Reviewerly/exHarmony huggingface dataset:
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion

How to leverage dynamic contextual information in end-to-end speechrecognition has remained an active research area. Previous solutions to thisproblem were either designed for specialized use cases that did not generalizewell to open-domain scenarios, did not scale to large biasing lists, orunderperformed on rare long-tail words. We address these limitations byproposing a novel solution that combines shallow fusion, trie-based deepbiasing, and neural network language model contextualization. These techniquesresult in significant 19.5% relative Word Error Rate improvement over existingcontextual biasing approaches and 5.4%-9.3% improvement compared to a stronghybrid baseline on both open-domain and constrained contextualization tasks,where the targets consist of mostly rare long-tail words. Our final systemremains lightweight and modular, allowing for quick modification without modelre-training.
Modelling the blockchain adoption barriers in the AEC industry

Purpose In the era of digitalisation, blockchain has the potential to fundamentally change the architecture, engineering and construction (AEC) industry's workflow, trust and procurement environments. However, few studies have investigated blockchain adoption barriers in the AEC industry in detail. Therefore, the study aims to provide a comprehensive understanding of these barriers and their interdependent relationships in the context of the AEC industry. Design/methodology/approach Based on a review of the literature, industry reports and expert feedback, 11 barriers towards adopting the blockchain were identified. Then, the authors investigated the interdependencies amongst the factors by adopting a two-stage integrated interpretive structural modelling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) method. Findings The findings show that the lack of information technology infrastructure (BC4) and legal and regulatory uncertainty (BC11) are the most prominent barriers towards blockchain adoption in the AEC industry. Practical implications The research contributes in providing a clearer understanding of related barriers and potential solutions for practitioners in this area. Subsequently, the identification of adoption barriers can enable an important knowledge foundation and suggest possible solutions for adopting blockchain techniques successfully and effectively in the AEC industry. Originality/value The study lays an essential research foundation for the effective adoption and use of blockchain in the AEC industry.
Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques

The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.
Using Identity-Based Cryptography as a Foundation for an Effective and Secure Cloud Model for E-Health

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients’ confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.
RNON: image inpainting via repair network and optimization network

In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon. The paper has proposed an effective image inpainting method using generative adversarial networks, which is composed of two mutually independent generative confrontation networks. Among them, the image repair network module aims to solve the problem of repairing the irregular missing areas of the image, and its generator is based on a partial convolutional network. The image optimization network module aims to solve the problem of local chromatic aberration in the repaired images, and its generator has based on deep residual networks. Through the synergy of the two network modules, the visual effect and image quality of the images has improved. The experimental results can show that the proposed method (RNON) performs better from comparisons of qualitative and quantitative evaluations with state-of-the-arts in image inpainting quality field.
Segmentation of lung from CT using various active contour models

The aim of the paper is to develop a region based active contour model using variational level set function for segmentation of lung. Ultimately, segmentation of parenchyma is essential for accurate diagnosis of various lung diseases. Among many imaging modalities, Computed Tomography (CT) is a pioneer to most image analysis applications. This work proposes a powerful technique named Selective Binary and Gaussian filtering-new Signed Pressure Force (SBGF-new SPF) function for segmentation of CT lung images. This process detects the external boundary of the lung and effectively stops the contour even at blurry boundaries. The proposed algorithm was compared with four different active contour models. Comparative experiments demonstrate the advantage of proposed method in terms of computation time and accurate segmented lung.
Hashtag Recommendation for Photo Sharing Services

Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy.
Robot Motion Planning in an Unknown Environment with Danger Space

This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.
Continuous Driver’s Gaze Zone Estimation Using RGB-D Camera

The driver gaze zone is an indicator of a driver’s attention and plays an important role in the driver’s activity monitoring. Due to the bad initialization of point-cloud transformation, gaze zone systems using RGB-D cameras and ICP (Iterative Closet Points) algorithm do not work well under long-time head motion. In this work, a solution for a continuous driver gaze zone estimation system in real-world driving situations is proposed, combining multi-zone ICP-based head pose tracking and appearance-based gaze estimation. To initiate and update the coarse transformation of ICP, a particle filter with auxiliary sampling is employed for head state tracking, which accelerates the iterative convergence of ICP. Multiple templates for different gaze zone are applied to balance the templates revision of ICP under large head movement. For the RGB information, an appearance-based gaze estimation method with two-stage neighbor selection is utilized, which treats the gaze prediction as the combination of neighbor query (in head pose and eye image feature space) and linear regression (between eye image feature space and gaze angle space). The experimental results show that the proposed method outperforms the baseline methods on gaze estimation, and can provide a stable head pose tracking for driver behavior analysis in real-world driving scenarios.
QE BERT: Bilingual BERT Using Multi-task Learning for Neural Quality Estimation

For translation quality estimation at word and sentence levels, this paper presents a novel approach based on BERT that recently has achieved impressive results on various natural language processing tasks.Our proposed model is re-purposed BERT for the translation quality estimation and uses multi-task learning for the sentence-level task and word-level subtasks (i.e., source word, target word, and target gap).Experimental results on Quality Estimation shared task of WMT19 show that our systems show competitive results and provide significant improvements over the baseline.
Enhancing collaborative creativity with virtual dynamic personas

In many sectors, designers have to develop products that are creative, and thus both new and adapted to the context. They can use a variety of methods to favor their creative design activities, including a new one that we have developed, featuring dynamic personas. This method allows participants to interact in real time in a virtual space with an avatar that represents an archetypal future user and provides them with information about this future user throughout the interactions. In the present experimental study, we compared this method with the classic (or static) persona method, by asking 102 participants to perform a creative design task. Results revealed statistical differences between the use of the static and dynamic persona methods, and highlighted the advantages of the dynamic method over the static one. We discuss the prospects for using this method in an ecological setting and identify the aspects to be improved.
The knowledge complexity of interactive proof-systems

research-article Share on The knowledge complexity of interactive proof-systems Authors: Shafi Goldwasser MIT MITView Profile , Silvio Micali MIT MITView Profile , Chales Rackoff University of Toronto University of TorontoView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 203–225https://doi.org/10.1145/3335741.3335750Published:04 October 2019Publication History 6citation132DownloadsMetricsTotal Citations6Total Downloads132Last 12 Months49Last 6 weeks3 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
ES-MAML: Simple Hessian-Free Meta Learning

We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement. Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable. We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.
Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network

High-resolution magnetic resonance imaging with fat suppression can obtain accurate anatomical information of all 35 lower limb muscles and individual segmentation can facilitate quantitative analysis. However, due to limited contrast and edge information, automatic segmentation of the muscles is very challenging, especially for athletes whose muscles are all well developed and more compact than the average population. Deep convolutional neural network (DCNN)-based segmentation methods showed great promise in many clinical applications, however, a direct adoption of DCNN to lower limb muscle segmentation is challenged by the large three-dimensional (3-D) image size and lack of the direct usage of muscle location information. We developed a cascaded 3-D DCNN model with the first step to localize each muscle using low-resolution images and the second step to segment it using cropped high-resolution images with individually trained networks. The workflow was optimized to account for different characteristics of each muscle for improved accuracy and reduced training and testing time. A testing augmentation technique was proposed to smooth the segmentation contours. The segmentation performance of 14 muscles was within interobserver variability and 21 were slightly worse than humans.
Detection outliers on internet of things using big data technology

Internet of Things (IoT) is a fundamental concept of a new technology that will be promising and significant in various fields. IoT is a vision that allows things or objects equipped with sensors, actuators, and processors to talk and communicate with each other over the internet to achieve a meaningful goal. Unfortunately, one of the major challenges that affect IoT is data quality and uncertainty, as data volume increases noise, inconsistency and redundancy increases within data and causes paramount issues for IoT technologies. And since IoT is considered to be a massive quantity of heterogeneous networked embedded devices that generate big data, then it is very complex to compute and analyze such massive data. So this paper introduces a new model named NRDD-DBSCAN based on DBSCAN algorithm and using resilient distributed datasets (RDDs) to detect outliers that affect the data quality of IoT technologies. NRDD-DBSCAN has been applied on three different datasets of N-dimensions (2-D, 3-D, and 25-D) and the results were promising. Finally, comparisons have been made between NRDD-DBSCAN and previous models such as RDD-DBSCAN model and DBSCAN algorithm, and these comparisons proved that NRDD-DBSCAN solved the low dimensionality issue of RDD-DBSCAN model and also solved the fact that DBSCAN algorithm cannot handle IoT data. So the conclusion is that NRDD-DBSCAN proposed model can detect the outliers that exist in the datasets of N-dimensions by using resilient distributed datasets (RDDs), and NRDD-DBSCAN can enhance the quality of data exists in IoT applications and technologies.
An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training

Image segmentation is critical to lots of medical applications. While deep learning (DL) methods continue to improve performance for many medical image segmentation tasks, data annotation is a big bottleneck to DL-based segmentation because (1) DL models tend to need a large amount of labeled data to train, and (2) it is highly time-consuming and label-intensive to voxel-wise label 3D medical images. Significantly reducing annotation effort while attaining good performance of DL segmentation models remains a major challenge. In our preliminary experiments, we observe that, using partially labeled datasets, there is indeed a large performance gap with respect to using fully annotated training datasets. In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation. We achieve this by (i) selecting representative slices in 3D images that minimize data redundancy and save annotation effort, and (ii) self-training with pseudo-labels automatically generated from the base-models trained using the selected annotated slices. Extensive experiments using two public datasets (the HVSMR 2016 Challenge dataset and mouse piriform cortex dataset) show that our framework yields competitive segmentation results comparing with state-of-the-art DL methods using less than ~ 20% of annotated data.
LAMOL: LAnguage MOdeling for Lifelong Language Learning

Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling. LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples. When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task. The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound. The source code is available at this https URL.
Segmented Spherical Projection-Based Blind Omnidirectional Image Quality Assessment

In contrast with traditional images, omnidirectional image (OI) has a higher resolution and provides the user with an interactive wide field of view. OI with equirectangular projection (ERP) format, as the default for encoding and transmitting omnidirectional visual contents, is not suitable for quality assessment of OI because of serious geometric distortion in the bipolar regions, especially for blind image quality assessment. In this paper, a segmented spherical projection (SSP) based blind omnidirectional image quality assessment (SSP-BOIQA) method is proposed. The OI with ERP format is first converted into that with SSP format, so as to solve the problem of stretching distortion in the bipolar regions of ERP format, but retain the equatorial region of ERP format. On the one hand, considering that the bipolar regions of the SSP format are circular, a local/global perceptual features extraction scheme with fan-shaped window is proposed for estimating the distortion in the bipolar regions of OI. On the other hand, the perceptual features of the equatorial region are extracted with heat map as weighting factor to reflect users' visual behavior. Then, the features extracted from the OI's bipolar and equatorial regions are pooled to predict the quality of distorted OIs. The experiments on two databases, namely CVIQD2018 and MVAQD databases, demonstrate that the proposed SSP-BOIQA method outperforms the state-of-the-art blind quality assessment methods, and is more consistent with human visual perception.
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

A deep learning type of architecture used in CF combines wide & deep learning [36] [37]; the wide component is a single-layer perceptron, whereas the deep component is a MLP.Combining the two learning techniques enables the RS to capture both generalization