Reviewerly/exHarmony huggingface dataset:
Design of distance teaching system of College Chinese course based on Data Mining

The data throughput of conventional distance teaching system is low, and the length of course search path is long. Therefore, this paper proposes the design of distance teaching system of College Chinese course based on data mining, and optimizes the system software on the basis of conventional system hardware. Select the appropriate data mining tools, filter the course resources in the web page information, and then integrate the pre-processed course resources and set up a system database. Through the neural network, the course resources with high degree of association are clustered, and service nodes and leaf nodes are selected to transfer resource data remotely. Experimental results show that the design system increases the data throughput, reduces the length of the search path, and improves the transmission performance of curriculum resources.
Automating Rapid Network Anomaly Detection With In-Band Network Telemetry

Network anomaly detection plays a significant role in Operation Administration and Maintenance (OAM). In this letter, we propose INT-detector, an automated and rapid network anomaly detection system, by combining In-band Network Telemetry (INT) and Deep Learning (DL). First, we build an INT-based telemetry prototype, enabling fine-grained monitoring by acquiring hop-by-hop device states. Then, we leverage Generative Adversarial Active Learning (GAAL) to detect anomalies without overreliance on the human intervention. Besides, we perform data preprocessing with low-pass filtering to eliminate transient traffic jitters for detecting more persistent anomalies. INT-detector is accurate and achieves 0.979 AUC on the collected INT dataset.
Design and Implementation of the Monitoring System for NoSQL-based Cloud Platform Resources

China Meteorological Administration has established a meteorological cloud platform based on virtualization, distributed technologies, etc., and has been opened for internal use, but lacks an effective resource usage monitoring system, which is helpful for fine management of resource. Based on the development and application of non-relational database in the industry, this paper analyzes the functions and display requirements of the real-time monitoring system of cloud platform resources, it also expounds the system design and implementation, and discusses the reasons for the selection of non-relational databases of this kind of system. This system can effectively solve the difficult problem of resource usage monitoring of heterogeneous cloud platform, improve the refined management capability, and has high practical application value. It has good reference significance for the design and construction of monitoring systems of similar platforms in other industries. At the end of the paper, the application of emerging technologies in the meteorological industry is prospected.
Integrating deep learning for 360-degree video applying to space user flow and behavior patterns

Modern city means the densely populated and developed industrial and commercial areas. Based on the influence of different types of urban space, time, and industry categories, pedestrians will create different space use patterns. It has always been an important topic for the development of smart cities in the future to understand the user's behavior, movement line, direction, observation of different aggregation modes, and data collection in urban space. This study proposes to use 360° camera to capture videos of human space interaction in different spaces, and integrate data pre-processing, image optimization processing, and image recognition deep learning steps, trying to analyze human position in space, basic data reading, walking path and directional interpretation, and aggregation mode. It is hoped that through the collection and analysis of a large amount of data from 360° panoramic videos, we can develop an application system for real time and semi-automatic recognition of user characteristics and behavior patterns in cognitive space in the future.
Classification of Indonesian News using LSTM-RNN Method

News categorization has the aim of categorizing news into certain categories. In this paper, we build a machine learning model to categorize Indonesian news. One of the best methods for predicting large text sets is the Recurrent Neural Network (RNN) algorithm with Long-Short Term Memory (LSTM) architecture. In previous studies, the use of the LSTM-RNN method has a high level of accuracy for classifying news in English. For further exploration, in this study, a dataset to train and test the Indonesian news application model from the Jakartaresearch and web scraping from Kompas.com is used. Based on the experiment for the LSTM-RNN model, the final score of accuracy was 93%, the recall score was 91.8%, the precision score was 92.4%, and the Fl-Score score was 91.8%s. 17 news predictions from Detik.com have 100% accurate results predicting the correct category.
Robust Deep Stochastic Configuration Network Modeling Method Based on Kernel Density Estimation

In order to alleviate the negative impact of noise on the accuracy of deep stochastic configuration network modeling, a robust deep stochastic configuration network modeling method based on kernel density estimation is proposed. The output weights of each hidden layer of deep stochastic configuration networks are obtained by solving the weighted least square problem, where the kernel density estimation method is employed to set the penalty weights of training samples. In addition, the alternating optimization technique is applied to update the penalty weights and hidden layer output weights. The effectiveness of the proposed method is tested and evaluated by using function approximation and the benchmark dataset. The results show that the proposed method can effectively alleviate the impact of noise on modeling accuracy, which is valuable for applications in the field of robust modeling.
Ontology-based Process Reengineering To Support Digitalization Of MRO Operations: Application To An Aviation Industry Case

After sales services are characterized by interlinked service provider and customer operations. Furthermore, proper management of data related to the physical product and its lifecycle is essential for proposition of value-adding services. In this article, we present a novel ontology-based approach for improvement of maintenance, repair and overhaul (MRO) processes, where the ontology captures information of industry standards and product-related data. By clearly defining relations, the ontology supports the digitalization of operations and utilization of data in operational processes of different stakeholders. The approach was developed and demonstrated by investigating data of a case from aviation MRO industry.
Cyber Security Risk Management for Ports - A Systematic Literature Review

Understanding the importance of the maritime sector to the global economy and the peculiarities of Ports, due to their critical role in an extremely dynamic environment, this paper looks to perform a systematic literature review, aiming to define the state of the art and to discover the processes and methodologies that may assist port facilities in conforming to IMO’s cyber security related resolutions. Meanwhile, seeking to further advances in research for this sector, this review also looks to acknowledge the current gaps in the field.
Modified score function for monotone likelihood in the semiparametric mixture cure model

The cure fraction models are intended to analyze lifetime data from populations where some individuals are immune to the event under study, and allow a joint estimation of the distribution related to the cured and susceptible subjects, as opposed to the usual approach ignoring the cure rate. In situations involving small sample sizes with many censored times, the detection of nonfinite coefficients may arise via maximum likelihood. This phenomenon is commonly known as monotone likelihood (ML), occurring in the Cox and logistic regression models when many categorical and unbalanced covariates are present. An existing solution to prevent the issue is based on the Firth correction, originally developed to reduce the estimation bias. The method ensures finite estimates by penalizing the likelihood function. In the context of mixture cure models, the ML issue is rarely discussed in the literature; therefore, this topic can be seen as the first contribution of our paper. The second major contribution, not well addressed elsewhere, is the study of the ML issue in cure mixture modeling under the flexibility of a semiparametric framework to handle the baseline hazard. We derive the modified score function based on the Firth approach and explore finite sample size properties of the estimators via a Monte Carlo scheme. The simulation results indicate that the performance of coefficients related to the binary covariates are strongly affected to the imbalance degree. A real illustration, in the melanoma dataset, is discussed using a relatively novel data set collected in a Brazilian university hospital.
Limits of “the Self”, boundaries of Personal space and IT

The problems of personal autonomy and privacy that have arisen with the rapid development of information technology are explored. The concept of personal space is introduced using a systemic approach and the foundations for a theory of personal space are developed. The conclusion is made about the pulsating nature of the boundaries of personal space. We consider the influence of information technology on the dynamics of personal space boundaries. The proposed approach is extended to neuroinformational technologies. The phenomenon of personal space narrowing inside the mental sphere of the person and the reaction of the individual in the form of blocking of external influences are discussed. It is emphasised that the current legislation does not regulate the relations arising in the process of neuroinformational interaction. Since the rate of IT progress is incompatible with the traditionally conservative nature of the legislation, it is suggested that the solution to this contradiction should be sought in the area of experimental jurisprudence.
Label-Noise Robust Person Re-Identification via Symmetric Learning

Person re-identification (Re-ID) methods based on deep learning has obtained impressive progress in recent years. And the training of the model usually relies on a large amount of accurate pedestrian identity annotations. However, label noise is unavoidable and has a negative impact on the model training process. But existing research pays little attention to Re-ID with noisy label. In this paper, we propose a label noise robust model for person Re-ID by symmetric learning, without additional clean labels or predefined label distributions. We symmetrically enhance cross-entropy loss through noise robust reverse cross entropy. The experiments demonstrated its effectiveness against various ratios of label noise on the three different benchmark datasets.
Towards Best Practices in Spatial Audio Post Production: A Case Study of Brazilian Popular Music

Since the beginning of the XXI century, we have been witnessing a significant shift in the media landscape towards enhanced immersive audiovisual manifestations, from controlled research environments to gradual production market penetration. Virtual reality, augmented reality, mixed reality, extended reality, 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">circ</sup> video, and digital games are representative examples of these immersive technologies. Spatial audio design and production are instrumental to the immersive experience. As Ambisonics techniques do potentially mean more expense – in memory, processing power, and production budget –, limited exploration in the development of new composition and production methodologies across popular music production has been considered beyond the traditional stereophonic format. Our work details a postproduction case study using spatial audio, namely High Order Ambisonics. The case study is a Brazilian popular song, remixed using 3rd order Ambisonics from a multitrack recording session composed of monophonic and stereophonic audio tracks. The song encompasses a unique approach for audio spatialization guided by hierarchical audio content attributes across multiple structural time scales and musical contexts. The evaluation of our production process adopted iterative heuristic assessments comparing technical decisions and aesthetic intentions in fostering an augmented spatial audio song. A set of technical guidelines and good practices on how and why to positioning audio in space are abstracted from our case study evaluation, which critically advances the theory and practice of popular musical audio production in immersive technologies.
Time series modelling and forecasting for predicting Covid19 Case Load using LSTM

The epidemic of the Novel Coronavirus across the globe has influenced the globe overall and caused a large number of death results. This remains as an unfavorable admonition to general wellbeing and will be set apart as perhaps the inordinate pandemics in the history. Inorder to validate and analyse, the details was taken from COVID-19. The detail contains daily tallies of confirmed, relieved and demise cases. Likewise, it includes extra data with respect to patients testing present in various states and the outcomes isolated in confirmed and invalidated cases. With the data provided it allows infected person to get the proper treatment and timely quarantine. The proposed paper utilizes Long Short Term Memory (LSTM ) networks for sequential prediction of data. The networks are viable apparatuses in short-term time series gauge the COVID-19 confirmed cases. It is a complex gated memory unit made to disappearing gradient issues restricting the effectiveness of a basic Recurrent Neural Network (RNN). Here Neural Network is used to solve the complex operation on the dataset. The result demonstrate that the LSTM Network is executed with various activation functions by utilizing a exponential linear unit brought about better execution for determining the complete number of COVID-19 cases. With the timely observations the corona virus state can be effectively monitored and the proper treatment can be assigned for the infected ones.
Optimized SS-based Audio Watermarking System Using Partial Embedding Method

In recent years, digital audio watermarking has shown increasing academic and commercial value. The embedding method in frequency domain with spread spectrum technology performed well in infomation-hiding communication. But when applied in copyright protection, it doesn't have a good performance. we propose an optimized embedding method, in which watermark information are embedded only on the imaginary part of FFT points (frequency lines) instead of on both imaginary and real part. The optimization makes the watermark hardly perceived by people, meanwhile, it gives the watermark great robustness against attacks especially from perceptual audio compression. The synchronization codes are also proposed in this paper as an approach to adapt to desynchronization attacks like possible audio cropping.
A generic and decentralized approach to XAI for autonomic systems: application to the smart home

How can a smart home system generate explanations to its user on unusual or unwanted situations? Despite the rise of Explainable AI in recent years, there is still no satisfying solution to this problem. Most of the challenge lies in the fact that explanations are most needed when facing unusual or strange situations, which is where standard statistical methods are less effective. When faced with similar problems, humans rely on sequential reasoning, examining causally related conflicts and solving them one after the other. The approach explored by this PhD thesis is to implement this kind of reasoning into a Cyber-Physical System such as a smart home. To do so, a generic and modular architecture is designed to account for the specificity of smart home systems (runtime adaptation, variety of components, importance and uniqueness of the context). The aim of the thesis is to build the base framework of an Explanatory Engine and to provide a proof-of-concept demonstrator.
Online Calligraphy Teaching Interest Cultivation Scheme Based On Information Technology in the Internet Era

The continuous development of science and technology has led to new changes in the medium of teaching calligraphy, with the emergence of various online platforms for teaching communication, and the constant innovation of educational philosophy has led to corresponding adjustments in the original teaching mode of calligraphy, which has changed from the traditional offline teaching and private teaching to online teaching and online interaction. This paper discusses the current state of calligraphy teaching on the Internet, focusing on how online teaching can mobilise students' interest in learning through the OBE concept and how the algorithmic assessment system can be scientifically evaluated, with a view to providing solutions to the problems that exist in online calligraphy teaching in the Internet era.
Towards Development of Knowledge Graph for Narrative Information in Medicine

The purpose of this work is to provide a framework for patient-doctor narration. The framework was designed as a step-by-step approach in two phases. The initial phase was to construct a base model for the narration supported by the narrative models and theory. To represent the composite model with actual information, ontology development methodology was followed. This was phase two. State of the art study revealed that there are ontology-based models for narration in various domains. But none exists in the medical domain. This is overcome by providing a framework to capture the narration in medical field. The study resulted in the initial development of a composite model for patient-doctor narration. Hence this study contributes in capturing of patient-doctor narration. The framework reuses the existing vocabularies, schemas etc. to build the model.
KG2Code: Correct Code Examples Mining Service Based on Knowledge Graph for Fixing API Misuses

API misuse has become an important factor restricting the quality of software services. Existing API misuse detectors based on the API-constraint knowledge graph can not intuitively assist developers in fixing the API misuse. Correct code examples are more direct and straightforward for developers to modify and debug code. Therefore, we first enrich the API-constraint knowledge graph. Besides, we publish a service called KG2Code, which can map the API-constraint Knowledge Graph to the Correct Code examples. According to the different types of constraint relations in the API-constraint knowledge graph, we design a code snippet mining framework that extracts the corresponding correct API usage pattern from over 9528K Java repositories GitHub. KG2Code is implemented by the interactive visualization website. It helped users (1) learn how to use an unfamiliar API or fix an API misuse and (2) understand why API misuse occurs.
Automatic Text Summarization Using Transformers

Nowadays, we are facing to huge amount of data that makes the task of information analysis quite complex. In this context, automatic text summarization has gained a great deal of success where it is able to extract an efficient short version of documents covering the most important information. In this paper, we propose a new extractive approach for automatic text summarization based on deep learning techniques. This extractive approach can be easily applied on any document independently of its language. Furthermore, by selecting sentences from the document, we guarantee the grammatical and linguistic correctness of summaries. Some experimental results were conducted in order to improve the performance of the proposed approach.
U2-VC: one-shot voice conversion using two-level nested U-structure

Abstract Voice conversion is to transform a source speaker to the target one, while keeping the linguistic content unchanged. Recently, one-shot voice conversion gradually becomes a hot topic for its potentially wide range of applications, where it has the capability to convert the voice from any source speaker to any other target speaker even when both the source speaker and the target speaker are unseen during training. Although a great progress has been made in one-shot voice conversion, the naturalness of the converted speech remains a challenging problem. To further improve the naturalness of the converted speech, this paper proposes a two-level nested U-structure (U 2 -Net) voice conversion algorithm called U 2 -VC. The U 2 -Net can extract both local feature and multi-scale feature of log-mel spectrogram, which can help to learn the time-frequency structures of the source speech and the target speech. Moreover, we adopt sandwich adaptive instance normalization (SaAdaIN) in decoder for speaker identity transformation to retain more content information of the source speech while maintaining the speaker similarity between the converted speech and the target speech. Experiments on VCTK dataset show that U 2 -VC outperforms many SOTA approaches including AGAIN-VC and AdaIN-VC in terms of both objective and subjective measurements.
A New Framework Based on Spatio-Temporal Information for Enhancing Compressed Video

Artifacts are generated during video compression; many methods have been proposed to solve this problem. Most of the methods to get better temporal information, using optical flow to estimate temporal motion compensation. However, optical flow could be inaccurate because compressed video might be distorted by various compression artifacts. The optical flow also introduces additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with significant motion and heavy occlusion. To address these problems, we propose an effectively multi-frame with spatio-temporal information-guided quality enhancement network. Our algorithm employs spatio-temporal deformable convolution to aggregate temporal information. We define inputs as a target frame and its neighboring reference frames that can jointly predict offset fields to deform the spatio-temporal sampling positions. Specifically, we enhance the quality of reconstruction compressed video by devising an efficient deformable alignment module with a receptive field block to handle bad cases in Compression Artifact Removal.
Facial Age Estimation by Learning Label Distribution CNN

In this paper, we propose a new deep convolutional neural networks based architecture with learning label distributions for human facial age estimation. Our proposed method first takes the Xception model with the use of data augmentation, and then Label Distribution Learning (LDL) is adopted for age encoding, KLD is used in loss function, and dropout is considered at fully connected layers. Finally, it takes the expected value of the model output as the solution in estimating ages. The IMDB-WIKI dataset is used as our pretraining dataset and the training set of APPA-REAL dataset is used to fine-tune our proposed model. In the experiment, the state-of-the-art results of MAE 3.09, 2.78, and 3.628 years are attained on MORPH-II, FG-NET, and APPA-REAL datasets, respectively.