Reviewerly/exHarmony huggingface dataset:
A Comparative Study on End-to-End Speech Synthetic Units for Amdo-Tibetan Dialect

This paper focuses on the speech synthesis modeling for Amdo-Tibetan dialect. We analyze the linguistic and phonetic characteristics of Amdo-Tibetan and present to use Latin letters as the synthetic unit for building the end-to-end speech synthesis model. The comparative experiments are designed and carried out to evaluate three type of synthetic unit – Latin letters, Tibetan initials and finals, and Tibetan syllables. The experimental results show that the model using the Latin letters transcribed from Tibetan characters by Wiley transliteration has better performance than the ones using the other two synthetic units. Also, this paper compares the different acoustic features and different vocoders for Amdo-Tibetan speech synthesis. The experimental results show that the speech synthesized using mel-spectrogram as acoustic feature and WaveNet as vocoder has the better clarity and naturalness.
Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language

Sinhala is a native language spoken by the Sinhalese people, the largest ethnic group in Sri Lanka. It is a morphologically rich language, which is a derivation of Pali and Sanskrit. The Sinhala language creates a diglossia situation, as the language’s written form differs from its spoken form. With this difference, the written form requires more complex rules to be followed when in use. Manually proofreading the content of Sinhala material takes up much time and labor, and it can be a tedious task. Hence, a system is necessary which can be used by different industries such as journalism and even students. At present, there are a handful of systems and research that have automated Sinhala spelling analysis and grammar analysis. In addition, the existing systems are mainly focused on either spelling analysis or grammar analysis. However, the proposed system will cover both aspects and improve upon existing work by either optimizing or re-building the process to provide accurate outputs. The proposed system consists of a suffix list built for verbs and subjects, which helps the system stand out from the current proposed solutions. This research intends to implement a service for spell checking and grammar correctness of formal context in Sinhala. The research follows a rule-based approach with some components adopting a hybrid approach. As per the literature survey, many papers were analyzed, related to different aspects of the proposed system and complete systems. The proposed system would be able to overcome most barriers faced by previous papers whilst it takes a fresh take on providing a solution.
Lightweight CNN-Based Deep Neural Networks Application in Safety Measurement

Inspired by the face covering period in the past two years, COVID-19 pandemic has resulted in the mandate of public safety measures such as face mask-wearing in many countries. This paper provides a preliminary feasibility planning on how Artificial Intelligence (AI), Computer Vision (CV) and the Internet of Things (IoT) can work together to implement a face-mask detection system as a public health safety solution. This paper reviews how edge computing can overcome traditional cloud computing issues. This work also examines the current state of computer vision, convolutional neural networks and their potential application in the health and safety domain. This writing serves as an interim report on how the lightweight CNNs and single-shot detectors such as YOLOv5 variants with SSD to train and deploy an object detection system.
An Anomaly Detection Algorithm for Multi-dimensional Segmentation Plane Isolation Forest

Traditional anomaly detection is mostly based on single-dimensional data for identification and analysis. The isolated forest algorithm is not a description of normal samples, but a field is divided by isolated anomalies. This paper introduces a multi-dimensional segmentation plane anomaly detection algorithm for isolated forest. By using multi-dimensional plane segmentation in the process of constructing hyperplane segmentation, and controlling the generation process of the isolated tree, the generation process of the isolated forest is optimized. By adding some common datasets for comparative experiments, good results have been achieved
Research on a User Context Model Based on Data Mining

With the popularity of the Internet and under the rising number of online shoppers, e-commerce platforms are facing increasing challenges in personalizing their descriptions and information services to users. Therefore, in this paper, the context factors that have an impact on users' decisions are counted through questionnaires and derived by way of statistical analysis. Based on the coarse-grained context conclusions drawn from the statistical analysis through SPSS, a web crawler was used to crawl the Taobao users' context and behavioral dataset as the experimental dataset. The crawled data was then analyzed by K-Means. Finally, the K-Means clustering model analysis is used to propose the construction of a user context model, which is used to promote the correct understanding of personalized information needs on the platform, thereby increasing user loyalty.
Performance Comparison of Neural Network Models on Edge Devices Using Face Mask Detection System

COVID-19 pandemic has affected the human lifestyle in an unprecedented way. Apart from vaccination, one of the best precautionary measures is wearing masks in public. Face Mask detection models that are deployable on single board computers (SBC) enable data security, low latency, and low cost in the real-world deployment of face mask detection systems. Offline deployment is possible on SBC as data is referenced on the device compared to a server implementation while securing monitored individuals’ privacy. Thus, this paper aims to implement an autonomous vision system that is deployed on Raspberry Pi devices to detect face masks in real-time. Performances of MobileNet, MobileNetV2, and EfficientNet Convolutional Neural Network (CNN) architectures were compared in both standard hardware and edge devices. We used the TensorflowLite format to compress the model for deployment. Accuracy, precision, and recall were used as metrics to compare the model performance. MobileNet achieved the overall best test accuracy of 97.93% while MobilNetV2 attained 96.12% ranking second. Each model’s average inference times for standard hardware and a raspberry pi 4 device were measured by connecting to a camera feed. MobileNetV2 outperformed the other two models in inference time on the Raspberry pi device.
Application of Contrast Embedding with Feedback Regulation in GZSL

Generalized Zero-Shot Learning is a world-class challenge. in real life, many scenes have been used and have broad prospects for development. GZSL's purpose is to identify new classes by transferring semantic knowledge from seen classes to unseen classes. Although the generation model learned by the feature generation method can synthesize the missing visual features in unseen classes to reduce the data imbalance in GZSL. It is disadvantageous to the classification of GZSL due to the absence of the original image feature information area. In order to solve this problem, a method of integrating the generated model with the embedded model has been proposed recently to produce a hybrid GZS L framework. This hybrid framework maps real samples and synthetic samples into an embedded space for classification. On this basis, we propose to add a feedback regulation mechanism to feed back the hidden features before contrast embedding classification into the synthetic network of unseen classes. We named it CEFWGAN. We performed validation evaluations on three official base data sets (FLO CUB SUN) in which some of the evaluation criteria were superior to the current approach.
Performance Optimization of Microservice Applications under Resource Constrained Environments

Performance of microservice applications deployed on cloud platforms have a non-linear relationship with resources allocated to each service of the application. Applications can be limited by fixed resource budgets or operation costs. This study presents an automated framework utilizing equality constrained Bayesian Optimization (BO) on CPU, and Memory limits of individual services in a benchmark single node microservice application with the objective of minimizing latency and maximizing throughput. The model found configurations that achieve over 3 times improvement on latency and over 2 times improvement on the throughput of the default configuration. Particle Swarm Optimization (PSO) achieved similar improvement in performance with a higher number of iterations compared to BO.
Computer-Vision Based Attention Monitoring for Online Meetings

Due to the appearance of COVID-19, virtual video conferencing platforms like Zoom and Google Meet have become one of the main alternative ways to conduct virtual meetings and presentations. While the virtual platforms are cheaper and more flexible, presenters and meeting hosts are likely less efficient at assessing audience attention and engagement due to the lack of body language. In this paper, we propose a system for estimating and monitoring participant attention in virtual meetings by using computer vision. Our approach mainly focuses on changes in a person’s presence, gaze direction, and head orientation as a computer camera has a limited field of view. We first created a module to detect and extract participant video cells to isolate users and process their attention individually. Using those videos, we then monitored the user’s presence, using YOLOv3 and DeepSORT, and their gaze direction and head orientation, using PTGaze. Through this monitoring, the system is able to record and graph a user’s attention over the total amount of frames and return a collective attention level graph for the entire meeting. We believe that our system has potential usage in settings where attention is critical, such as academic lectures or collaborative business meetings.
GMBLOF: A Machine Learning Algorithm of Novelty Detection Based on Local Outlier Factor

Local Outlier Factor (LOF) algorithm is a typical machine learning algorithm and has good accuracy in novelty detection for detecting global and local outlier. However, the LOF algorithm requires to traverse the entire dataset when calculating the local outlier factor of new data object. Aiming at the problem of extra time overhead in traversing entire dataset, we studied the combination of LOF and Growing Neural Gas (GNG) network and proposed the Growing-Model-Based Local Outlier Factor (GMBLOF) algorithm. Firstly, the topology of normal dataset generated by GNG was regarded as distribution description of the entire normal dataset and realized transformation from large to small order of magnitude. Secondly, the correlation of each node extracted from topology formed growing model. Finally, GMBLOF jointly growing model was used to calculate local outlier factor of new data point. The result of comparison experiments between GMBLOF and state-of-the-art novelty detection algorithms shows that the proposed algorithm outperforms in both accuracy and execution time, and especially the accuracy on WBC dataset improves by 8.69 % to 93.42%, compared to the other algorithms' average accuracy of 84.73%.
Zernike Sub-pixel Edge Detection Method Based on Ramer-Turkey-Fitzgibbon

Based on Ramer-Turkey-Fitzgibbon of zernike sub-pixel edge detection is a more accurate method than the traditional edge detection method. In the paper, in order to automatically segment and synthesize the edge contour curve data into straight line segment, arc segment or elliptical arc segment. Firstly, the discrete coordinate points are simulated into contour curve by nonlinear least square method, and then an edge detection method based on Zernike moment sub-pixel is established for edge contour segmentation, and thirdly when detecting straight line, circle, and ellipse, the Ramer Douglas pucker algorithm is established, and the fitting curve of edge contour is obtained. Finally, the error analysis is carried out to verify the effectiveness of the algorithm.
Video Anomaly Detection via Successive Image Frame Prediction Leveraging Optical Flows

Video anomaly detection is challenging due to the diversity of abnormal events, which turns unsupervised learning the promising solution in recent endeavors. In a framework as such, the existing works hold with such an assumption that anomalies cannot be reconstructed or predicated from historical data as accurately as normal ones, so the reconstruction or prediction error can act as an indicator of anomalies. In this study, we propose to discriminate anomalies from normal ones by fusing both appearance and motion in a frame prediction framework, where the uniqueness lies in that we embed optical flows into the frame prediction framework as the clue to direct the transformation from the input to the predicted frame, making appearance-motion fusion quite natural without any extra effort to align them. Then, the error of predicting the next frame based on the concatenation of the appearance and the associated motion of the present frame can indicate the anomaly score. Notably, we employ one frame-based optical flow computation instead of the traditional ones over two continuous frames. The goal is to make the optical flows subject to the whole training data such that anomalies deviating remarkably from the training data will result in highly distorted optical flows and relatively high prediction error correspondingly, which is not promised by the traditional optical flows rooting in the differential of two continuous frames. In summary, we extend the appearance-motion correspondence learning to motion-guided prediction tying the appearances of two consecutive frames. We also introduce a margin loss to enhance the learning of frame prediction. Experiments on widely accepted benchmarks demonstrate the state-of-the-art performance of our approach.
Feature Distribution Distillation-Based Few Shot Class Incremental Learning

This paper focuses on the challenging but practical problem: convolutional neural networks (CNN) based few-shot class-incremental learning (FSCIL), which requires learning new knowledge incrementally from a few examples without forgetting the previously learned knowledge. To balance the memory of the old knowledge and the adaptation to the new knowledge, we propose a feature distribution distillation-based incremental learning framework. First, we freeze the shallow parameters of the network to alleviate the massive forgetting of the learned knowledge. Then, we propose a feature distribution-based knowledge distillation loss to transfer the previously knowledge. Finally, we also generate the classification weights initialization for each new class to improve the adaptation of the network. Extensive experiments show that our proposed method can effectively alleviate the forgetting of the previously knowledge and improve the adaptability to the new knowledge. Our method outperforms other state-of-the-art few-shot class incremental learning methods on MiniImageNet dataset and gets a competitive result on CIFAR-100 dataset.
Tibetan Syllable Prediction with Pre-trained Cross-lingual Language Model

In recent years, with the development of Tibetan language information technologies, the Internet Tibetan data is increasing year by year. Due to the need for the Tibetan input method and Tibetan error correction, Tibetan language prediction has become an urgent problem to be solved. At present, the challenges of Tibetan prediction are that the Tibetan syllable composition is complex, the vocabulary of Tibetan words which is composed of syllables is extremely large, and the Tibetan word separation technology is not mature. To solve the above problems, this paper proposes a Tibetan syllable prediction method based on a pre-trained cross-lingual language model using Tibetan syllables instead of Tibetan words as the token for prediction. The method uses the cross-lingual language model XLM-R and fine-tunes it using Tibetan news texts to make it more suitable for predicting Tibetan in the news domain. We conduct experiments on Tibetan syllable prediction for texts crawled on the Tibetan news website. The experiments show that the precision of our model for Tibetan text prediction is higher than that of the current n-gram methods.
Improving Aerospace Big Data Infrastructure and Applications with Distributed File System and Massive Parallel Calculation

As the aerospace business growing rapidly, data flow and volume has exploded in recent years, bringing chances and challenges to big data infrastructures and applications in this field. In traditional aerospace data and application centers, data is stored in network attached storages(NAS) and processed by sequential or low level parallel programs, which can hardly meet the demand of performance, availability and scalability. In this paper, we provided a big data infrastructure based on HDFS for big data centers, which can improve the availability and scalability remarkably. Besides, we gather a series of typical big data applications in aerospace filed as benchmarks, analyzes their characteristics and accelerates them in MapReduce framework. The experiment result shows that among all the benchmarks, the speedup is 4.98 to the peak and 3.87 on the average.
Two-Space Spatial-Temporal Regularized Correlation Filters for UAV-Based Aerial Tracking

Discriminative Correlation Filters (DCF) are proved to be effective and efficient tracking methods for unmanned aerial vehicle (UAV) because of their high speed and accuracy. However, DCF suffers from unwanted boundary effects. Spatially regularized DCF (SRDCF) tackles this problem by adding spatial penalty on DCF coefficients. Afterwards, spatial-temporal regularized DCF (STRCF) introduces temporal regularization in the DCF coefficient space to SRDCF for more robust appearance learning and faster computation. In this work, we present two-space spatial-temporal regularized DCF (TSSTCF) by introducing spatial-temporal regularization in the regression space to STRCF. The spatial-temporal regularization in the regression space is complement with it in the DCF coefficient space, leading to more robust target appearance learning. More importantly, in the regression space, we can easily control the learning of every single circular shifted sample. This property helps the model suppress the responses of hard negative samples while still keep temporal regularization of other samples. Furthermore, an adaptive factor is introduced to dynamically control the penalty quantity in the regression space. The TSSTCF model can be efficiently solved via the Alternating Direction Method of Multipliers (ADMM). Extensive experiments have been done to prove the superiority of our method.
Arbitrary-Shaped Text Detection with Gaussian Probability Distance Distribution

With the development of semantic segmentation, segmentation-based methods have yielded great success in detecting arbitrary-shaped texts. However, many existing text detection methods use binary discrete distributions to predict shrunk text instances, which cannot generate complete and accurate text bounding boxes. In this paper, we propose an arbitrary-shaped scene text detection method based on predicting Gaussian probability distance map of the complete text region, and this map can retain more text boundary information. Then, the boundary pixels are clustered into high-confidence text centers by a learnable post-processing and false positives are filtered out by pixel-level score maps. We also propose an adaptive channel enhancement module to improve the pixel-level segmentation accuracy. Experiments on three standard datasets, including CTW1500, Total-Text, and MSRA-TD500, demonstrate that the proposed method achieves great robustness and performance. The method obtains an F-measure of S2.S% on CTW1500 and S3.0% on MSRA-TD500.
TransID: Image Dehazing Based on TransUNet

Because UNet works well in learning damaged picture restoration from large-scale data, these models are commonly utilized in image dehazing applications. Transformer, another type of network design, has recently proven to be effective in natural language and complex visual tasks. Transformer models improve convolutional neural network limitations, but they fall short of UNet in terms of texture and detail repair. In this paper, we present an effective and efficient hierarchical encoder-decoder network architecture based on TransUNet. Our network has two core designs to make it suitable for this task. The first essential component is a TransUNet-based multi-scale dense feature fusion network, where we merge global self-attention from Transformer and multi-scale context data from UNet. The second essential component is our investigation of the Context Information Compensation Module to add-on data from the encoder to the decoder. With the use of these two designs, our strategy keeps the benefits of the conventional UNet network while enhancing the network with the new transformer’s superior performance in terms of global self-attention. The results of our experiments show that both the quantitative and qualitative evaluations are better than the current methods.
Trusted Management Infrastructure with Blockchain for Edge Device in Smart City

As smart cities continue to be developed, network traffic surges. Although edge computing improves the quality of service by shifting computing tasks from cloud computing centers to locations closer to the edge through a multi-layer distributed computing model, there are security risks in the face of central node failures and internal malicious attacks, which make it difficult to provide stable and reliable services. In this paper, we design a trusted management model of edge device in smart city with blockchain, introducing blockchain technology into the research of smart city construction. It uses the blockchain distributed architecture and decentralization idea to realize the trusted collection and storage of sensory data. And based on this architecture, a new reputation-based PoW consensus algorithm is proposed to provide a trust mechanism for IoT devices. The algorithm greatly increases the cost of malicious attacks launched by the IoT device, effectively prevents malicious attacks on devices, and achieves trusted management of edge device behavior. The smart city application based on the proposed method verifies its feasibility, effectively prevents malicious attacks from nodes, and enhances the information security of the system.
Knowledge Graph Completion Based on Graph Attention Networks and Text Information

In knowledge graphs (KGs), there exist some unsolved problems such as incomplete data, hidden information with incomplete mining and so on. In the most completion models, the information of the triples in the KG is generally utilized, but the neighborhood information and rich entity description information are not included in the triples. In this paper, the knowledge graph completion (KGC) method is improved based on graph attention networks (GATs) with text information by using the neighborhood information of aggregated triples and entity description information. And the embedding capability of semantic information is enhanced in KGs. First, the feature vector of entity description information is extracted by the Bi-LSTM model and concatenated with the entity embedding in the triples. Then the joint vectors are trained by GATs to aggregate the neighborhood information. Next, the KGC task is realized by a decoder. Finally, the effectiveness of the proposed method is verified by the link prediction experiments in the public datasets FB15K-237 and WNISRR and comparison is investigated with several other existing methods. The test results show that most of the indicators in the two datasets are improved. Furthermore, it is proved that the model combined with multi-source information has better representation ability for entities, which can further improve the accuracy and comprehensive performance of KGC tasks.