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The emerging, overclocking signal-based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross-device authentication and media tracking. However, micro...
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... The perpetual advancement and investigation in communication networking have facilitated the interconnectedness of smart devices, leading to data transmission across applications and devices (F Şahin & Söylemez, 2022;Yang, Fu, Liu, & Walid, 2022). Nevertheless, the heightened interconnectivity also introduces concerns regarding security and privacy since consumers may lack awareness regarding the vulnerabilities and risks associated with sophisticated technologies (Duan, Zhang, Cheng, & Ren, 2021). The balance between privacy and accuracy is an essential factor to consider. ...
The ethical implications of neuroscience and the collection of data from the human body are complex and controversial. While the data collected may seem harmless, it raises questions about its potential misuse and potential misuse to manipulate human behaviors. The main issue lies in the purpose driving the use of neuroscience data, whether it genuinely aims to understand and advance humanity or violate personal freedom and influence behavior. The role of neuroscience in the well-being of humanity is a subject of ongoing debate, with a comprehensive approach necessary to consider ethical limitations, preserving individual autonomy, and applying scientific progress to benefit humanity. Personal responsibility, a concept in human societies, depends on social interactions and rules of engagement. Smart devices can collect neuroscientific data and acquire private information about consumers, raising concerns about security and privacy. The balance between privacy and accuracy is essential, as collecting and utilizing private user data can improve personalized help but also puts confidential information at risk. The goal is to either emancipate individuals or limit control to hyper-intelligent machines.
... In modern wireless local area networks [10], the development of dense wireless access points makes adjacent cofrequency networks more and more common [11,12]. The use of the same channel in adjacent networks makes the channel competition of each node in the network fierce with serious interference [13,14]. ...
In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.
Because of the widespread use of smartphones in everyday life and advances in data analysis technology, brands are increasingly focusing on advertising via smartphone while collecting customer data. Advertisements are made to reach customers through two types of data collection: the first is using customer online behaviour to show personalised ads, which is known as online behavioural advertising (OBA), and the second is hearing out customer media usage and providing personalised ads, which is known as synced advertising (SA). The question here is whether personalised ads are useful and project a positive image to customers, which are reliable. The purpose of this paper is to provide a knowledge of how OBA and SA work and why it is vital to address personalised adverts in smartphones that are powered by AI algorithms due to privacy concerns. The purpose of this empirical investigation is to determine is purchase intention is accelerated based on the advertising value of personalised adverts, their literacy, and privacy concerns and to test the effect of personalised advertisement value on purchase intention.
