Reviewerly/exHarmony huggingface dataset:
Researching Into the Use of Real-Life Materials in Language Learning Apps

The study reported in this chapter investigates how mobile language apps facilitate the learning of L2 Chinese, focusing on the use of real-life materials in the design of HANZI – a mobile app specifically developed for this study. Guided by the pedagogy of using authentic materials in language learning and some key design principles of language mobile apps, this chapter explores beginner Chinese learner experiences of mobile-assisted Chinese language learning. It is a qualitative-led case study with multiple datasets including descriptive statistics from 111 survey responses, student focus group discussions, and teacher interviews. The analysis reveals that real-life materials can promote learners' motivation, personalised learning, and social interaction, which improve language proficiency. Such materials should be included in language activities in both digital and print media because they are central to overcoming the challenges of learning Chinese characters.
PFMNet: Few-Shot Segmentation with Query Feature Enhancement and Multi-Scale Feature Matching

The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable to make better predictions, for new categories of information that have never been seen before, than the few-shot segmentation that has emerged. However, the few-shot segmentation is still faced up with two challenges. One is the inadequate exploration of semantic information conveyed in the high-level features, and the other is the inconsistency of segmenting objects at different scales. To solve these two problems, we have proposed a prior feature matching network (PFMNet). It includes two novel modules: (1) the Query Feature Enhancement Module (QFEM), which makes full use of the high-level semantic information in the support set to enhance the query feature, and (2) the multi-scale feature matching module (MSFMM), which increases the matching probability of multi-scales of objects. Our method achieves an intersection over union average score of 61.3% for one-shot segmentation and 63.4% for five-shot segmentation, which surpasses the state-of-the-art results by 0.5% and 1.5%, respectively.
Applications of Multilingual Phone Recognition in Code-Switched and Non-code-Switched Scenarios

This chapter describes the applications of multilingual phone recognition in code-switched and non-code-switched scenarios. It compares two approaches for multilingual phone recognition using code-switched and non-code-switched test sets. The development and evaluation of Multi-PRSs using LID-Mono and common multilingual phone-set based approaches are described. The analysis and comparison of the results are provided. The code-switched speech recognition using Multi-PRSs is studied using code-switched speech data of Kannada and Urdu languages.
A Self-Tallying Electronic Voting Based on Blockchain

Abstract Electronic voting (e-voting) has been studied for many years. Recently, researchers find that blockchain can provide an alternative secure platform for e-voting systems, because of its properties of tamper resistance and transparency. However, existing blockchain-based schemes either require central authorities to tally ballots or can only handle a limited number of voters. This paper tries to propose a self-tallying e-voting system, i.e. the public can verify the validity of all ballots and tally the ballots without a centralized authority. To achieve this goal, we solve two challenges, namely how to cancel out all random numbers used for ballots and to prove the validity of ballots using a non-interactive zero knowledge proof. Our scheme is proved to be secure and shown to be practical by experiments.
Mechanism Analysis and Mitigation of Visual Navigation System Vulnerability

Autonomous systems have attracted significant interest from academia and industry in recent years. To achieve accurate and reliable positioning, and to improve navigation robustness in complex operational scenarios, visual navigation has become indispensable. However, visual systems are vulnerable to many influencing factors. Therefore, this paper introduces the “vulnerability” concept for visual navigation, which originates from satellite navigation. We analyze the underlying causes of visual vulnerability and propose mitigation methods accordingly. Following the pipeline of visual navigation systems, several internal and external vulnerability factors are revealed: image blur, object motion, incorrect feature matching, biased depth recovery, repeated texture, etc. The causing and influencing mechanisms are analyzed, based on which we propose some mitigation methods. Using the well-known open-source datasets (e.g., TartanAir, EuRoc), it is proved that visual navigation is highly and frequently vulnerable due to the factors above. Some typical examples are offered to illustrate the vulnerability factors. Besides, experiments are carried out to validate the proposed vulnerability mitigation approaches, and the results show their effectiveness.
Scalable Bayesian High-dimensional Local Dependence Learning

In this work, we propose a scalable Bayesian procedure for learning the local dependence structure in a high-dimensional model where the variables possess a natural ordering. The ordering of variables can be indexed by time, the vicinities of spatial locations, and so on, with the natural assumption that variables far apart tend to have weak correlations. Applications of such models abound in a variety of fields such as finance, genome associations analysis and spatial modeling. We adopt a flexible framework under which each variable is dependent on its neighbors or predecessors, and the neighborhood size can vary for each variable. It is of great interest to reveal this local dependence structure by estimating the covariance or precision matrix while yielding a consistent estimate of the varying neighborhood size for each variable. The existing literature on banded covariance matrix estimation, which assumes a fixed bandwidth cannot be adapted for this general setup. We employ the modified Cholesky decomposition for the precision matrix and design a flexible prior for this model through appropriate priors on the neighborhood sizes and Cholesky factors. The posterior contraction rates of the Cholesky factor are derived which are nearly or exactly minimax optimal, and our procedure leads to consistent estimates of the neighborhood size for all the variables. Another appealing feature of our procedure is its scalability to models with large numbers of variables due to efficient posterior inference without resorting to MCMC algorithms. Numerical comparisons are carried out with competitive methods, and applications are considered for some real datasets.
A Sub-Milliwatt and Sub-Millisecond 3-D Gaze Estimator for Ultra Low-Power AR Applications

The critical factors of real-time gaze tracking are high accuracy and user-friendliness such as low latency and no run-time calibration. Existing gaze estimator hardware designs are based on 2D regression algorithms as 2D methods have a simple computation process. However, they require multiple run-time calibration steps, and are vulnerable to head motions. On the other hand, the 3D model-based method can maintain better accuracy than the 2D method without run-time calibration steps, and is robust to head motions. In this paper, we aim to design the first 3D model-based gaze estimator hardware that consumes less than 1mW power and 1ms latency per frame. The simulation results based on the hardware synthesis show that the proposed design requires 172μW and 0.5ms per frame, while maintaining less than 0.9° error.
Design and Implementation of Scientific Research Big Data Service Platform for Experimental Data Managing

The goal of the scientific research big data service platform for experimental data managing is to solve the problem of data silos in the state-owned scientific research management system caused by backward informatization. The new data service platform integrates data collection, data analysis, data governance, monitoring and management, prediction and early warning, and visualization platform. We are committed to improving data management and service capability with informatization, and to grasp the material development and design situation timely and accurately. The goal of our platform is to truly use data to speak, manage and make decisions with data.
A Bayesian approach to Expert Gate Incremental Learning

Incremental learning involves Machine Learning paradigms that dynamically adjust their previous knowledge whenever new training samples emerge. To address the problem of multi-task incremental learning without storing any samples of the previous tasks, the so-called Expert Gate paradigm was proposed, which consists of a Gate and a downstream network of task-specific CNNs, a.k.a. the Experts. The gate forwards the input to a certain expert, based on the decision made by a set of autoencoders. Unfortunately, as a CNN is intrinsically incapable of dealing with inputs of a class it was not specifically trained on, the activation of the wrong expert will invariably end into a classification error. To address this issue, we propose a probabilistic extension of the classic Expert Gate paradigm. Exploiting the prediction uncertainty estimations provided by Bayesian Convolutional Neural Networks (B-CNNs), the proposed paradigm is able to either reduce, or correct at a later stage, wrong decisions of the gate. The goodness of our approach is shown by experimental comparisons with state-of-the-art incremental learning methods.
A CNN Based Anomaly Detection System for Real Time Fog Based Application

The rapid growth in technology and everything becoming smart the adaptability of intelligent technologies in our daily life has taken a prominent place. In the view to maintain and stock the data cloud is being used. To condense the issues like latency and energy consumption fog computing has been introduced. However many techniques are present to find the anomaly present in the data but deeper inside no works are found to detect anomaly of in network. In order to provide system that identifies the anomaly present in network while transmitting data at fog level and to aggregate those anomaly if found using a Machine learning technique is proposed in the paper. With a fog based eco system built, further taking up a neural network algorithm that is convolution neural network a system for detecting anomaly and aggregating data has been anticipated in the paper.
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning

Ensemble and auxiliary tasks are both well known to improve the performance of machine learning models when data is limited. However, the interaction between these two methods is not well studied, particularly in the context of deep reinforcement learning. In this paper, we study the effects of ensemble and auxiliary tasks when combined with the deep Q-learning algorithm. We perform a case study on ATARI games under limited data constraint. Moreover, we derive a refined bias-variance-covariance decomposition to analyze the different ways of learning ensembles and using auxiliary tasks, and use the analysis to help provide some understanding of the case study. Our code is open source and available at https://github.com/NUS-LID/RENAULT.
A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

Knowledge graph, as a semantic network, provides a method to model knowledge from the perspective of entities and their relationships. Since a large quantity of material knowledge is widely distributed, using the structure of the knowledge graph is an effective approach to clarify the internal connections between related knowledge. Named entity recognition is a crucial step to construct a knowledge graph. However, most of the professional domain datasets are label-free and collected from multiple heterogeneous sources. To identify the entities from these datasets, a rule-based and deep learning hybrid method is proposed. The knowledge extracted by regular expression was used to semi-automatically label the corpus, and then trained by deep learning model, which achieved good results. The constructed knowledge graph regarding to the commonly used steels is of great significance to the integration and management of related knowledge, and it lays a foundation for future engineering application.
High-Speed Non-Contact Thermal Display Using Infrared Rays and Shutter Mechanism

Considerable literature has explored the topic of haptics, including thermal presentation, in virtual reality (VR). However, the existing thermal presentation methods have limitations such as low responsiveness and need for contacts. As such, this study proposes a high-speed, non-contact thermal presentation method using infrared rays and a shutter mechanism consisting of fins. The results of the preliminary study suggested that the method could present temperature with different intensities according to the angle of the fin. Finally, several applications taking advantage of its features of high-speed and non-contact thermal presentation are proposed.
Weakly-Supervised Salient Object Detection through Object Segmentation Guided by Scribble Annotations

With the advent of Neural Network, Fully-supervised salient object detection achieves great success. However, it takes plenty of efforts to obtain precise pixel-level annotations. In order to reduce human labeling efforts, some research adapt weak form annotations, but they still fall short of the fully-supervised. In this paper, we propose a novel weakly-supervised salient object detection framework, which can reduce labeling efforts by using scribble annotations. In the meantime, we also incorporate Deep Convolutional Network to achieve high performance. To this end, we utilize high-quality region hierarchies, which are generated by Convolutional Oriented Boundary (COB) network, to select optimal level for object segmentation. We build initial saliency maps and thoroughly annotate the images during the initialization phase by spreading labels information from scribbles to other regions. During the training phase, the salient object detection convolutional network is trained using the initial saliency maps. Then, we utilize Conditional Random Field (CRF) to refine saliency maps, which will then be used to retrain the network. To achieve quality saliency maps, we iteratively optimize the training process. Extensive experiments on five benchmarks demonstrate that our proposed method outperforms previous weakly-supervised algorithms.
Timeception Single Shot Action Detector: A Single-Stage Method for Temporal Action Detection

Temporal action detection is used to detect the start and end times and classify the potentially specific actions in a video. Prior studies in temporal action detection perform weak because they can not fully understand the whole input video's temporal structure and context information, and fail to adapt to the diversity of action time span. We propose a novel Timeception Single Shot Action Detector (TC-SSAD) to solve the problems mentioned above. In detail, we leverage the multiple Timeception layers to generate multi-scale feature sequences, where each Timeception layer uses depthwise-separable temporal convolution with multi-scale convolution kernels to capture the diversity of time spans. Besides, we use the super-event modules to learn the entire input video’s temporal structure and contextual information. The experimental results on THUMOS14 dataset show that when IoU threshold is 0.5, our method achieves 38.2% and 44.3% mAP on Two-stream features and Two-stream i3D features respectively, which is better than Decouple-SSAD network based method by 2.4% and 0.6%. Our method on Activitynet-1.3 dataset achieves 20.4% mAP, which is better than Decouple-SSAD network based method by 0.61% as far as Two-stream features on concerned.
Edge Prior and Spatial Attention Fusion Enhanced Hierarchical Multi-Patch Network for Image Deblurring

How to exploit useful features to enhance the quality of blurred images is a long-standing topic in single image deblurring. Existing learning-based approaches show exciting performance by increasing the receptive fields depending on multi-scale and scale recurrent strategy. However, it is still a challenging task for deblurring to enlarge the receptive field only relying on increasing the number of layers of a neural network. To tackle this challenge, we propose a multi-scale spatial and edge attention enhanced model (MSEA) for image deblurring. Firstly, edge features are extracted to guide the network's attention to the recovery of fine details and texture information. Then we introduce spatial attention fusion mechanism for the adaptive fusion of features derived from edge maps and blurry images, and those representing shallow fine-grained details and in-depth abstract features. Qualitative and quantitative evaluation results over GoPro and VideoDeblurring datasets demonstrated the improved performance, especially when there are sharp edges and rich textures.
Multi-scale Deformable Deblurring Kernel Prediction for Dynamic Scene Deblurring

Deblurring aims to restore clear images from blurred ones. Recently deep learning are widely used. Previous methods regard deblurring as dense prediction problems and rarely consider the inverse operation of blur. In this paper, we propose a multi-scale deformable deblurring kernel prediction network for dynamic scene deblurring which uses a coarse-to-fine method to predict the per-pixel deformable deblurring kernel and uses the fusion weight to integrate the latent images in different scales. Since the spatially variable blur scatters pixel information to surrounding sub-pixels and leads to the spatially and quantitively uneven distribution of latent pixel information, the per-pixel deformable deblurring kernel can adaptively select the sub-pixels and linearly combine them into the clean pixel for information aggregation. The multi-scale architecture helps the deformable deblurring kernel enlarge the reception field. The residual image is added to convolution result in each scale to supply refined edges when the kernel cannot cover the areas existing latent pixel information. Besides, we add local similarity loss to constrain deformable deblurring kernel’s weight and offset which boosts the deblurring performance. Qualitative and quantitative experiments show that our method can produce competitive deblurring performance.
Tangled: A Conventional Processor Integrating A Quantum-Inspired Coprocessor

Quantum computers use quantum physics phenomena to create specialized hardware that can efficiently execute algorithms operating on entangled superposed data. That hardware must be attached to and controlled by a conventional host computer. However, it can be argued that the main benefit thus far has been from reformulating problems to make use of entangled superpositions rather than from use of exotic physics mechanisms to perform the computation – such reformulations often have produced more efficient algorithms for conventional computers. Parallel bit pattern computing does not simulate quantum computing, but provides a way to use non-quantum, bit-level, massively-parallel, SIMD hardware to efficiently execute a broad class of algorithms leveraging superposition and entanglement.
Image Deblurring Algorithm by using Conditional Generation Adversarial Network

Deblurring from a motion blurred image has been studied in recent years. Recently, convolution neural network (CNN) has been used widely, which can be used on finding the blur kernel or the latent sharp edge of a blurred image. The last five years, the generative adversarial network (GAN) performs well on style transformation. It is often difficult to maintain a relatively static state between shooting equipment and the photographed object in real life and industrial production, so motion blur is easy to appear in the image taken. To solve this problem, we can use DeblurCGAN to deblur motion blur images. The model can effectively remove the motion blur in moving images in the experimental process. At the same time, compared with the traditional blur kernel, the algorithm not only can get a clearer image but also to restore the texture and detail in the image. Both in the image evaluation index peak signal-to-noise ratio and structural similarity measurement can be verified this method can achieve a better deblurring effect.
Utterance Style Transfer Using Deep Models

The paper describes a solution to utterance style transfer within the speaker’s identity and emotional tone exchange while maintaining the utterance’s content. Using deep generative neural networks, we developed two models that differ in introducing information related to the speaker’s identity and an additional variable representing the expected emotional category. The embedding of emotions is taken from the convolutional network that was trained to classify emotional categories. Siamese network is responsible for learning the content embeddings. The models can perform the style transfer between any two speakers with a satisfactory result. This assessment is based on a survey that considers the degree of content retention, the quality of transferring voice features related to identity, and the degree of converting emotional features into a desirable category.
Providing Security to private cloud Using AES Algorithm

In this digital era, people are generating huge amount of data. This necessitates the need to develop a specific device to store all the data. Generally, the data will be stored in pen drives, hard drives, etc. sometimes the data will be lost due to the corrupt devices. To overcome all these issues, this research work has implemented a cloud space for storing the data and it provides more security to the data. The data can just be accessed by using the internet from anywhere in the world. All these techniques were implemented with the java by using Net beans IDE. Once user uploads the data, the user will not have any rights to change the data. User uploaded files are stored in the cloud with the file name as system time and a directory will be created with some random words. Also, the cloud accepts the data only if the size of file is less than 2MB.
Articulated Human Pose Estimation Using Greedy Approach

The goal of this Chapter is to introduce an efficient and standard approach for human pose estimation. This approach is based on a bottom up parsing technique which uses a non-parametric representation known as Greedy Part Association Vector (GPAVs), generates features for localizing anatomical key points for individuals. Taking leaf out of existing state of the art algorithm, this proposed algorithm aims to estimate human pose in real time and optimize its results. This approach simultaneously detects the key points on human body and associates them by learning the global context. However, In order to operate this in real environment where noise is prevalent, systematic sensors error and temporarily crowded public could pose a challenge, an efficient and robust recognition would be crucial. The proposed architecture involves a greedy bottom up parsing that maintains high accuracy while achieving real time performance irrespective of the number of people in the image.