The global rollout of 5G in 2021 marked a transformative leap in network capabilities, enabling ultra- low latency, massive machine-type communications, and enhanced mobile broadband. Yet, with this evolution came an expanded cyber attack surface, heightened dependency on software-defined networking (SDN) and network function virtualization (NFV), and amplified national security concerns. This paper examines critical threat vectors including signaling storms, network slicing attacks, and vulnerabilities in the service-based architecture of the 5G core. It also explores policy and regulatory challenges—particularly those (...) tied to vendor diversity, global supply chains, and trust in Chinese telecommunications infrastructure. As a position paper, it advocates for an integrated security framework built on Zero Trust principles, AI-driven anomaly detection, and end-to-end encryption tailored to 5G infrastructure. The paper argues for global harmonization of security policies and stronger public-private collaboration to secure this foundational technology. (shrink)

In recent years, with the continuous growth of social and economic level, people's living standard has also been constantly improved. Under this background, contemporary people highly respect a good living environment, and the importance of building interior decoration engineering has been fully reflected. As one of the important design contents of interior decoration, the use level and effect of color will directly affect the overall visual effect of interior decoration and the comfort of interior space. Through the rational collocation of (...) colors, the artistic characteristics of interior decoration can be displayed, which can also show the important role of color in interior decoration. In modern interior decoration, the use of colors is gradually enriched, and the use of multiple colors leads to the problem of actual color matching. Image segmentation, which is the premise of image recognition and tracking, divides an image into regions with different characteristics and extracts interesting objects. This technology has been widely used in military affairs, medicine, intelligent transportation, pedestrian detection, product inspection, sports, remote sensing, machine vision and other fields. Therefore, good color matching is very important in the actual interior decoration practice. (shrink)

Many individuals often struggle to identify the type of music they truly want to listen to based on their current mood. These challenges can lead to significant time wasted in searching for the "perfect" song that aligns with their emotional state. In this era of rapid technological advancements, particularly in the domain of Artificial Intelligence, leveraging AI in song recommendation systems can bridge this gap, creating a seamless fusion of technology and user needs. This paper aims to develop an emotion-based (...) song suggestion system that suggests songs based on the user's emotional state. The process begins with the user capturing a photo of themselves using their laptop's webcam—an action undertaken only with their explicit consent. Using a Convolutional Neural Network (CNN), the system analyzes the image to detect the users current emotional state. Once the emotion is identified, the system matches it to a curated playlist or category of songs that correspond to the detected mood. The selected song is then played automatically, fulfilling the user's musical preferences effortlessly. The proposed system enhances user engagement by dynamically adapting to their emotions, making it a promising approach for next-generation music recommendation platforms. (shrink)

Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, changes in (...) writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. The application of NLP for mental health detection is particularly impactful in healthcare and public safety. It enables early identification and intervention for at-risk individuals by providing clinicians and support organizations with actionable insights from large volumes of textual content. On social media platforms, it facilitates automated monitoring to detect crisis situations and initiate timely support. Additionally, in educational institutions and workplaces, such systems can promote mental well-being by identifying signs of distress and enabling appropriate counseling or outreach efforts. However, this field faces notable challenges, including data privacy concerns, ethical considerations, the risk of false positives or misdiagnosis, and ensuring the cultural and linguistic diversity of datasets to avoid bias. Ongoing research aims to improve model accuracy and interpretability while integrating multimodal data for more robust detection. The goal is to develop responsible and sensitive AI systems that assist in understanding and supporting mental health, ultimately contributing to a safer, more empathetic digital environment. (shrink)