Reviewerly/exHarmony huggingface dataset:
Integration of Knowledge-Driven and Data-Driven Based Korean Phonetic Transcription

In the construction of resources for Korean phonetic information processing, automatic phonetic transcription technology plays a crucial role. However, because Korean word formation is extremely powerful and new words emerge regularly, it is impossible to create a database that contains all of the words. As a result, in addition to the words in the database, how to solve the pronunciation annotation of those unregistered words outside the database, referred to as OOV (out of vocabulary) words, has turned into a problem that must be resolved in the process of Korean natural language processing. The current academic approaches to grapheme-to-phoneme (G2P) conversion techniques have been commonly knowledge-based or data-based. Previously, the methods which were only knowledge-driven based are difficult to adapt to the actual situation of a large amount of data information. The data-driven approach relies solely on high-quality data, makes it difficult to reasonably determine the input variables, and necessitates the use of adequate and precisely selected model features. To address these issues, the paper proposes a knowledge-driven and data-driven fusion based an automatic Korean language G2P method. Firstly, we extract eigenvalues based on the pronunciation rules and the phonetic changing rules between words in Korean, and feed them in to the model for training. And then, the model is trained to achieve the automatic phonetic transcription for Korean using the data-driven model, which can better fit the mapping relationship between input and output variables. The proposed model can reflect the phonological changes in the Korean continuous speech stream, and can accurately obtain the phonemes corresponding to the graphemes. The method has been cross-validated for validity and superiority to improve model performance, and the average accuracy on grapheme-to-phoneme conversion can reach 94.63%.
WebAR Object Detection Method Based on Lightweight Multiscale Feature Fusion

In the field of mobile augmented reality, object detection methods based on deep learning have become a research hotspot. Aiming at the problem of low detection accuracy and inability to meet the real-time detection target of mobile devices in the case of complex scenes and target occlusion, a WebAR object detection scheme with a lightweight neural network structure is proposed. First, using the lightweight network Mobilenet as the backbone feature extraction network of the SSD model of the object detection framework can significantly reduce the number of model parameters and computation and meet the real-time detection of targets. Second, a multi-scale feature fusion module is proposed. The Kronecker convolution and feature pyramid network are introduced to expand the receptive field. Capture richer multi-scale feature information, reduce the loss of important information, and improve the precision measurement accuracy. Finally, an object detection system based on WebAR is designed to realize the recognition of 2D images. The WebAR detection system designed with this model improves the efficiency of users' work. Experiments show that the improved model in this paper achieves better detection results on the PASCAL VOC2007 and MS COCO test dataset.
Attentional Fusion TDNN for Spoof Speech Detection

In this article, for spoof speech detection, we propose AF-TDNN model, which based on ECAPA-TDNN. Utilizing multi-scale feature aggregation methods and Attentional Fusion Module (AFM), this architecture can get the fusion information of all convolution layers. This fusion information can help the network get more implicit feature of one utterance so that achieving better evaluation metric results than original model. Our work is devoted to finishing ASVspoof 2021 LA and DF task. We take different data augmentation strategies to deal with the domain mismatch of training data and test data. In addition, we introduce multi-head attentive statistic pooling to explore the impacts of this mechanism. According to the experiments’ results, our proposed model outperforms the baseline models and original model in most situations. The proposed AF-TDNN model achieves best EER of 5.65% and min-tDCF of 0.3104 in ASVspoof 2021 LA task. In DF task, AF-TDNN model get best EER of 21.12%.
Panoramic Stitching of Reactor Core Image

Since the nuclear reactors need frequent replacement of fuel rods, it is necessary to accurately determine the type of reactor core fuel rods and their installation positions through image stitching technology. Herein, the global and local virtual two-dimensional coordinate mapping models have been established in terms of the distribution relationship of fuel rods installation positions. When identifying the local target, the local pictures are taken, then the location of local targets in the fuel rods are recognized through deep learning. After that, each local virtual 2D coordinate model is rotated, translated and scaled by the location of the local targets. Afterwards, the position of the fuel rod in each local image and the position coordinates of the transformed local virtual two-dimensional coordinate mapping model are employed to determine the spatial distance. The fuel rod is roughly positioned, and finally the position of the fuel rod is precisely positioned by template matching. Therefore, the reactor core panoramic image can be obtained. The simulation results display the proposed strategy can effectively detect the category and installation positions of fuel rods. Moreover, the stable panoramic stitching result indicates this system have the wonderful application prospect in the proofreading work of the reactor core fuel rods.
Low-Light Image Enhancement Algorithm Based on Multi-scale Concat Convolutional Neural Network

We present a deep learning-based method for lowlight image enhancement. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain the problems of excessive enhancement, unclear details and performance degradation. To address these tasks, we propose the Multi-scale Concat Image Enhancement Network (MCIEN). The core of our approach is a feed-forward neural network that learns affine transforms of local and global features. By these means, our network is able to recover clear details, distinct contrast, and natural color in the enhancement results. We perform extensive experiments on the benchmark MIT-Adobe FiveK dataset, and show that our network is superior to other contrast algorithms in visual effects and quantitative evaluations.
An Automatic Pipeline Parallel Acceleration Framework for Neural Network Models on Heterogeneous Computing Platforms

With the widespread use of heterogeneous computing platforms in the AI field, neural network models on heterogeneous computing platforms are experiencing problems such as low execution efficiency and high deployment difficulty. To solve these problems, this paper proposes an automatic pipeline parallel acceleration framework for neural network models on heterogeneous computing platforms. Firstly, the framework divides neural network computing into multiple work task stages. It uses the software pipeline design method and multi-threading technology to realize the parallel execution of multiple work task stages in neural network model computing. Secondly, the framework provides a set of universal programming interfaces for automatic pipeline parallel acceleration function. Finally, the framework realizes the rapid and efficient deployment of the neural network model on the heterogeneous computing platform without the need for specialized knowledge. Experiments have verified that the framework can bring good speedup ratios while ensuring the accuracy of the model. In particular, the average speedup ratio on the object detection model can reach 1.36 times. With the increase of the image size on object detection models, the speedup ratio increases. Therefore, the automatic pipeline parallel acceleration framework can effectively improve the intelligent processing capability of the heterogeneous computing platform.
Experimental Assessments of a AWGR Based Optical Switching Network for Distributed Reinforcement Learning Training

An arrayed waveguide grating router based all-optical switching network is proposed to accelerate the communication phase of distributed reinforcement learning training. Experimental investigations validate 465ns worker-to-server latency with error-free communication at 0.9 traffic load.
Construction of Narrative Text Component Recognition Corpus

Textual structure analysis is an important part of Automatic Essay Score (AES), and is also one of the important research directions in Natural Language Processing. At present, there are still deficiencies in the research of narrative textual structure in China, one of the main reasons is the lack of data available for research. To solve this problem, this paper proposes and constructs a corpus for the textual component identification of narrative essay. This paper divides the text structure of narrative essay, and forms a corpus for the narrative essay component identification. The paper finally annotated 3024 articles with 21128 sentences in total. This paper combines manual annotation and the automatic annotation of the model to build corpus, and conducts statistical analysis on the distribution of the corpus content and the consistency of the corpus annotation. The experiment shows text component recognition performance achieves 80.75% F 1 score. The work provided basic data for the research of AES.
Performance Analysis of Amplified Spontaneous Emission Noise in Quantum Noise Stream Cipher Transmission System

We simulate and analyze the impact of amplified spontaneous emission noise with different noise figures of erbium-doped fiber amplifier on the quantum noise stream cipher system from the perspective of transmission and security performance.
Training Agent to Play Pac-Man under Authentic Environment Based on Image Recognition

The development of reinforcement learning (RL) in video games is becoming increasingly popular. Most experiments were carried out in a simulated environment in the past, making it easy for agents to interact with the environment while it could not retain the original game mechanism, such as AI decision-making. In this study, we mainly use image recognition to construct an RL scenario for the agent to play the game Pac-Man in an authentic game environment. Based on the Deep Q-learning (DQN) algorithm, combined with the inspiration of A* and DFS (Depth-first Search) graph traversal algorithm, the behaviour of the agent has been optimised to get higher scores.
Artificial Intelligence Service by Satellite Networks based on Ensemble Learning with Cloud-Edge-End Integration

Satellite network is the necessary supplement to ground network for wireless coverage for remote area. At the same time, Deep Neural Networks (DNN) based artificial intelligence (AI) applications has been widely applied in many aspects of people's life, but most DNN models are deployed in cloud due to their high complexity. Since cloud is far from end users in remote area, cloud based AI service cannot meet the latency requirement for users at remote area. Satellite based edge computing is a promising solution to this problem, but the limited resources on satellite platform cannot support complex models. In this paper, we propose to integrate the cloud, satellite edges and the end users to form an AI service framework, and apply ensemble learning to provide efficient and reliable AI service. Two case studies on image classification task are performed to show the effectiveness of the proposed scheme.
Cloud-Based E-Learning Platform using Django

Because of today's education system, providing excellent education to students is extremely tough for educational institutions/colleges. Information technology can help students and educational institutions overcome the challenges they encounter owing to a lack of infrastructure and resources; nevertheless, the centralized approach is hindering progress. The Internet is now more widely dispersed than ever before, because of the widespread availability of a diverse variety of telecommunications devices, as well as music players like the iPad and iPod. Cloud computing is becoming increasingly popular in a variety of industries because of its many advantages, and educational institutions can benefit from this by reducing the cost of infrastructure by using cloud computing services. Additionally, e-learning is becoming a widely accepted and widely used trend. Many resources are needed for e-learning systems, including both Hardware and Software. Such investments are excessively expensive at many educational institutions.
KiriPhys: Exploring New Data Physicalization Opportunities

We present KiriPhys, a new type of data physicalization based on kirigami, a traditional Japanese art form that uses paper-cutting. Within the kirigami possibilities, we investigate how different aspects of cutting patterns offer opportunities for mapping data to both independent and dependent physical variables. As a first step towards understanding the data physicalization opportunities in KiriPhys, we conducted a qualitative study in which 12 participants interacted with four KiriPhys examples. Our observations of how people interact with, understand, and respond to KiriPhys suggest that KiriPhys: 1) provides new opportunities for interactive, layered data exploration, 2) introduces elastic expansion as a new sensation that can reveal data, and 3) offers data mapping possibilities while providing a pleasurable experience that stimulates curiosity and engagement.
Prediction of Short-Term Solar Radiation Using Machine Learning Methods

The renewable energy such as wind energy and solar energy has been established to offer benefits of sustainable energy sources and lowered environmental pollution levels. In addition to the benefits they offer, factors such as global warming and the growing energy crisis have led to a progressive increase in demand for renewable energy. To meet such a growing demand, there is a need for an efficient energy management system that promotes more accurate forecasting techniques. Forecasting to solar system output is mainly focused on the prediction of solar radiation. Numerous forecasting methods have been applied for achieving short-term solar radiation forecasting. This paper presents a comparative analysis of forecasting performance of three machine learning methods such as support vector machine, multiple linear regression, and artificial neural network. It is investigated that the prediction accuracy based on the multiple linear regression yields better results than that of the artificial neural network and support vector regression. Subsequent paragraphs, however, are indented.
Design of Adaptive Artificial Vehicle Acoustic System (AVAS) for an Electric Vehicle

One of the emerging technologies in the automobile sector is replacing an internal combustion engine with an electric motor to decrease the share of environmental pollution caused by automobiles. India is one of the countries which is adapting this technology intending to see all 150 cc two-wheelers and three-wheelers with IC engines replaced by electric vehicle by 2025 and 2023, respectively. One of the challenging tasks with the introduction of electric vehicles is its acoustic feature. As electric motors are silent in operation, it is difficult for pedestrians to notice the arrival and existence of vehicles near them. There is a need for technology that produces artificial sound as of internal combustion engine in electric vehicles to solve this problem. This project is one of the methods to solve the problem, as mentioned earlier. New technology is designed using an embedded system to produce the required and specific sound in electric vehicles.
Time bin quantum key distribution protocols for free space communications

Quantum key distribution (QKD) is a promising technology to enable secure cryptography after quantum computers have been developed. It allows for a key growing protocol that permits creating absolutely random keys to be used in the onetime pad codification scheme. Enabling a global QKD network is one of the final goals of the field. However, to do this with conventional optical fibres presents a fundamental limitation due to their intrinsic loss. Free-space, and specifically satellite links, have been proposed as an alternative and have gathered a lot of interest in recent years. They are considered one of the best candidates to enable a global network. Free-space QKD implementations are dominated by polarisation encoding protocols due to the relative transparency of the atmosphere to polarization. Nonetheless, time-bin and phase codifications offer some advantages and can be practical thanks to new passive interferometer designs. In this paper, the first free-space Coherent One-Way (COW) implementation is reported, some design considerations are commented, and the results of the experiment are shown. These show how time-bin/phase codifications are interesting candidates for free-space QKD.
Efficient embedding to solve the quantum linear systems problem in near-term quantum processors

Recent advances in quantum machine learning and quantum state embedding are integrated, providing a resource efficient framework for solutions of linear systems on Noisy Intermediate Scale Quantum (NISQ) machines. A divide and conquer algorithm is used to embed the indexing vector after which the Coherent Variational Quantum Linear Solver (CVQLS) algorithm is used to invert the problem matrix. This integrated procedure has an improved complexity scaling in the quantum resources needed to execute and produces solutions which agree with what is found classically.
Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This article introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval . Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions.
Compression artifact reduction of low bit‐rate videos via deep neural networks using self‐similarity prior

This paper presents a hybrid scheme that integratedly uses self-similarity prior and deep convolutional neural network (CNN) fusion for compression artifact reduction in low bit-rate video applications. Based on the temporal correlation hypothesis, the self-similarity prior is extended to the temporal domain by using as references not only the current decoded frame but also its neighbouring frames. Furthermore, being cognizant of that the bicubic downsampling process can typically improve the perceptual quality of a video coded at low bit-rate, for each small patch in the current frame, we search for similar patches in down-scaled versions of these references, and then form several self-similarity prior based predictions by tiling these similar patches at corresponding positions. To further exploit information flow across scales, a deep CNN model is constructed that contains two sub-networks to estimate the final output. One sub-network takes the self-similarity prior based predictions along with the decoded frame itself; and the other takes the down-scaled versions of these frames as network input. Experimental results demonstrate that the proposed method can remarkably improve, both subjectively and objectively, quality of compressed video sequences of low bit-rates.
Mitigation strategies for participant non-attendance in VR remote collaborative experiments

COVID-19 led to the temporary closure of many HCI research facilities disrupting many ongoing user studies. While some studies could easily move online, this has proven problematic for virtual reality (VR) studies. The main challenge of remote VR study is the recruitment of participants who have access to specialized hardware such as head-mounted displays. This challenge is exacerbated in collaborative VR studies, where multiple participants need to be available and remotely connect to the study simultaneously. We identify the latter as the worst-case scenario regarding resource wastage and frustration. Across two collaborative user studies, we identified the personal connection between the experimenter and the participant as a critical factor in reducing non-attendance. We compare three recruitment strategies that we have iteratively developed based on our recent experiences. We introduce a metric to quantify the cost for each recruitment strategy, and we show that our final strategy achieves the best metric score. Our work is valuable for HCI researchers recruiting participants for collaborative VR remote studies, but it can be easily extended to every remote experiment scenario.
Privacy Preserving Datamining Techniques with Data Security in Data Transformation

Data mining is the process of pattern recovery in multiple database fields. Big data is the great volume of data that is being processed in the environment of data mining. It is therefore impossible to use hand-held database management tools or typical data processing programme to collect data sets, so that data mining techniques have been deployed. An effective data transformation is a vital prerequisite for facilitating an efficient process for discovering information on a wider scale. I would want to mention that in present PPDM research, certain fundamental problems are not addressed. First of all, Privacy Preserving Data Mining (PPDM) does not have a standard terminology. Second, for the centralised database, most algorithms are developed. However, data are commonly stored in several sites in today’s global digital world. Third, many algorithms are focused on safeguarding the privacy of personal information, but do not focus on data mining results. There is no single approach to obtain data and to hide limitations. Fourth, each algorithm is specialised on data mining tasks primarily. No single strategy can work for any type of data clustering algorithm. Part of data privacy also is a crucial role in transforming data after data storage. These can be used as a guide to future PPDM research. Present research explores the privacy of data mining with various ways to approach the scope of new development approach.