Notes

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With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user's quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately -2.85%, -8.89%, and -10.05% BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.In the human resource system of modern enterprises, human-post matching big data occupies an important irreplaceable position. With the deepening of the reform of state-owned enterprises, some shortcomings of human-post matching big data have become prominent. The purpose of this article is to solve the current state-owned enterprises. There are a variety of problems with big data in the enterprise, and an effective method is found that can accurately evaluate the degree of human-job matching in state-owned enterprises and provide a scientific basis for the manager of talent and resource allocation to make more rational decisions. Through the radial basis function (RBF) neural network-based big data model of human-post matching evaluation of state-owned enterprises, we scientifically and effectively evaluate the matching degree of the quality and ability of the personnel with the relevant requirements of the position and then help the company to adjust the personnel at any time changes in positions to maximize the efficiency of human resources. In this paper, considering the actual situation of the enterprise, the RBF neural network and the analytic hierarchy process (AHP) method are used comprehensively. Firstly, the AHP is used to obtain the weight of each evaluation index in the human-post matching index system. At the same time, the artificial neural network theory is self-adapting. Learning is helpful to solve the problem that the AHP method is too subjective. The two learn from each other's strong points and combine their weaknesses organically to increase the convenience and effectiveness of evaluation.With the daily increase of data production and collection, Hadoop is a platform for processing big data on a distributed system. A master node globally manages running jobs, whereas worker nodes process partitions of the data locally. Hadoop uses MapReduce as an effective computing model. However, Hadoop experiences a high level of security vulnerability over hybrid and public clouds. Specially, several workers can fake results without actually processing their portions of the data. Several redundancy-based approaches have been proposed to counteract this risk. A replication mechanism is used to duplicate all or some of the tasks over multiple workers (nodes). A drawback of such approaches is that they generate a high overhead over the cluster. Additionally, malicious workers can behave well for a long period of time and attack later. This paper presents a novel model to enhance the security of the cloud environment against untrusted workers. A new component called malicious workers' trap (MWT) is developed to run on the master node to detect malicious (noncollusive and collusive) workers as they convert and attack the system. An implementation to test the proposed model and to analyze the performance of the system shows that the proposed model can accurately detect malicious workers with minor processing overhead compared to vanilla MapReduce and Verifiable MapReduce (V-MR) model [1]. In addition, MWT maintains a balance between the security and usability of the Hadoop cluster.Episodic memory allows a person to recall and mentally reexperience specific episodes from one's personal past. Studies of episodic memory are of great significance for the diagnosis and the exploration of the mechanism of memory generation. Most of the current studies focus on certain brain regions and pay less attention to the interrelationship between multiple brain regions. To explore the interrelationship in the brain network, we use an open fMRI dataset to construct the brain functional connectivity and effective connectivity network. We establish a binary directed network of the memory when it is reactivated. P505-15 mouse The binary directed network shows that the occipital lobe and parietal lobe have the most causal connections. The number of edges starting from the superior parietal lobule is the highest, with 49 edges, and 31 of which are connected to the occipital cortex. This means that the interaction between the superior parietal lobule and the occipital lobe plays the most important role in episodic memory, and the superior parietal lobule plays a more causal role in causality. In addition, memory regions such as the precuneus and fusiform also have some edges. The results show that the posterior parietal cortex plays an important role of hub node in the episodic memory network. From the brain network model, more information can be obtained, which is conducive to exploring the brain's changing pattern in the whole memory process.The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained.
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