Development Of Mobile Internet Technology Research Articles

Social Exclusion and Problematic Mobile Phone Use Among Chinese College Students: Mediating Role of Basic Psychological Needs Satisfaction

Background: The development of mobile Internet technology has brought great changes and convenience in our daily life. However, it has also arisen a series of mental health problems, especially problematic mobile phone use (PMPU). Objective: This study aimed to explore the relationship between social exclusion and PMPU among Chinese college students, as well as the mediating role of basic psychological needs satisfaction (BPNS). Methods: We recruited a total of 340 participants (mean age 19.71, 44.7% males) from a university in China by convenience sampling. All of them completed the self-report tests of the Social Exclusion Scale, BPNS Scale and Mobile Phone Addiction Index Scale. Data analysis was performed in SPSS 23.0, using the PROCESS macro to examine the mediating effect. Results: Social exclusion was significantly positively correlated with PMPU (r = 0.22, p < 0.001), and significantly negatively correlated with BPNS (r = -0.66, p < 0.001), and BPNS was significantly negatively correlated with PMPU (r = -0.24, p < 0.001). BPNS played a mediating role in the relationship between social exclusion and PMPU. In addition, autonomy needs and competence needs mediated the link of such a relationship, whereas the mediating role of relatedness needs was not significant. Conclusions: Results showed that social exclusion was closely related to PMPU, and social exclusion affected PMPU through BPNS. These findings underline the important role of social relationship and BPNS on adolescents’ PMPU.

Modeling User Participation in Facebook Live by Applying the Mediating Role of Social Presence

The rapid development of mobile Internet technology has brought about the flourishing growth of social media live streaming. This study employs social presence theory as the primary framework to investigate the impact of Facebook Live’s features of sociality, immediacy, and entertainment on users’ sense of presence. These features were then considered within the dimensions of awareness, emotion, and cognition. The influence of social presence on user engagement behaviors was divided into browsing behavior, interactive behavior, and creative behavior. Using snowball sampling, an online survey was administered to Facebook Live users, and 416 valid responses were collected. The research team used software to analyze the data, primarily encompassing descriptive statistics, reliability and validity analyses, structural equation modeling, and mediation effects testing. The research findings are as follows. First, the media characteristics of Facebook Live significantly influence the sense of presence. Specifically, sociality, immediacy, and entertainment on Facebook Live have a notable impact on users’ awareness, emotion, and cognition. Second, different dimensions of social presence have distinct effects on various user engagement behaviors. Notably, the dimensions of awareness, emotion, and cognition of social presence positively affect users’ browsing and interactive behaviors, while emotion influences users’ creative behavior. The third finding was that awareness, emotion, and cognition act as intermediates between Facebook Live’s media characteristics and user engagement behaviors. Implications are discussed.

A Routing Algorithm Based on Software-Defined Computing First Network

With the continuous development of mobile Internet technology, new services and applications appear, such as AR/VR, Internet of Vehicles, and Ultra HD Video. They not only require higher network bandwidth resources but also need more computing resources to ensure normal operation and user experience. As a new network technology, computing first network promotes the deep integration of distributed computing resources and networks to make efficient use of these resources. This paper proposes a computing power routing algorithm based on software-defined computing first network (SD-CFN) architecture. It comprehensively considers computing resources and available bandwidth resources of network links. Experimental results show that compared with the nearby scheduling strategy, the proposed computing power routing algorithm can better allocate computing resources to services. Moreover, it can improve the utilization rate of computing resources and provide optimal bandwidth resources in the process of service transmission.

Research on the Influence of Douyin's Short Video on Love on Contemporary Chinese Female College Students' Concept of Marriage and Love

With the development of mobile Internet technology, Douyin short video platform has risen rapidly and become the mainstream social media for Chinese college students. In recent years, the short videos about love on Douyin have become more and more popular among female college students, and their contents also show diversified characteristics.With the theme of "Research on the Influence of Douyin's Short Video on Love on Contemporary Female College Students' Concept of Marriage and Love", this paper explores the problems existing in the transmission of love short videos among contemporary female college students and how to guide them to correctly recognize and treat marriage and love problems through questionnaires.The research found that Douyin's short videos about love not only affected female college students' attitudes towards marriage, but also affected their cognition of partner requirements and money substance. Therefore, contemporary female college students should not only enjoy the happiness brought by Douyin short videos, but also maintain a rational thinking on the concept of marriage and love. The platform should improve the value of Douyin content, the government should supervise public opinion, and individuals should improve their media literacy. Taking various measures to guide contemporary female college students to establish a correct concept of marriage and love.

Analysis of Mobile Game Marketing Strategy - Taking Honor of Kings as an Example

With the development of mobile Internet technology and the upgrading of smart phones, the mobile game market has ushered in a huge space for development. If a mobile game wants to occupy a certain position in the highly competitive market and can develop for a long time, it is necessary to formulate an appropriate marketing Strategy. This paper takes "Honor of Kings" as the main research object, and combines the marketing theory to research and analyze the current marketing strategies of "Honor of Kings", in order to provide reference plans and improvement directions for the marketing strategies of mobile games. In general, strategy of Honor of Kings is based on interactive marketing, supplemented by others such as dissemination of live-broadcast platforms.

SWOT Analysis of Agricultural Products E-commerce Live Delivery PlatformTake Oriental Selection as an Example

With the development of mobile Internet technology and the advent of the epidemic era, the new sales model of on-site delivery with goods is booming and accepted by more and more consumers. This paper takes "Oriental Selection", a live-streaming platform of agricultural products owned by Beijing New Oriental Education and Technology Co., Ltd. as an example, and uses SWOT analysis to study the advantages and disadvantages, opportunities and threats of Oriental Selection at present. In addition, our research puts forward a series of suggestions for the future development of Oriental Selection, such as strengthening personnel and cargo management, strengthening cooperation with local governments, establishing their own supply chains, and controlling the quality of agricultural products. Finally, we describe the limitations of the current research from the perspective of research methods and propose specific suggestions for further research in the future.

Android Malware Detection Based on Program Genes

The security issues with mobile devices have received more attention as a result of the development of mobile Internet technology and the adoption of mobile intelligent terminal devices. It is becoming more crucial to quickly and effectively identify and remove harmful applications from systems in order to protect user data and personal devices. The Dalvik bytecode, permission applications, and system calls of Android apps are the main targets of the current Android malware analysis approaches. However, in recent years, an increasing amount of Android malware conceals harmful code in native code. The method for using program gene technology to identify malware on the Android platform is presented in this research. This method extracts executable library files from the Native layer binary executable files of Android programs and disassembles the library files to obtain program genes. Then, the programs’ genes perform feature screening by the information gain method and next use Word2Vec to express the semantic abstraction of the screened features. Finally, the screened features were used in deep neural network models for training and detection. The experimental results demonstrate that compared with KNN, SVM, and other machine learning algorithms, the deep neural network model is more effective and the detection accuracy reaches up to 97.51%. Thus, it confirmed the feasibility of the Android malicious program detection method based on program genes in this paper.

Research and Implementation of Automatic Indexing Method of PDF for Digital Publishing

With the rapid development of mobile Internet technology and artificial intelligence technology, the digital publishing industry is in urgent need of using intelligent technology to change the current way of content production and service. Most of the e-book resources owned by publishing enterprises are in PDF format, which is not suitable for reading on mobile devices, and it is not convenient to directly extract key information and construct knowledge graph. With this in mind, this article designs a PDF automatic indexing scheme that can identify all the element information in PDF and output structured data automatically and then extract all the key information in it to generate a keyword library with tag weights. The scheme mainly involves two key technical points: parsing PDF based on text features and grammar rules and extracting keywords based on tag weights. The former visualizes the text block in PDF into a rectangular area, divides the elements by clustering algorithm, and, finally, outputs structured data containing all the information. The latter combines the tags and their weights in the structured data and extracts the keywords in it by the inter-word relation algorithm. The structured data and keywords database produced by this scheme can be used to produce intelligent e-book and build knowledge graph, thus helping publishing enterprises to transform from a content service provider to an intelligent knowledge service provider. This transformation can deeply excavate the core value of the content held by the publishing industry and promote the digitization and intelligentization process of the whole industry.

Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user's photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).

LG-Tree: An Efficient Labeled Index for Shortest Distance Search on Massive Road Networks

With the development of mobile Internet technology, the road network in the real world is becoming larger and more complex, and the real-time response to the shortest distance query has become a high requirement for many industrial applications. Many existing approaches, such as G-tree or G*-tree, have been proposed to answer such search problems, however, these approaches are not efficient enough in dealing with very large graphs. To this end, we propose a novel index called LG-tree, which partitions the large graph into sub-graphs, and then indexes these subgraphs using a balanced tree. For each leaf node of LG-tree, a Distance Inverted File (DIF) is constructed, and these DIFs preserve all the connectivity information between the border vertices on the original graph. Inspired by the state-of-the-art label methods, we propose a novel hierarchy computing approach for each border vertex of LG-tree. Based on DIF and the hierarchy of border vertices, the border vertex list for each border is established, which is convenient for us to calculate the shortest distance and path between a query vertex <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_{q}$ </tex-math></inline-formula> and a target vertex <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> . Specifically, the number of levels of a vertex hierarchy increases dramatically as the graph size increases, and calculating vertex labels on a very large graph is an NP-hard problem. In order to further improve the calculation efficiency of the shortest distance between vertices, a heuristic method is proposed to limit the level of vertex hierarchy. In addition, a stage-based dynamic programming search method is proposed to divide all search situations between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v_{q}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> into three cases to guarantee the efficiency of the shortest distance search. Extensive experiments are conducted to show that LG-tree and the stage-based method have better performance than the state-of-the-art approaches.

Is something out of reach more attractive? The effectiveness of visual distance in computational advertising.

With the development of mobile Internet technology, firms need to complete the entire process of consumer targeting, ad content generation, and ad display in a very short time window. Therefore, computational advertising, such as native ads on social media platforms, has become the mainstream of online advertising with its automation and personalization features. However, computational advertising faces some problems when using artificial intelligence technology to generate content. First, the images should have a significant enough impact on consumers and be easy to adjust to save computational power at the same time; second, the iteration of the computational advertising system relies on consumer behaviors or advertising effectiveness, and firms need to learn the relationship between ad design and consumer behaviors. Under the above two problems, this paper selects visual distance as the main variable, and images can be adjusted by cropping to save computational power. This paper incorporates image design and ad effectiveness metrics into the construal level theory framework, under which the effectiveness metrics can be quickly determined. Following previous studies, we use click-through rate (CTR) to represent the early stage of the sales funnel and a higher construal level and CVR (conversion rate) to represent the later stage of the sales funnel and a lower construal level. Therefore, visually distant images bring distant psychological distance or higher construal level, which can get higher CTR; visually proximate images bring near psychological distance or lower construal level, which can bring higher CVR. These findings suggest that firms can improve the efficiency of their advertising systems and gain more revenue by understanding consumer psychological states.

Mobile Edge Computing to Assist the Online Ideological and Political Education

With the rapid development of mobile Internet technology, mobile network data traffic presents an explosive growth trend. Especially, the proportion of mobile video business has become a large proportion in mobile Internet business. Mobile video business is considered as a typical business in the 5G network, such as in online education. The growth of video traffic poses a great challenge to mobile network. In order to provide users with better quality of experience (QoE), it requires mobile network to provide higher data transmission rate and lower network delay. This paper adopts a combined optimization to minimize total cost and maximize QoE simultaneously. The optimization problem is solved by ant colony algorithm. The effectiveness is verified on experiment.

Qos-aware mobile service optimization in multi-access mobile edge computing environments

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users’ demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience.

Research and Design of Docker Technology Based Authority Management System.

With the development of mobile Internet technology and the continuous popularization of the network, various kinds of network software come out constantly and people are becoming more and more dependent on them, while role authority management is of great importance for the security of software, the control of management process, and the usability of users. In terms of system implementation, virtual machine technology is often faced with problems such as high virtualization overhead, poor scalability, and long deployment time in spite of its good isolation effect. Container technology represented by Docker can well solve these problems and make it possible to quickly build, deploy, operate, and maintain as well as expand services. Based on Docker technology, this research compares and chooses from various authority control models and finally decides to take the role authority management model as the infrastructure. It designs the role authority control model based on cloud computing and Docker technology in combination with the Task Controller Function, the Project Controller Function, and the User Controller Function and realizes this model by adopting the MongoDB database combined with HTML/CSS/Javascript syntax and the Boor Strap framework. After the test, it is found that the Docker technology based role authority management system has satisfactory test performance consistent with expected outputs as well as strong robustness, which can meet the requirements of different objects and subjects.

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

With the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.

Research on the Impact of Smart Phone Use on Children's Socialization

With the development of mobile Internet technology, smart phones play an increasingly important role in people's social life, and also gradually become an important companion in children's life. But at present, the content of children's access to mobile phones is mainly adult information with adults as the target group. Children's access to smart phones means that they are exposed to the adult information world without protection, which has an important impact on their socialization. This paper investigates children's smart phone contact behavior in the form of questionnaire, analyzes the impact of smart phone contact on children's socialization, and thinks that smart phone can help children understand society more comprehensively, cultivate calm personality, improve safety awareness, form correct communication concept, and clarify career ideal, At the same time, there are also some negative effects such as excessive use of mobile phones leading to children's personality loneliness, moral decline, social value distortion, Internet addiction and so on. Finally, from the family, school, society, government, media five aspects put forward corresponding strategies to reduce the negative impact of smart phones on children's socialization, so as to promote the development of children's socialization.

Text Complexity Classification Data Mining Model Based on Dynamic Quantitative Relationship between Modality and English Context

With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. This article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment. Secondly, based on the specific process of text mining, namely, data collection, data processing, and data visualization, it is proposed to subdivide the user demand analysis into three stages of text complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology. After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. Then, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology. Experimental results show that the collected quantitative relationship information is identified and expressed in order to realize the conversion of quantitative relationship information into product features. The market data and text data can be integrated to help improve the model performance and the use of text data can further improve predictions for accuracy.

Can Wavelet Transform Detect LDDoS Abnormal Traffic in Multipath TCP Transmission System?

With the rapid development of mobile Internet technology and multihost terminal devices, multipath transmission protocol has been widely concerned. Among them, multipath TCP (MPTCP) has become a hot research protocol in recent years because of its good transmission performance and Internet compatibility. Due to the increasing power of Low-Rate Distributed Denial of Service (LDDoS) attack, the network security situation is becoming increasingly serious. The robustness of MPTCP network has become an urgent performance index to improve. Therefore, it is very necessary to detect LDDoS abnormal traffic timely and effectively in the transmission system based on MPTCP. This paper tries to use wavelet transform technology to decompose and reconstruct network traffic and find a detection method of LDDoS abnormal traffic in the MPTCP transmission system. The experimental results show that in the MPTCP transmission system, the signal processing technology based on wavelet transform can realize the identification of LDDoS abnormal traffic. It indicates a direction worth further exploration for the detection and defense of the LDDoS attack.

Design and Implementation of an Intelligent Online English Learning System Based on Mobile Internet Technology

A predicament facing education is that the traditional teaching mode could no longer meet the requirements of modern society. The fast development of mobile Internet technology has brought online education platforms into existence, and these newborn things are very helpful to solve the mentioned contradiction. To deepen the research of the application of mobile Internet technology in modern education, this paper designed the functional modules of an intelligent online English teaching (IOET) system and implemented the proposed system. In our study, satisfying students’ learning requirements had been taken as the core principle when building the proposed system, and the database design had followed the principles of standardization, consistency, security, integrity, scalability, and ease of maintenance. The IOET system adopts a three-layer architecture, from top down, the system has a representation layer, a business logic layer, and a data service layer. The main management modules of the system include course notification module, visitor management module, and homework management module, etc. Through this paper, we hope to provide a piece of evidence for the basic research on the upgrade and improvement of intelligent online English teaching platforms.