Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

Energy Efficiency Optimization of Intelligent Reflective Surface-assisted Terahertz-RSMA System

This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation. The objective function for energy efficiency is optimized using the salp swarm algorithm (SSA) and compared with the successive convex approximation (SCA) technique. SCA technique requires multiple iterations to solve non-convex resource allocation problems, whereas SSA can consume less time to improve energy efficiency effectively. The simulation results show that SSA is better than SCA in improving system energy efficiency, and the time required is significantly reduced, thus optimizing the system's overall performance

Quantum Gaussian process regression

In this paper, a quantum algorithm based on gaussian process regression model is proposed. The proposed quantum algorithm consists of three sub-algorithms. One is the first quantum subalgorithm to efficiently generate mean predictor. The improved HHL algorithm is proposed to obtain the sign of outcomes. Therefore, the terrible situation that results is ambiguous in terms of original HHL algorithm is avoided, which makes whole algorithm more clear and exact. The other is to product covariance predictor with same method. Thirdly, the squared exponential covariance matrices are prepared that annihilation operator and generation operator are simulated by the unitary linear decomposition Hamiltonian simulation and kernel function vectors is generated with blocking coding techniques on covariance matrices. In addition, it is shown that the proposed quantum gaussian process regression algorithm can achieve quadratic faster over the classical counterpart

Tanshinone IIA inhibits exosome-induced cardiomyocyte pyroptosis through NLRP3/caspase 1 pathway

Purpose: To investigate the effect of Salvia miltiorrhiza, a traditional Chinese medicinal plant, on exosome-induced cardiomyocyte pyroptosis. Methods: Pyroptosis was induced in human AC cells using exosomes. Then, the effect of Danshen (dried roots of S. miltiorrhiza) on exosome-induced pyroptosis was determined using flow cytometry. The expressions of pro-inflammatory cytokines were measured by enzyme-linked immunosorbent assay (ELISA), while protein levels of cytokines were assayed by Western blotting. Results: Tanshinone IIA (Tan IIA), the bioactive molecule in Danshen, inhibited cardiomyocyte pyroptosis by significantly reducing the expressions of proinflammatory cytokines (p < 0.001). Thus, Tan IIA reduced pyroptosis induced by cardiomyocyte-derived exosome via inhibition of the expression of NLRP3 inflammasome in human AC cells. Conclusion: This study has identified a potential mechanism through which Danshen functions to prevent cardiac diseases. It involves, at least in part, the inhibition of pyroptosis in cardiomyocytes. Thus, tanshinone IIA may be a pharmacologically beneficial cardioprotective compound, especially when used against heart failure

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.Comment: Homepage: https://ailab-cvc.github.io/videocrafter; Github: https://github.com/AILab-CVC/VideoCrafte

StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter

Text-to-video (T2V) models have shown remarkable capabilities in generating diverse videos. However, they struggle to produce user-desired stylized videos due to (i) text's inherent clumsiness in expressing specific styles and (ii) the generally degraded style fidelity. To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image. Considering the scarcity of stylized video datasets, we propose to first train a style control adapter using style-rich image datasets, then transfer the learned stylization ability to video generation through a tailor-made finetuning paradigm. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image using a decoupling learning strategy. Additionally, we design a scale-adaptive fusion module to balance the influences of text-based content features and image-based style features, which helps generalization across various text and style combinations. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images. Experiments demonstrate that our approach is more flexible and efficient than existing competitors.Comment: Project page: https://gongyeliu.github.io/StyleCrafter.github.io/ ; GitHub repository: https://github.com/GongyeLiu/StyleCrafte

Preparation and Characterization of Nano-Cu/Polysaccharide Composite Antimicrobial Film and Its Control Effect on Black Spot Disease of Winter Jujube

In this study, a nano-Cu/polysaccharide composite film was prepared by the solution casting method using gelatin and sodium alginate as film-forming substrates. Before casting, green synthesized nano-Cu was incorporated into the film-forming solution by co-blending method. Field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TAG), diffuse reflectance spectroscopy (DRS), texture analysis (TA) and inductively coupled plasma-mass spectrometry (ICP-MS) were used to characterize the structure, light transmittance and physicochemical properties of nano-Cu and the composite film. The antifungal activity of the composite film was also evaluated and applied to the biological control of the black spot disease of winter jujube. Finally, the migration of Cu2+ in the composite film was measured. The results showed that the particle size of green synthetic nano-Cu was approximately 44 nm, and gelatin/sodium alginate could be used as an excellent carrier for nano-Cu. The composite film had good thermal stability, barrier properties and mechanical properties. In addition, the inhibition rates of the composite films with different concentrations of nano-Cu against Alternaria alternata, Fusarium and Botrytis cinerea were up to 87.80%, 77.73% and 81.96%, respectively, showing good and broad-spectrum antifungal properties. The half maximal inhibitory concentration (IC50) of nano-Cu against A. alternata biomass was 0.25 g/L. When stored for 10 days, the composite film with nano-Cu at 0.25 g/L reduced the lesion diameter by 52.53% and the incidence of black spot disease by 53.16% compared with the control group, and the migration of Cu2+ was 0.018 7 μg/mL. This study provides a new idea for the application of nano-Cu and a theoretical basis for the development of new antifungal materials

Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation

Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of existing video clips and synthesize a coherent storytelling video by customizing their appearances. We achieve this by developing a framework comprised of two functional modules: (i) Motion Structure Retrieval, which provides video candidates with desired scene or motion context described by query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates plot-aligned videos under the guidance of motion structure and text prompts. For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure. For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters. The videos are synthesized by following the structural guidance and appearance instruction. To ensure visual consistency across clips, we propose an effective concept personalization approach, which allows the specification of the desired character identities through text prompts. Extensive experiments demonstrate that our approach exhibits significant advantages over various existing baselines.Comment: Github: https://github.com/VideoCrafter/Animate-A-Story Project page: https://videocrafter.github.io/Animate-A-Stor