Multi-user and multi-language collaborative speech translation system and method in the era of big data

Design of Translation Error Correction System Based on Improved Seq2Seq

The government in the era of big data requires safer infrastructure, information storage and data application. As a result, security threats will be the bottleneck for e-government development. Based on the e-government hierarchy model, this thesis focuses on such information security threats as human effects, network technology defects and management deficiency facing the e-government system in the era of big data. On this basis, three solutions are put forward to improve e-government information security system. Firstly, enhance information security awareness and improve network technology of information management departments in the government; secondly, conduct proper information encryption by ensuring information confidentiality and identity authentication; thirdly, implement strict information management through isolation between intranet and extranet and united planning of e-government information management.

With the improvement of the material standard of living, the demand of the people for spiritual culture continues to increase. In terms of tourism, people have gradually shifted from simple tourism needs to integrated tourism needs. Tourism has become an effective way for people to expand their horizons and enrich their spiritual world. Tourism is one of the first industries to apply network technology. After long‐term exploration and innovation, tourism e‐commerce has developed rapidly. Coupled with the advent of the era of big data (EBD), the concept of customized tourism has gradually entered people’s lives. This paper mainly introduces the research on the impact of e‐commerce tourism on custom tourism in the EBD and intends to provide some ideas and directions for the good development of custom tourism. This paper proposes a research strategy on the impact of tourism e‐commerce on customized tourism in the EBD, including related theoretical research methods, random forest algorithms, support vector machine classification algorithms, and Bayesian estimation algorithms, which are used to customize tourism e‐commerce in the EBD, and research experiment on the impact of tourism. The experimental results show that 79.84% of customers are willing to purchase related products again after experiencing travel customization services using big data technology. By using the various characteristics of tourism big data for data mining and classification, it provides users with personalized travel search services. At the same time, big data technology can provide basic technical support for customized tourism development, which shows that it can also provide customized services for users.

In multi-user collaborative interaction systems, the interface serves not only as a medium for human-computer interaction but also as a crucial channel for communication between users. Consequently, the quality of collaborative interface design directly impacts the overall effectiveness of the system. In collaborative systems, different users typically assume distinct roles, and task flows are typically more complex. Compared to single-user interfaces, multi-user collaborative interfaces must account for a broader range of collaboration requirements and characteristics. Although a substantial body of theoretical and practical research on user interface design exists, design methods specifically for multi-user collaborative interaction interfaces are still lacking. Therefore, this study builds on the existing theories and case studies of collaborative systems, extending user-centered design methods. The study emphasizes the analysis of task flows and role relationships in multi-user collaboration and integrates collaboration needs and characteristics throughout every stage of the interface design process. Ultimately, we propose a methodological framework for interface design tailored to multi-user collaborative interaction systems, aiming to provide theoretical support for the development of more advanced and comprehensive collaborative systems.

In the era of big data, the basic role of data is becoming more and more significant: digital technology, goods and services are not only accelerating the penetration of multi-directional, multi-level and multi chain into traditional industries, that is, industrial digitization; It also promotes the continuous development and growth of digital industrial chains and industrial clusters such as Internet data center construction and services, that is, digital industrialization. It can be said that big data related technologies and applications have spawned a blue ocean for the development of digital economy, including emerging technologies such as big data, cloud computing, Internet of things, block-chain, artificial intelligence and 5g communication; At the application level, typical representatives are ‘new retail’, ‘new manufacturing’, etc. (Abstract)

Since the 20th century, information and network technology, mainly represented by microelectronics, computer communication and network technology, has progressed and developed rapidly, and its dissemination and application have had a significant impact on economic development and social transformation. Among them, modern computer communication is an emerging discipline that effectively combines modern computer technology and communication technology. It has developed rapidly after experiencing the information revolution, which has greatly changed the daily work and lifestyle of people in modern society, and the changes in communication have also changed. After entering the new era, the conversion stage from analog signal to digitization to communication signal has basically ended. However, in the field of modern digital communication, there is still a lot of room for the advancement and development of electronic computer technology. Many new technologies and new technologies are emerging in an endless stream, which also provides ample room for future development of the industry. This article analyzes the transformation of computer network communication technology based on multi-platform data. First, use literature research methods to analyze the advantages of computer network communication technology in the era of big data. And summarize the problems of computer network general technology, and then use questionnaires based on big data platform to investigate the impact of computer network communication technology changes on people and the failure of computer network communication in life. The result shows that the video upload fails. The occurrence is very high. About 60% of people have experienced video upload failures. There are relatively few failures in hacking and image upload failures. This shows that the solution to the information network security problem in China is still possible, but the video high incidence of upload failures indicates that the speed of computer network communication needs to be strengthened. The direct impact of the reform of computer network technology is that people have more frequent contacts. About 50% of people have more frequent contacts with family and friends after the reform of computer network technology.

As speech translation (ST) systems become increasingly prevalent, understanding their vulnerabilities is crucial for ensuring robust and reliable communication. However, limited work has explored this issue in depth. This paper explores methods of compromising these systems through imperceptible audio manipulations. Specifically, we present two approaches: (1) adapting perturbation-based techniques used for automatic speech recognition (ASR) attacks to the ST context, making our work the first to apply this approach to ST, and (2) proposing a novel music generation-based method to guide targeted translation, while also conducting more practical over-the-air attacks in the physical world. Our experiments reveal that carefully crafted audio perturbations can mislead translation models to produce targeted, harmful outputs, while adversarial music achieve this goal more covertly, exploiting the natural imperceptibility of music. These attacks have proven effective across multiple languages and translation models, highlighting a systemic vulnerability in current ST architectures. Beyond immediate security concerns, our findings highlight broader challenges in the robustness and interpretability of neural speech systems.

Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain. In the era of big data and large complex information delivery system, it is impossible to get the right information in the online environment. In this research work, we offered a novel movie-based collaborative recommender system which utilizes the bio-inspired gray wolf optimizer algorithm and fuzzy c-mean (FCM) clustering technique and predicts rating of a movie for a particular user based on his historical data and similarity of users. Gray wolf optimizer algorithm was applied on the Movielens dataset to obtain the initial clusters, and also the initial positions of clusters are obtained. FCM is used to classify the users in the dataset by similarity of user ratings. Our proposed collaborative recommender system performed extremely well with respect to accuracy and precision. We analyzed our proposed recommender system over Movielens dataset which is available publically. Various evaluation metrics were utilized such as mean absolute error, standard deviation, precision and recall. We also compared the performance of projected system with already established systems. The experiment results delivered by proposed recommender system demonstrated that efficiency and performance are enhanced and also offered better recommendations when compared with our previous work [1].

In recent years, due to the development of science and technology, the existence of objective factors such as people’s abuse of pesticides and unreasonable development, the soil has been seriously polluted, and its quality has also declined. People’s living environment and the agricultural products they need are also affected by it. Frequent problems have seriously affected the sustainable development of the ecological environment. This article takes the central and western parts of a province as the survey object, uses the methods of geological accumulation index and comprehensive pollution index to analyze the soil pollution status of the survey area, and uses the index soil fertility index to evaluate the soil fertility in the fresh food production area. The AERMOD model was used to calculate the dry sedimentation, wet sedimentation, and total sedimentation of organic waste gas. In addition, in the era of big data, with the emergence and development of artificial intelligence technology, translation technology has developed rapidly and has become the new favorite of the translation industry, which has had an important impact on the translation industry. It also puts forward new requirements for the construction of the corpus. Constructing the ecology of soil environment prediction from the perspective of ecological translation is helpful to construct the connotation of interconnection, mutual restriction and interaction among system components. This thesis is guided by the theoretical thoughts of ecological translation, and tries to explore the advancement of soil environment prediction by constructing an ecological view of translation. Through the application of project management concepts in daily teaching, it can help a student to better master the process and methods of translation and improve their translation ability.

The internet financial model is booming in the era of big data, and gradually penetrates into all aspects of people's lives. With the continuous development of network technology, under the background of big data, people's living standards are constantly improving, and higher requirements are put forward for the quality of life. In order to promote the sustainable development of the internet finance industry, seize the opportunity and strengthen itself in the era of big data, the internet finance industry needs to uphold the development concept of pluralism and openness. Internet finance has played a positive role in improving the efficiency of financial services and formed a new payment method. With the help of big data, internet finance is constantly innovating Internet financial products, while some risks are amplified under the background of financial big data. On the basis of expounding the concepts of big data and Internet financial risks, this paper provides a method for the construction of Internet financial risk control model based on data envelopment analysis and the main forms of Internet financial risks.

Today, we have entered an era of big data. Under the background of global economization, communication in all walks of life is becoming more and more frequent and cross-language communication is inevitable. Cross-language communication is difficult for many people. The online translation system can greatly reduce the communication barriers between people of different languages. As an efficient tool, the translation system can realize the translation of different languages under the conditions of retaining the original semantics equivalent conversion. The article adopts the Internet of Things technology and big data model to build an English intelligent translation system, which can realize intelligent translation between multiple languages and English. The research results of the article show the following: (1) For samples with high semantic feature values, the correlation coefficient and similarity coefficient will be higher. Therefore, it can be concluded that for different semantics, the similarity is generally positively correlated with its feature value and correlation coefficient. The translation speed of the system proposed in the article is the fastest among the three translation systems. When the number of sentences is 10,000, the translation speed of the translation system proposed in the article is 5.89 seconds, the translation speed of the network multilingual translation system is 6.74 seconds, and the translation speed of the traditional translation system is 10.53 seconds. (2) The translation accuracy of the big data intelligent translation model proposed in the article is the highest among the three models. The translation accuracy of simple sentences can reach 99%. The translation accuracy of general sentences is 98%, and the translation accuracy of complex sentences is 95%. The BLEU value of the method in this paper is basically the same as that of the RNN cyclic neural network translation model. When translating general sentences, the BLEU value of the method in this paper is slightly higher than that of the RNN cyclic neural network translation model; especially when translating complex sentences by machine, the BLEU value of the method in this paper is far higher than that of the RNN translation model. (3) The average response time will increase with the increase in the number of tests, and the success rate generally remains above 98%, close to 100%, indicating that the response time of the system operation is normal. The number of designed test cases for the data processing module is 90, the number of executed test cases is 90, and the execution rate can reach 100%. Normal operation means that in the process of operation, no fault occurs. In the system load test, the load of serial number 1 is normal, the average delay is 38 seconds, the average delay of serial number 2 is 48 seconds, the average delay of serial number 3 is 59 seconds, the average delay of serial number 4 is 62 seconds, and the average delay of serial number 5 is 47 seconds. The delay of data packets under all kinds of loads can meet the standard requirements.

Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks.

The continued rapid growth of data, along with advances in Artificial Intelligence (AI) to extract knowledge from such data, is reshaping the computing ecosystem landscape. With AI becoming an essential part of almost every end-user application, our current computing platforms are facing several challenges. The data-intensive nature of current AI models requires minimizing data movement. Furthermore, interactive intelligent datacenter-scale services require scalable and real-time solutions to provide a compelling user experience. Finally, algorithmic innovations in AI demand a flexible and programmable computing platform that can keep up with this rapidly changing field. We believe that these trends and their accompanying challenges present tremendous opportunities for FPGAs. FPGAs are a natural substrate to provide a programmable, near-data, real-time, and scalable platform for AI analytics. FPGAs are already embedded in several places where data flows throughout the computing ecosystem (e.g., smart network/storage, near image/audio sensors). Intel FPGAs are System-in-Package (SiP), scalable with 2.5D chiplets. They are also scalable at datacenter-scale as reconfigurable cloud, enabling real-time AI services. Using overlays, FPGAs can be programmed through software without needing long-running RTL synthesis. With further innovations, and leveraging their existing strengths, FPGAs can leap forward to realize their true potentials in AI analytics. In this talk, we first discuss the current trends in AI and big data. We then present trends in FPGA and opportunities for FPGAs in the era of AI and big data. Finally, we highlight selected research efforts to seize some of these opportunities: (1) 2.5D SiP integration of FPGA and AI chiplets to improve the performance and efficiency of AI workloads, and (2) AI overlay for FPGA to facilitate software-level programmability and compilation-speed.

Bioinformatics is an intercross field of biology and computer science. With the develop-ment of computer network information technology andprecision medical care, biomedical field ushered in the era of big data. The demand for bioinformatics knowledge to medical students has shifted from single gene level to the analysis on massive medical data. This paper analyzed the characteristics of the big data era, as well as the problems of the teaching idea, curriculum coverage and teaching platform could not meet the requirements of the new era in teaching of bioinformatics course for medical students. Finally, we pro-posed several measures to strengthen the reform of bioinformatics teaching to meet the challenge of the big data era, including optimizing courses, changing educational philosophy, introducing new teaching methods, and setting up teaching platform with times. Key words: Bioinformatics; Big data era; Teaching methods

In the popularization of the theory of computerization technology in China, the internal audit of the enterprise has become more and more convenient. Especially in the era of big data, China's computer technology theory research results are more and more, enterprise internal control and financial audit application of accounting computerized management mode is more and more mature, can not only ensure the integrity and authenticity of information data, but also on a certain basis to standardize enterprise computerized management standards. Therefore, after understanding the relationship between enterprise internal control financial audit and accounting computerization, based on the existing problems of enterprise internal control financial audit under the background of accounting computerization, I mainly explored the current accounting computerization system architecture applied in enterprise construction, and proposed the optimization strategy of enterprise internal control financial audit from the perspective of long-term development. In order to change the current situation of enterprise internal control financial audit.