494 research outputs found
Effect of Heating Rate on the Municipal Sewage Sludge Pyrolysis Character
AbstractIn this paper, the TG and DTG curve of sewage sludge from Xi’an Business Water Limited Corporation were derived from tests of thermogravimetric analyzer. Pyrolysis characteristics and kinetics of dried sludge were investigated using thermalanalysis. The result showed that there are three temperature ranges in which the sludge lost weight in the process of pyrogenation. Kinetic fitting equation of the second pyrolysis reaction stage were founded and the kinetic parameters of pyrolysis reaction were calculated respectively by Coats-Redfern integration method, and the effect of heating rate on the sludge pyrolysis characteristics was discussed
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
Computational study on planar dominating set problem
AbstractRecently, there has been significant theoretical progress towards fixed-parameter algorithms for the DOMINATING SET problem of planar graphs. It is known that the problem on a planar graph with n vertices and dominating number k can be solved in O(2O(k)n) time using tree/branch-decomposition based algorithms. In this paper, we report computational results of Fomin and Thilikos algorithm which uses the branch-decomposition based approach. The computational results show that the algorithm can solve the DOMINATING SET problem of large planar graphs in a practical time and memory space for the class of graphs with small branchwidth. For the class of graphs with large branchwidth, the size of instances that can be solved by the algorithm in practice is limited to about one thousand edges due to a memory space bottleneck. The practical performances of the algorithm coincide with the theoretical analysis of the algorithm. The results of this paper suggest that the branch-decomposition based algorithms can be practical for some applications on planar graphs
Protecting the Intellectual Property of Diffusion Models by the Watermark Diffusion Process
Diffusion models have emerged as state-of-the-art deep generative
architectures with the increasing demands for generation tasks. Training large
diffusion models for good performance requires high resource costs, making them
valuable intellectual properties to protect. While most of the existing
ownership solutions, including watermarking, mainly focus on discriminative
models. This paper proposes WDM, a novel watermarking method for diffusion
models, including watermark embedding, extraction, and verification. WDM embeds
the watermark data through training or fine-tuning the diffusion model to learn
a Watermark Diffusion Process (WDP), different from the standard diffusion
process for the task data. The embedded watermark can be extracted by sampling
using the shared reverse noise from the learned WDP without degrading
performance on the original task. We also provide theoretical foundations and
analysis of the proposed method by connecting the WDP to the diffusion process
with a modified Gaussian kernel. Extensive experiments are conducted to
demonstrate its effectiveness and robustness against various attacks
Machine Unlearning: Solutions and Challenges
Machine learning models may inadvertently memorize sensitive, unauthorized,
or malicious data, posing risks of privacy violations, security breaches, and
performance deterioration. To address these issues, machine unlearning has
emerged as a critical technique to selectively remove specific training data
points' influence on trained models. This paper provides a comprehensive
taxonomy and analysis of machine unlearning research. We categorize existing
research into exact unlearning that algorithmically removes data influence
entirely and approximate unlearning that efficiently minimizes influence
through limited parameter updates. By reviewing the state-of-the-art solutions,
we critically discuss their advantages and limitations. Furthermore, we propose
future directions to advance machine unlearning and establish it as an
essential capability for trustworthy and adaptive machine learning. This paper
provides researchers with a roadmap of open problems, encouraging impactful
contributions to address real-world needs for selective data removal
On the Security Bootstrapping in Named Data Networking
By requiring all data packets been cryptographically authenticatable, the
Named Data Networking (NDN) architecture design provides a basic building block
for secured networking. This basic NDN function requires that all entities in
an NDN network go through a security bootstrapping process to obtain the
initial security credentials. Recent years have witnessed a number of proposed
solutions for NDN security bootstrapping protocols. Built upon the existing
results, in this paper we take the next step to develop a systematic model of
security bootstrapping: Trust-domain Entity Bootstrapping (TEB). This model is
based on the emerging concept of trust domain and describes the steps and their
dependencies in the bootstrapping process. We evaluate the expressiveness and
sufficiency of this model by using it to describe several current bootstrapping
protocols
Technology acceptance comparison between on-road dynamic message sign and on-board human machine interface for connected vehicle-based variable speed limit in fog area
Purpose – Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver's driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors. Design/methodology/approach – This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density. Findings – The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects. Originality/value – Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems
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