Reviewerly/exHarmony huggingface dataset:
Developing an Automatic Speech Recognizer For Filipino with English Code-Switching in News Broadcast

Closed-captioning systems are well-known for video-based broadcasting companies as society transitions into internet-based information consumption. These captioning systems are utilized to cater to most consumers. However, a captioning system for the Filipino language is not readily available to the public. News anchors in the Philippines tend to incorporate a code-switching behavior that mixes English and Filipino languages, which are the two major languages that Filipinos use. The goal of this research is to develop an automatic speech recognizer (ASR) for a captioning system for Filipino news broadcast domain videos. Experiments on finding the optimal speech models and features, and on how code-switching affects the system were conducted. Best results were obtained by using linear discriminant analysis with maximum likelihood linear transform (LDA+MLLT) and speaker adaptive training (SAT) for acoustic modeling. Initial investigation also shows that there is no general pattern for the ASR&#x0027;s performance as a function of code-switching frequency.
A dataset for voice-based human identity recognition

This paper introduces a new English speech dataset suitable for training and evaluating speaker recognition systems. Samples were obtained from non-native English speakers from the Arab region over the course of two months. The dataset was divided into two sub-datasets. Ten samples were collected from each speaker for each sub-dataset. The first sub-dataset contains samples of speakers repeating the phrase "Machine learning 1, 2, 3, 4, 5, 6, 7, 8, 9, 10". The second sub-dataset contains samples for the same speakers speaking randomly for five to ten seconds for each sample. The dataset consists of 150 speakers with a total of 3,000 data samples and about six hours of speech.
Quantized photonic neural network modeling method based on microring modulators

Photonic chips have great potential for neural network computing due to their fast speed, low power consumption, and parallelism. We propose a quantized neural network modeling method based on microring resonators (MRR). We analyze the optical properties of the MRRs and utilize lasers with different wavelengths as inputs of the neural network. The quantization aware method is adopted to train the neural network, and the stochastic search method is utilized to determine hyperparameters of the network. We transform the network parameters and hyperparameters into MRR parameters to simulate neural network matrix multiplication operations. Finally, we used the Mixed National Institute of Standards and Technology database for testing the proposed model. For 4-, 5-, and 6-bit quantization of weight parameters, we obtain classification accuracies of 94.23%, 94.73%, and 96.11%, respectively. Thus our study demonstrates the feasibility of building a neural network inference system using a microring structure and provides a theoretical support for applying MRRs in neural networks.
The HWDI Dataset of Camera Captured Warped Hindi Text Document Images

Nowadays, capturing text images and using the information contained in these texts have become an integral part of almost everyone&#x2019;s life in one way or the other. Although a myriad of datasets and advancements have flourished for the Alphabetic scripts such as English for text recognition, text line segmentation, and extraction processes, not much has been developed for Alpha Syllabary scripts such as Hindi. For instance, there is no publicly available dataset of warped text document images for Alpha Syllabary script Hindi for the use of community to work and experiment on. Hence, as a first move in the progression of the above-mentioned field, this paper introduces a novel dataset (HWDI - HINDI WARPED DOCUMENT IMAGES) of camera-acquired text document images in Alpha Syllabary script Hindi and the methodology employed to produce it. The dataset contains 253 warped images of varying warping, having convex or concave surfaces and additionally differs in the area where warping is present, namely, left warping, right warping, and middle warping that challenge the existing text line segmentation techniques. We have also provided ground truths and flatbed-scanned images of the warped images along with the dataset for the use of researchers for various purposes. This innovative dataset will benefit research society to further take ahead the development in the field of text line segmentation, text recognition, dewarping, text extraction and many more developments for Hindi language.
Anti-aliasing of gray-scale/color/outline images: Looking through the lens of numerical approaches for PDE-based models

Down-sampling in the process of saving and transferring images often leads to the appearance of jaggies or step like edges in the objects of images where they should be smooth to seem natural. The application of blurring filters for the purpose of anti-aliasing images seems inappropriate since it leaves a blurring effect that destroys the edges in a way that they become indistinguishable. In 2012 Ziou and Horé proposed a novel partial differential equation (PDE) in which anti-aliased images efficiently. However, solving this equation as it is severely nonlinear and defined in a large domain due to the size of images is a challenging problem. In this paper, we propose a new numerical method that is based on a combination of iterative operator splitting (IOS) technique with Gaussian radial basis functions (RBFs) method for solving this equation in an efficient way. The proposed method converts the problem to several one-dimensional linear problems that are solved in smaller sizes to overcome the large domain of the problem. The Crank-Nicolson method is applied for the discretization of the time dimension. The proposed method is tested on different gray-scale, color, and outline images to illustrate the efficiency of the proposed method visually and numerically in the section of numerical results.
Stance Detection Based on User Feature Fusion

Rapid development of the Internet has contributed to the widespread adoption of social network platforms. Network media plays an important role in the process of public opinion dissemination and bears significant social responsibility. Public opinion mining is of great significance for online media to improve the quality of content provision and enhance media credibility. How to make full use of user-generated content is the key to improving the accuracy of position detection tasks. In this paper, we proposed a stance detection model based on user feature fusion by using comments of netizens in false news events on Weibo as research content. The method of feature fusion is adopted to integrate vectors including user sentiment, cognitive features, and text feature at the feature layer for model training and position prediction. The model is evaluated on a dataset of related microblog comments in false news. The result shows that our proposed method has a certain improvement in the effect of stance detection.
Recognition and Error Correction of Piano Playing Features Based on Filtering Technology

The extraction of piano features needs to be based on mature technology, but there is no mature technology for feature recognition of music to ensure the correct rate of feature extraction. This article combines filter processing technology to carry out the research on piano playing feature recognition and builds an intelligent error correction system, and it realizes the feature recognition and error correction of piano playing with the support of filter technology. Moreover, this article conducts experimental research on the system constructed. Through experimental research, it can be seen that the piano performance feature recognition system proposed in this article can effectively recognize the piano practitioner’s playing effect and record it for error correction. This intelligent method can effectively improve the piano playing effect.
An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning

Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework’s training performance. Furthermore, we summarize eight privacy-sensitive information classes on which existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.
Research on Application of Sports Training Performance Prediction Based on Convolutional Neural Network

In order to improve the prediction effect of sports training performance and improve the effect of sports training, this paper classifies the sports training image area, refines the image into different areas, finds suspicious areas, and completes the error prediction. Moreover, this paper calculates the regional similarity of sports training images in the fully connected layer of the convolutional neural network and introduces the local linear weighting method for analysis. In addition, this paper gives a certain weight to each prediction point near the area to be predicted and selects the suspicious area feature based on the multievaluation standard fusion method. Finally, this paper combines the convolutional neural network algorithm to construct a sports training performance prediction system to improve the effect of sports training and design experiments to verify the system proposed in this paper. From the experimental research results, we can see that the sports training performance prediction system based on the convolutional neural network proposed in this paper has good practical effects.
Impact of the b-Learning Model on University Teaching

In this work, the impact of the implementation of B-Learning and traditional face-to-face models is analyzed, by comparing the academic results achieved by four groups of university students in the electronics course, by using objective and subjective measurements. Analysis of the results based on objective data demonstrated a lower student dropout rate and an improvement in the general grade point average under the B-Learning model. The subjective comparison collects student’s opinions through a formal evaluation instrument. This analysis concluded that students perceive the teaching-learning process under the B-Learning model more motivating, more useful and that it meets their expectations in a better way. Finally, the Student's t test is carried out to demonstrate that the analyzed research variables present statistically significant differences.
Have a Clue! The Effect of Visual Cues on Children’s Search Behavior in the Classroom

We study the effect of visual cues on children searching in the classroom. We do so by examining whether Search Engine Result Pages (SERP) enhanced with emojis, unlike standard SERP, affect children's search behaviour. To capture search behaviour, we use well-known metrics, in addition to users' success in identifying relevant results on SERP. Outcomes from our work reveal that a one-size-fits-all approach for SERP does not befit students who are searching for learning. Thus, we discuss the implications of our findings and suggest directions for future research, focused on the design and evaluation of information retrieval systems that can better support the classroom setting.
Gradient Scheduling With Global Momentum for Asynchronous Federated Learning in Edge Environment

Federated Learning has attracted widespread attention in recent years because it allows massive edge nodes to collaboratively train machine learning models without sharing their private data sets. However, these edge nodes are usually heterogeneous in computational capability and statistically different in data distribution, i.e., non-independent and identically distributed (IID), leading to significant performance degradation. Although status quo asynchronous training methods can solve the heterogeneity issue, they cannot prevent the non-IID problem from reducing the convergence rate. In this article, we propose a novel paradigm that schedules the gradient with partially averaged gradients and applies the global momentum (GSGM) for asynchronous training over non-IID data sets in an edge environment. Our key idea is to apply global momentum and partial average on the biased gradients calculated on edge nodes after scheduling, to make the training process stable. Empirical results demonstrate that GSGM can well adapt to different degrees of non-IID data and bring 20% performance gains in terms of training stability for popular optimization algorithms with enhanced accuracy over Fashion-Mnist and CIFAR-10 data sets.
Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non-rigid transformations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent representations, and involves two factorised transformation networks (one per each encoder-decoder channel) and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments. We focus on explaining model-data components to enhance model explainability in medical image registration. On computational time experiments, we show that the FIRE model performs on a memory-saving mode, as it can inherently learn topology-preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi-modal image registrations in the clinical setting.
2K-Fold-Net and feature enhanced 4-Fold-Net for medical image segmentation

For segmenting medical images, U-Net has become a popular and effective tool. However, it also has some shortcomings in segmenting fuzzy boundaries and eliminating interferences. Improvements of the original U-Net have been proposed by many authors, resulting in many variants such as MultiResUNet, DoubleU-Net and W-Net. Based on the common characteristics of these structures, we propose in this work a generalized structure by multiplying the folds of a fully convolutional network (FCN) for even more times, and thus name it as "2K-Fold-Net". The more folds in this structure provide more freedoms to create cross links between the neighboring folds. The influence of the fold-pair number K on its performance is also studied. The realizations with K up to 6 are compared to three other variants of cascaded U-Nets using the CVC-ClinicDB dataset. Then the special case "4-Fold-Net" is further empowered with the feature enhancing functionalities recently seen in the attention-aware feature enhancement method. This new net is hence named as "Enhanced-Feature-4-Fold-Net", abbreviated as "EF3-Net". Finally, 2K-Fold-Net and EF3-Net have been compared with U-Net, SegNet, DoubleU-Net, MultiResUNet and its variants using four challenging medical image datasets. The results have demonstrated that the proposed nets outperform the other variants of U-Net, even with slightly lower amount of parameters. The code is available on: https://github.com/raik7/EF3-Net.
Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.
PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service. Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present PowerMorph , a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind PowerMorph is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit.
Toward Understanding Playful Beverage-based Gustosonic Experiences

Recent advances in interactive technology are being used to enrich our interactions around food and drinks. Making use of sound to enrich dining - providing "gustosonic" experiences - has recently garnered interest as an exciting area of ubiquitous computing. However, these experiences have tended to focus on eating. In response to the lack of drinking-focused experiences, we explore the combining playful design and drinking activities so as to allow users to experience playful personalized sounds via drinking through "Sonic Straws". We present the findings of an in-the-wild study that highlights how our system supported self-expression via playful drinking actions, facilitated pleasurable social drinking moments, and promoted reflection on drinking practices. We discuss the implications of this work for designers of future gustosonic experiences, including how to amplify entertainment experiences around drinking/eating, how to highlight the joy coming from multisensory experiences, and how to facilitate mindful engagement with what one drinks. Ultimately, we aim to contribute to the enrichment of dining experiences through playful design.
The Strategy of Underground Construction Objects Planning Based on Foresight and Cognitive Modelling Methodologies

The strategy of underground construction objects planning based on the mathematical support of foresight methodology with the aim of scenarios alternatives creating and cognitive modelling to build scenarios for the development of the desired future and ways of their implementation is proposed. These methodologies are suggested to be used together: the obtained results at the stage of the foresight methodology are used as initial data for cognitive modelling. Using the foresight process at the first stage of modelling allows to identify critical technologies and build alternative scenarios with quantitative characteristics applying expert assessment procedures. For the justified implementation of a particular scenario, cognitive modelling is used, which allows building causal relationships considering a large number of interconnections and interdependencies. The suggested strategy is applied to the study of underground communications in order to select reasonable scenarios for justification of their creation priority.