Reviewerly/exHarmony huggingface dataset:
An IBC Reference Block Enhancement Model Based on GAN for Screen Content Video Coding

As a special kind of video coding, screen content coding (SCC) has received widespread attention because of the popularity of online classes and conferences. However, few people use neural networks to improve the compression efficiency of SCC. Intra block copy (IBC) is one of the most important coding tools in SCC, which can save half of the bitrate. Due to the need to copy the content of the reference block, the performance of IBC mode largely depends on the quality of the reference block. In the standard encoding process of Versatile Video Coding (VVC), the IBC reference block is not filtered, and there are still serious compression artifacts. This will result in a decrease in IBC search accuracy and SCC compression efficiency. Inspired by in-loop filtering, we propose an IBC reference blocks enhancement network based on GAN (IREGAN) to filter the reference blocks before IBC estimation, which can improve the quality of IBC reference block and the accuracy of IBC matching. In addition to the generator used for image enhancement, our model also includes a variance-based classifier and a discriminator obtained from adversarial training. The classifier can effectively improve the efficiency of the model and the discriminator can improve the robustness of the entire system. Experimental results demonstrate the performance gains of IREGAN with VTM10.0, offering about 6.98% BDBR reduction, 0.71dB BDPSNR gains in average (luminance). SSIM increased by 0.0113 and the number of blocks using IBC mode is increased by 1.42%.
Research on Intelligent Identification Method of Power Grid Missing Data Based on Improved Generation Countermeasure Network with Multi-dimensional Feature Analysis

With the rapid development of data acquisition system in power grid, the data fusion of power grid has become more and more mature. Aiming at the problem of data missing in power grid, this paper proposes an intelligent identification method of power grid missing data based on generation countermeasure network with multi-dimensional feature fitting. Firstly, based on the fluctuation cross-correlation analysis (FCCA), the correlation between missing data and multidimensional features is analyzed, and the feature data set with strong correlation is selected; Secondly, the kernel principal component analysis (KPCA) algorithm is used to map the feature data set into a low-dimensional vector. Finally, the improved generation countermeasure network (WGAN) is used to reconstruct and distinguish the low-dimensional vector, fill in the missing data of power grid and enhance the identification ability of missing data. The simulation results show that the proposed method has higher data filling accuracy than the traditional data filling method.
A Visualized Analysis of Chinese Teaching Research in Mongolia Based on CNKI

The rapid development of information technology provides technical support for the visualization analysis of data. The definition of visual analysis method is to use software and scientometrics to conduct statistical analysis on a large number of information and make a complete analysis chart, so as to make the complex information intuitive and analyze the potential rules. In this paper, the research literature on Chinese teaching in Mongolia in the CNKI journal database from 2003 to 2020 is taken as the research object. Through the visualization analysis of CNKI, the influential institutions, research developments and hot spots in the field of Mongolian Chinese teaching and research are summarized. This will provide a new perspective for the future research for the relevant scholars in this field and promote the Chinese teaching in Mongolia.
Understanding Risk and Risk Perceptions of Cybercrime in Underground Forums

Understanding the reasons and the pathways of people becoming involved in cybercrime has been an important topic for research within different disciplines. Studies have explored the pathways of skilled hackers into deviant behaviour with a focus on online gaming, however little research has been conducted around understanding risk perception of cybercrime. This study investigates both surface and dark web forums, focusing on a variety of topics from hacking to gaming. The aim of this study is to a) investigate the ways cybercrime is perceived among different members of underground forums; b) identify whether there is an emotional construct of cybercrime; and c) identify the level of knowledge around behaviours which are considered as cybercrime and are criminalised. The novelty of this study lies in the methodological approach taken to conduct qualitative and quantitative research on large text datasets. Our findings identified hacking as one of the main deviant behaviours. Our analysis shows a general optimistic bias influencing the perception of risk associated with cybercrime as well as a number of risk avoidance strategies.
Focus-Based Text Summarisation with Hybrid Embeddings

Text summarisation has been recognised as an important Natural Language Processing task, attracting great attention from both researchers and practitioners. It has been widely adopted in various domains. For example, text summarisation of news, articles and book chapters can produce a short text, assisting the readers with grasping the main idea rapidly. In the medical domain, it is also applied to summarise the patients’ questions. However, it is very challenging to control the summariser output by producing domain-specific summaries since the focus of domain-specific information may be ignored. In this paper, we propose a novel summarisation model aiming at producing summaries by focusing on the domain-specific knowledge, where hybrid embeddings, i.e., focus, domain and context embeddings, are utilised. We conduct extensive experiments to evaluate our novel model by using the MeQSum dataset. The experimental results demonstrate that our model outperforms state-of-the-art algorithms.
Method for Detecting and Analyzing the Compromising Emanations of USB Storage Devices

Compromising emanations (CE) is produced in-evitably by the electronic information equipment while processing and transmitting information. Malicious software and side-channel attack can achieve information interception and cause information leakage by enhancing the compromising radiation or intentionally generating controlled electromagnetic emanations. To detect and analyze compromising emanations is of great significance to maintain information security. In this paper, we present a method to detect and analyze the compromising electromagnetic emanations of USB storage devices. The method is composed of three parts: signal preprocessing, electromagnetic emanations detection and electromagnetic emanations analysis. Firstly, the collected signal is denoised by the empirical mode decomposition (EMD) algorithm. Then singular value decomposition (SVD) is utilized to process the denoised signal. And microframe parameter of the denoised signal is analyzed to determine whether the compromising radiation is from USB. Finally, the radiation signal from the USB device is analyzed by the correlation function to assess whether there is a potential threat about this device. Experiments are carried out in normal scenarios and practical attack scenarios with “USBee”, which shows that the proposed method is effective and has practical application value.
Quantitative analysis of deep learning based multi-target tracking algorithms

Multi-object tracking is an active computer vision problem that has gained consistent interest due to its wide range of applications in many areas like surveillance, autonomous driving, entertainment, and, gaming to name a few.In the age of deep learning, many computer vision tasks have benefited from the convolutions neural network.They have been optimized with rapid development, whereas multi-target tracking remains challenging.A variety of models have benefited from the representational power of deep learning to tackle this issue.This paper inspects three CNN-based models that have achieved state-of-the-art performance in addressing this problem.All three models follow a different paradigm and provide a key inside of the development of the field.We examined the models and conducted experiments on the three models using the benchmark dataset.The quantitative results from the state-of-the-art models are listed in the standard metrics and provide the basis for future research in the field.
Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study

Deep learning is being increasingly used in mobile and edge autonomous systems. The prediction accuracy of deep neural networks (DNNs), however, can degrade after deployment due to encountering data samples whose distributions are differ-ent than the training samples. To continue to robustly predict, DNNs must be able to adapt themselves post-deployment. Such adaptation at the edge is challenging as new labeled data may not be available, and it has to be performed on a resource-constrained device. This paper performs a case study to evaluate the cost of test-time fully unsupervised adaptation strategies on a real-world edge platform: Nvidia Jetson Xavier NX. In particular, we adapt pretrained state-of-the-art robust DNNs (trained using data augmentation) to improve the accuracy on image classification data that contains various image corruptions. During this prediction-time on-device adaptation, the model parameters of a DNN are updated using a single backpropagation pass while optimizing entropy loss. The effects of following three simple model updates are compared in terms of accuracy, adaptation time and energy: updating only convolutional (Conv-Tune); only fully-connected (FC-Tune); and only batch-norm parameters (BN-Tune). Our study shows that BN-Tune and Conv-Tune are more effective than FC-Tune in terms of improving accuracy for corrupted images data (average of 6.6%, 4.97%, and 4.02%, respectively over no adaptation). However, FC-Tune leads to significantly faster and more energy efficient solution with a small loss in accuracy. Even when using FC-Tune, the extra overheads of on-device fine-tuning are significant to meet tight real-time deadlines (209ms). This study motivates the need for designing hardware-aware robust algorithms for efficient on-device adaptation at the autonomous edge.
Construction of Project-Driven Practical Teaching System for Educational Technology Major Based on SSH Framework

As a profession that integrates modern advanced educational concepts with modern information technology, educational technology provides strong support for the informatization of education in the new era. This paper builds a project-driven practical teaching system for education technology majors based on the SSH framework. The research includes four main aspects: first, the design of project-driven teaching activity process, including three stages: project design, project implementation and project evaluation; second, the design of SSH framework, representation layer, business layer, persistence layer and domain module layer; third, the design of the main functions of the system, including Practice management subsystem, Project management subsystem, User management subsystem, Teaching management subsystem; fourth, the data persistence design, the use of Hibernate will be the database access and operation, to achieve the mapping of entity objects to relational databases.
Integration Aspects of AI, IoT, Big Data, and Blockchain in Healthcare Industry

The specially designed IoT for Healthcare is known as IoHT (Internet of Healthcare Things). The healthcare industry consists of patients, doctors, hospitals, and ambulances. Hence, the applied technologies must be able to connect the dots among all these actors. Since the requirements of each actor are different in many aspects, the integration becomesnecessary. For example, a patient’s body sends critical information by using BANET, while the ambulance attendants work upon VANET. Also, the functioning of the same application may vary in different situations. For example, a pandemic is totally different from normal days. Keeping all these factors in mind, the authors think of integration among the different driving technologies of automation, such as AI, IoT, Big Data, and Blockchain. AI provides different working methodologies for smart devices under diverse conditions, the IoT provides an environment for all the actors to work in, big data is a solution forthe storage of huge amounts of generated data, and the blockchain keeps track of the deliveries of medicines and services of ambulances. In this chapter, the authors will elaborate on all the above mentioned technologies, the integration approaches of different technologies, and possible countermeasures for the threats in the process of integration.
Technologies and Applications for Big Data Value

Abstract The continuous and significant growth of data, together with improved access to data and the availability of powerful computing infrastructure, has led to intensified activities around Big Data Value (BDV) and data-driven Artificial Intelligence (AI). Powerful data techniques and tools allow collecting, storing, analysing, processing and visualising vast amounts of data, enabling data-driven disruptive innovation within our work, business, life, industry and society. The adoption of big data technology within industrial sectors facilitates organisations to gain a competitive advantage. Driving adoption is a two-sided coin. On one side, organisations need to master the technology necessary to extract value from big data. On the other side, they need to use the insights extracted to drive their digital transformation with new applications and processes that deliver real value. This book has been structured to help you understand both sides of this coin and bring together technologies and applications for Big Data Value. This chapter defines the notion of big data value, introduces the Big Data Value Public-Private Partnership (PPP) and gives some background on the Big Data Value Association (BDVA)—the private side of the PPP. It then moves on to structure the contributions of the book in terms of three key lenses: the BDV Reference Model, the Big Data and AI Pipeline, and the AI, Data and Robotics Framework.
PENINGKATAN PERANCANGAN SISTEM INFORMASI MERCHANDISE INSIDE LOMBOK BERBASIS WEBSITE MENGGUNAKAN API WHATSAPP

Aims to be able to help PT. Lombok Media Utama (Inside Lombok) in managing all its merchandise catalogs on the website and making it easier for customers who want to buy merchandise to input the purchase format using the WhatsApp API. This activity is in the form of making a website using the prototyping method and training carried out by the team by providing training to the Inside Lombok admin staff in the form of directions on the flow of using this website application, the participants involved are the inside Lombok admin staff. From the results obtained after testing using BlackBox Testing and evaluation of satisfaction, quite satisfactory results were obtained where the admin managed to manage merchandise products easily, and customers could also use the features on the website smoothly to fill in the format with the Whatsapp API feature to be more efficient
Cognitive Model for Probability Density Distribution Uncertainty Visualization

People often obtain information and make judgments with the help of systems and machines, but due to the uncertainty in the instrument, environment, and data processing, the judgments made by the system are not always correct. Blindly believing the results of the system's decision-making is very dangerous, so effective information exchange between the system and users is the prerequisite to ensure correct decision-making, and it is extremely important to convey the uncertain information of the system to users. Visualization is an important way to inform users of uncertain information. At present, many scholars have conducted research on how to express the uncertainty contained in data. Then whether users can effectively recognize and understand these visual graphics becomes an issues to be explored.What this article wants to explore is how people recognize and understand visualization for uncertainty. Only by understanding the way people interpret visualization can the visualization be optimized and the information exchange between the system and users can be better realized.In this paper, the user’s perception of probability under the triangular distribution is studied. A symmetric distribution model and two asymmetric models are used. In each distribution model, 13 test points are selected according to the true value of the probability density. The experiment is realized by Unity3D. In each trial, the positions of the distribution model and the test points appear randomly. The experiment describes the task of using remote sensing technology to recognize camouflage targets. The participants need to answer the probability that the target to be detected represented by the test point is the enemy's camouflage target.This paper verifies the effectiveness of using the brightness gradient to visualize the uncertainty, and this conclusion is consistent with the results of previous studies. At the same time, this paper proposes a fitting model of the user's internal probabilistic cognition and the visual expression of probability density. We found that when the participants used the triangular probability distribution information expressed by the brightness gradient to infer the probability that the test point is a camouflage target, this perceived probability has a strong linear correlation with the true value of the probability density corresponding to the point. Participants will give a probability prediction close to 1 at the test point with the lowest brightness, that is, the highest probability density, and a probability prediction close to 0 at the highest brightness point, that is, the lowest probability density point. Fitting based on the linear model can obtain the probabilistic cognitive model of the subjects for the visualized graph.This article provides a way to realize the communication of uncertainty information between the system and the user, and may contribute some help to the effectiveness and reliability of artificial intelligence.
Understanding Distributed Semantic Analysis with Spark Data Frames

Big data analytics is the most current research trends till date. Big data faces many challenges in terms of processing and storing of data for optimization and implementation of machine learning (ML) algorithms as ML needs too much computation power to process data for any kind of output in terms of analytics. To reduce the optimization problem, we are using the spark for its data frames. Also, in creating the architecture of any big data problem, spark engine provides very good support in that too as its engine support any archistecture with Hadoop, map-reduce. Analytics in big data need real-time processing for any behaviors that we needed to predict under any type of text analytics. While working on semantic analysis, we have faced many problems as there are many different spelling mistakes in documents from online sources. To handle this problem, we are creating different distributed schema under different layers of distributed data frames of spark engine with support of YARN, as this Yarn supports us for any kind of map-reduce process we need to follow to solve sematic behaviors of any mistakes that happened inside sources. But, the first step that we followed is to create ontology from the above semantic characteristics. As the base architecture of the ontology helps us to understand how data behave under different processes of semantic field under any language used in many datasets while working with Natural Language Processing (NLP), we need to take care of the sentiment of the document to parse the information under positive, negative, or neutral in term of sentiment analysis. In this research, we mainly focus on ontology architecture to process different semantic information to increase the sentiment accuracy by implementing different advance ML.
A Secure Multi-party Data Federation System

PDF HTML XML Export Cite reminder A Secure Multi-party Data Federation System DOI: 10.21655/ijsi.1673-7288.00273 Author: Affiliation: Clc Number: Fund Project: National Key Research and Development Program of China (2018AAA0101100); National Natural Science Foundation of China (61822201, U1811463, 62076017); the CCF-Huawei Database System Innovation Research Plan (CCF-HuaweiDBIR2020008B); State Key Laboratory of Software Development Environment (Beihang University) Open Program (SKLSDE-2020ZX-07) Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In the era of big data, data is of great value as an essential factor in production. It is of great significance to implement its analysis, mining, and utilization of large-scale data via data sharing. However, due to the heterogeneous dispersion of data and increasingly rigorous privacy protection regulations, data owners cannot arbitrarily share data, and thus data owners are turned into data silos. Since data federation can achieve collaborative queries while preserving the privacy of data silos, we present in this paper a secure multi-party relational data federation system based on the idea of federated computation that ``data stays, computation moves.'' The system is compatible with a variety of relational databases and can shield users from the heterogeneity of the underlying data from multiple data owners. On the basis of secret sharing, the system implements the secure multi-party operator library supporting the secure multi-party basic operations, and the resulting reconstruction process of operators is optimized with higher execution efficiency. On this basis, the system supports query operations such as Summation (SUM), Averaging (AVG), Minimization/Maximization (MIN/MAX), equi-join, and $\theta $-join and makes full use of multi-party features to reduce data interactions among data owners and security overhead, thus effectively supporting efficient data sharing. Finally, experiments are conducted on the benchmark dataset TPC-H. The experimental results show that the system can support more data owners than the current data federation systems SMCQL and Conclave and has higher execution efficiency in a variety of query operations, exceeding the existing systems by as much as 3.75 times. Reference Related Cited by