Robust Influence Maximization

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding $k$ seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.Comment: 12 pages, 4 figures, Technical Report, contains proofs for the paper appeared in KDD'201

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

The impact of generative artificial intelligence voiceover technology on user viewing behavioral intentions for short video film and television commentary on short video platforms

With the rapid development of the short video industry and continuous advancements in artificial intelligence technology, the application of generative artificial intelligence (AIGC) in video content creation has become increasingly widespread. Particularly in short videos that provide commentary on films and television, the use of AI-generated synthetic voice-over technology has garnered significant attention. Grounded in the Technology Acceptance Model (TAM), this study investigates whether perceived ease of use, perceived usefulness, perceived enjoyment, and community influence are significant positive factors affecting users\u27 attitudes and behavioral intentions towards watching short videos on these platforms. Additionally, the study examines the impact of user attitudes on behavioral intentions. The findings offer valuable insights for short video platform operators and content creators, and also hold substantial significance for promoting the application of AI technology in the media industry

Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition

The research and applications of multimodal emotion recognition have become increasingly popular recently. However, multimodal emotion recognition faces the challenge of lack of data. To solve this problem, we propose to use transfer learning which leverages state-of-the-art pre-trained models including wav2vec 2.0 and BERT for this task. Multi-level fusion approaches including coattention-based early fusion and late fusion with the models trained on both embeddings are explored. Also, a multi-granularity framework which extracts not only frame-level speech embeddings but also segment-level embeddings including phone, syllable and word-level speech embeddings is proposed to further boost the performance. By combining our coattention-based early fusion model and late fusion model with the multi-granularity feature extraction framework, we obtain result that outperforms best baseline approaches by 1.3% unweighted accuracy (UA) on the IEMOCAP dataset.Comment: Accepted to INTERSPEECH 202

Study on the Water Ecology Protection and Water Environment Remediation Technology, focusing on the heavy metal removal

Water environmental pollution has become a growing problem with the increase in pollutants released because of the expansion of human activities. Since water is peculiarly susceptible to pollution, water pollution control has garnered copious attention among the most crucial environmental conundrums. Therefore, water environment remediation is not only of great significance for river ecology, but also of theoretical and practical importance for sustainable human development. China’s central government passed the Yangtze River Protection Law in response to the need for changes in governance arrangements and cooperation. The purpose of this study is to explore the technology of heavy metal treatment in water environment remediation, to study and solve a series of problems such as water ecological maintenance methods, river desilting, and water body renewal. This paper will analyze the benefits of water environment remediation technologies and explore their potential for sustainable development, focusing on the removal of heavy metals from water body

Hans Christian Andersen HOUSE OF FAIRYTALES - Design Proposal Based On Experienceable Architectural Space

This master's thesis is based on Hans Christian Andersen House of Fairytales Ideas Competition which was held in the winter 2013/2014 in Odense, Denmark. The purpose of the competition was to find a design for a new Hans Christian Andersen House of Fairytales with a fairytale garden which should be a must-see attraction of international standing, where both architecture and content are clearly rooted in the fairytales and the history of the site. The competition has been used as practical framework of the thesis providing program and a defined site for examination. However, this thesis does not provide a competition entry, but rather shifts the focus on the design of experienceable architectural space. The thesis consists of two main parts, background research related to the competition contents and the design proposal. Background research is divided into three chapters: background and introduction, site studies and the objective of the design, which give basic information about the author, the city and the site as well as analysis diagrams that demonstrating the previous studies. The design proposal is the main part of this thesis, aiming to test a solution for a building project which is in need of experienceable architectural space

The Evolution and Future of Medical Robotic Diagnostics

This article provides a systematic review of research in the development of reliable and autonomous robotic systems capable not only of data collection but also of independent data analysis and interpretation. To better understand how to achieve such functions, four core modules are discussed. First, a data acquisition and preprocessing pipeline ensures the quality, consistency, and usability of incoming data by using multiple sensors to collect data and Manhattan distance to conduct correlation analysis. Second, using Probabilistic Neuro-Fuzzy Systems integrated with Artificial Intelligence (AI) along with Temporal Fusion Net and the model based on the SE-ResNet50 network, they are constructed and optimized for real-time diagnosis models. Third, fault prediction models including a cyber-physical system and a hybrid model forecast failures and maximize accuracy. Fourth, human-computer interaction can be improved by applying cloud-assisted wearable devices that are significant for reducing the interaction challenges and helping in real-time monitoring and diagnosis. In addition to the proposed framework, the paper analyzes key challenges according to the methods. It also discusses potential solutions and future development strategies. The findings of this study are expected to offer a solid foundation for advancing innovative research that supports the growth and wider adoption of medical robotic diagnostics

Striking nonlinear dynamics of mode-locked fibre lasers

We report on the real-time observation of various remarkable nonlinear phenomena in mode-locked fibre lasers. These include the build-up of dissipative solitons and soliton molecules, collision-induced soliton explosions, and the excitation and dynamics of breathing dissipative solitons and breather molecular complexes. Numerical simulations of the laser model support our experimental findings