial Behaviours in Clinical Team Simulations using Epistemic Network Analysis: Methodology and Teacher Evaluation

In nursing education through team simulations, students must learn to position themselves correctly in coordination with colleagues. However, with multiple student teams in action, it is difficult for teachers to give detailed, timely feedback on these spatial behaviours to each team. Indoor-positioning technologies can now capture student spatial behaviours, but relatively little work has focused on giving meaning to student activity traces, transforming low-level x/y coordinates into language that makes sense to teachers. Even less research has investigated if teachers can make sense of that feedback. This paper therefore makes two contributions. (1) Methodologically, we document the use of Epistemic Network Analysis (ENA) as an approach to model and visualise students' movements. To our knowledge, this is the first application of ENA to analyse human movement. (2) We evaluated teachers' responses to ENA diagrams through qualitative analysis of video-recorded sessions. Teachers constructed consistent narratives about ENA diagrams' meaning, and valued the new insights ENA offered. However, ENA's abstract visualisation of spatial behaviours was not intuitive, and caused some confusions. We propose, therefore, that the power of ENA modelling can be combined with other spatial representations such as a classroom map, by overlaying annotations to create a more intuitive user experience.
Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models

State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled by SSMs depend on the accuracy of the inferred/learned model. Most of the existing inference techniques for SSMs are capable of dealing with very small systems, unable to be applied to most of the large-scale practical problems. Toward this, this article introduces a two-stage Bayesian optimization (BO) framework for scalable and efficient inference in SSMs. The proposed framework maps the original large parameter space to a reduced space, containing a small linear combination of the original space. This reduced space, which captures the most variability in the inference function (e.g., log likelihood or log a posteriori), is obtained by eigenvalue decomposition of the covariance of gradients of the inference function approximated by a particle filtering scheme. Then, an exponential reduction in the search space of parameters during the inference process is achieved through the proposed two-stage BO policy, where the solution of the first-stage BO policy in the reduced space specifies the search space of the second-stage BO in the original space. The proposed framework's accuracy and speed are demonstrated through several experiments, including real metagenomics data from a gut microbial community.
Review-based hierarchical attention cooperative neural networks for recommendation

In e-commerce platform, users conduct purchase behavior and write reviews for the purchased items. These reviews usually contain a lot of valuable information for recommendation, which can reflect the purchase preference of the user and the characteristic of the item. We propose the Hierarchical Attention Cooperative Neural Networks (HACN) model for recommendation. Hierarchical attention mechanism is adopted to enrich user's and item's feature representation from review texts. Two parallel networks based on review texts are used to model users and items respectively, which makes the generated features more purposeful. Further, the target ID embedding is introduced to capture the global entity relationship in the dataset. The experiments are performed on five real-world datasets of different domains from Amazon, and our proposed HACN model has achieved better results than the existing state-of-the-art methods.
Using Information Content to Select Keypoints for UAV Image Matching

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.
Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation

Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues. In this paper, we present a novel approach to learn domain adaptive knowledge in models with limited memory, thus bestowing the model with the ability to deal with these issues in a comprehensive manner. We term this as “Domain Adaptive Knowledge Distillation” and address the same in the context of unsupervised domain-adaptive semantic segmentation by proposing a multi-level distillation strategy to effectively distil knowledge at different levels. Further, we introduce a novel cross entropy loss that leverages pseudo labels from the teacher. These pseudo teacher labels play a multifaceted role towards: (i) knowledge distillation from the teacher network to the student network & (ii) serving as a proxy for the ground truth for target domain images, where the problem is completely unsupervised. We introduce four paradigms for distilling domain adaptive knowledge and carry out extensive experiments and ablation studies on real-to-real as well as synthetic-to-real scenarios. Our experiments demonstrate the profound success of our proposed method.
EAGNet: Elementwise Attentive Gating Network-Based Single Image De-Raining With Rain Simplification

Rain streaks are one of the main factors that degrade the performance of computer vision algorithms. Therefore, a preprocessing method is needed to remove rain streaks from rainy images. The main issue of the rain removal task is to prevent over (or under) de-raining. Over de-raining means that the background details are removed along with rain streaks in light rain, and under de-raining means that the rain streaks are not completely removed in heavy rain. These occur as the density of rain and intensity of rain streaks vary. In order to solve this, this paper proposes a two-step rain removal method. The proposed system first estimates the rain streaks image redefined with a simple operation from an input rainy image. The proposed rain streaks image contains rain density and rain streak intensity for the rainy image. By using this, the proposed system can adaptively remove rain streaks from images captured in various rain conditions. In addition, we propose a novel architectural unit, the elementwise attentive gating block, which is an optimized block used to deal with high frequency rain streaks. The proposed block selectively passes the desired components from the input feature maps by applying different weights to each element. It helps to clearly extract the rain streaks, and as a result, there are no traces of rain streaks on the restored image. The proposed method outperforms previous rain removal algorithms for both synthetic and real-world images.
SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information

Design of a residual block that provides a rich set of features while requiring only small numbers of parameters and operations is crucial for the task of single image super resolution. This is especially important in applications with limited power and storage capacity. In this paper, a new multi-domain residual block is proposed in order to generate richer set of features for the task of image super resolution. The proposed residual block consists of two feature generation modules. The first one is a spatial information processing module and the second one is a spectral information processing module. The feature maps obtained by these two feature generation modules are concatenatively fused to obtain block's output. The new residual block is used to build light-weight super resolution networks. Extensive experiments are performed using several benchmark datasets in order to evaluate the performance of the networks using the new multi-domain residual block. It is shown that the use of both the spatial and spectral features enhances the performance of the light-weight super resolution networks.
General Bitwidth Assignment for Efficient Deep Convolutional Neural Network Quantization

Model quantization is essential to deploy deep convolutional neural networks (DCNNs) on resource-constrained devices. In this article, we propose a general bitwidth assignment algorithm based on theoretical analysis for efficient layerwise weight and activation quantization of DCNNs. The proposed algorithm develops a prediction model to explicitly estimate the loss of classification accuracy led by weight quantization with a geometrical approach. Consequently, dynamic programming is adopted to achieve optimal bitwidth assignment on weights based on the estimated error. Furthermore, we optimize bitwidth assignment for activations by considering the signal-to-quantization-noise ratio (SQNR) between weight and activation quantization. The proposed algorithm is general to reveal the tradeoff between classification accuracy and model size for various network architectures. Extensive experiments demonstrate the efficacy of the proposed bitwidth assignment algorithm and the error rate prediction model. Furthermore, the proposed algorithm is shown to be well extended to object detection.
Connecting Arabs

research-article Share on Connecting Arabs: bridging the gap in dialectal speech recognition Authors: Ahmed Ali HBKU, Qatar HBKU, QatarView Profile , Shammur Chowdhury HBKU, Qatar HBKU, QatarView Profile , Mohamed Afify Microsoft Advanced Technology Lab, Egypt Microsoft Advanced Technology Lab, EgyptView Profile , Wassim El-Hajj American University of Beirut, Lebanon American University of Beirut, LebanonView Profile , Hazem Hajj American University of Beirut, Lebanon American University of Beirut, LebanonView Profile , Mourad Abbas Centre de Recherche Scientifique et Technique pour le Développement de la Langue Arabe, Algeria Centre de Recherche Scientifique et Technique pour le Développement de la Langue Arabe, AlgeriaView Profile , Amir Hussein Kanari AI, UAE Kanari AI, UAEView Profile , Nada Ghneim Arab International University, Syria Arab International University, SyriaView Profile , Mohammad Abushariah The University of Jordan, Jordan The University of Jordan, JordanView Profile , Assal Alqudah Taibah University, Saudi Arabia Taibah University, Saudi ArabiaView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 4April 2021 pp 124–129https://doi.org/10.1145/3451150Online:22 March 2021Publication History 2citation2,492DownloadsMetricsTotal Citations2Total Downloads2,492Last 12 Months571Last 6 weeks31 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
RDE-OP: A Region-Based Differential Evolution Algorithm Incorporation Opposition-Based Learning for Optimising the Learning Process of Multi-layer Neural Networks

Learning in multi-layer neural networks (MLNNs) involves finding appropriate weights and biases and is a challenging and important task since the performance of MLNNs is directly dependent on the weights. Conventional algorithms such as back-propagation suffer from difficulties including a tendency to get stuck in local optima. Population-based metaheuristic algorithms can be used to address these issues. In this paper, we propose a novel learning approach, RDE-OP, based on differential evolution (DE) boosted by a region-based scheme and an opposition-based learning strategy. DE is a population-based metaheuristic algorithm which has shown good performance in solving optimisation problems. Our approach integrates two effective concepts with DE. First, we find, using a clustering algorithm, regions in search space and select the cluster centres to represent these. Then, an updating scheme is proposed to include the clusters in the current population. In the next step, our proposed algorithm employs a quasi-opposition-based learning strategy for improved exploration of the search space. Experimental results on different datasets and in comparison with both conventional and population-based approaches convincingly indicate excellent performance of RDE-OP.
Quo Vadis Solar Energy Research?

In this paper, solar energy research trends and their publishing evolution are presented in terms of bibliometric analysis tools applied to the existing publications within the Web of Science Core Collection. Publishing evolution shows a clear interest in solar energy topics, as 50% of the total number of publications have been published within the time period between 2015 and 2020; most of them at journals and conference proceedings. A results analysis shows that China, the United States and India were the most productive countries in terms of solar energy research publications, and the list of the 10 most productive countries accounted for 83% of total publications within 2019 and 2020. The study pointed out as well that the main thematic areas in solar energy research in 2019–2020 were related to particular topics such as solar cells and optical properties, nanoparticles and composites, TiO2 and photocatalysis, and more general topics such as performance, simulations optimization and renewable energy. Based on paper findings, the research future of solar energy is bright, with continued growth in the publications record and a wide variety of research topics.
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization

We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which component of the network to execute for a given audio frame. To accomplish this, we leverage a recurrent cell we call the Bifocal LSTM (BF-LSTM), which we detail in the paper. The architecture is compatible with other optimization strategies such as quantization, sparsification, and applying time-reduction layers, making it especially applicable for deployed, real-time speech recognition settings. We present the architecture and report comparative experimental results on voice-assistant speech recognition tasks. Specifically, we show our proposed Bifocal RNN-T can improve inference cost by 29.1% with matching word error rates and only a minor increase in memory size.
Two-Party Adaptor Signatures from Identification Schemes

Adaptor signatures are a novel cryptographic primitive with important applications for cryptocurrencies. They have been used to construct second layer solutions such as payment channels or cross-currency swaps. The basic idea of an adaptor signature scheme is to tie the signing process to the revelation of a secret value in the sense that, much like a regular signature scheme, an adaptor signature scheme can authenticate messages, but simultaneously leaks a secret to certain parties. Recently, Aumayr et al. provide the first formalization of adaptor signature schemes, and present provably secure constructions from ECDSA and Schnorr signatures. Unfortunately, the formalization and constructions given in this work have two limitations: (1) current schemes are limited to ECDSA and Schnorr signatures, and no generic transformation for constructing adaptor signatures is known; (2) they do not offer support for aggregated two-party signing, which can significantly reduce the blockchain footprint in applications of adaptor signatures.
Data Security Through Zero-Knowledge Proof and Statistical Fingerprinting in Vehicle-to-Healthcare Everything (V2HX) Communications

The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle's horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.
Precise head pose estimation on HPD5A database for attention recognition based on convolutional neural network in human-computer interaction

Head pose estimation (HPE) under infrared imaging has become more and more common in the human-computer interaction. In this paper, we proposed a novel HPE with convolutional neural network and established a precise head pose database under 5° angle (HPD5A) for human attention recognition. Specially, the HPD5A database includes 729 infrared head pose images from different subjects with and without glasses, which corresponds to drivers who wear glasses or do not wear glasses. To verify the availability and usability of the HPD5A database, the benchmark evaluations are performed on our database using traditional standard HPE classification methods with and without principal component analysis. The methods include linear discriminant analysis, K-nearest neighbor, random forest and Naïve Bayes classifiers. We also design and implement a convolutional neural network architecture as one of elementary assessments. All the results are provided for future reference. The developed deep learning technique could obtain the state-of-the-art performance on the HPD5A database. This database will certainly help in the development of model for infrared HPE and be beneficial to the attention recognition in human-computer interaction system.
Unsupervised Human Activity Representation Learning with Multi-task Deep Clustering

Human activity recognition (HAR) based on sensing data from wearable and mobile devices has become an active research area in ubiquitous computing, and it envisions a wide range of application scenarios in mobile social networking, environmental context sensing, health and well-being monitoring, etc. However, activity recognition based on manually annotated sensing data is manpower-expensive, time-consuming, and privacy-sensitive, which prevents HAR systems from being really deployed in scale. In this paper, we address the problem of unsupervised human activity recognition, which infers activities from unlabeled datasets without the need of domain knowledge. We propose an end-to-end multi-task deep clustering framework to solve the problem. Taking the unlabeled multi-dimensional sensing signals as input, we firstly apply a CNN-BiLSTM autoencoder to form a compressed latent feature representation. Then we apply a K-means clustering algorithm based on the extracted features to partition the dataset into different groups, which produces pseudo labels for the instances. We further train a deep neural network (DNN) with the latent features and pseudo labels for activity recognition. The tasks of feature representation, clustering, and classification are integrated into a uniform multi-task learning framework and optimized jointly to achieve unsupervised activity classification. We conduct extensive experiments based on three public datasets. It is shown that the proposed approach outperforms shallow unsupervised learning approaches, and it performs close to the state-of-the-art supervised approaches by fine-tuning with a small number of labeled data. The proposed approach significantly reduces the cost of human-based data annotation and narrows down the gap between unsupervised and supervised human activity recognition.