38 research outputs found
DETERMINATION OF DESIGN PARAMETERS OF ASPHALT PAVEMENT BASED ON PG TECHNOLOGY
The design parameters are one of the important factors to ensure the quality of asphalt pavement design. In “Highway Asphalt Pavement Design Specification” (JTGD50-2017), the stander of China, used the asphalt mixture anti-pressure resilience modulus at a single temperature of 20 ℃ as the design metrics. However, asphalt mixture, as a sticky-bullet plastic material, shows different mechanical properties at different temperatures. China is a vast territory, and there are great differences between the high and low temperature value (m and n) of each region. Therefore, it is unreasonable to design asphalt pavement only with the asphalt mixture anti-pressure resilience modulus value at 20 ℃. Studies show that the design parameters using PG technology can improve the high temperature anti-rutting and low temperature cracking performance of asphalt pavement
EduSAT: A Pedagogical Tool for Theory and Applications of Boolean Satisfiability
Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) are
widely used in automated verification, but there is a lack of interactive tools
designed for educational purposes in this field. To address this gap, we
present EduSAT, a pedagogical tool specifically developed to support learning
and understanding of SAT and SMT solving. EduSAT offers implementations of key
algorithms such as the Davis-Putnam-Logemann-Loveland (DPLL) algorithm and the
Reduced Order Binary Decision Diagram (ROBDD) for SAT solving. Additionally,
EduSAT provides solver abstractions for five NP-complete problems beyond SAT
and SMT. Users can benefit from EduSAT by experimenting, analyzing, and
validating their understanding of SAT and SMT solving techniques. Our tool is
accompanied by comprehensive documentation and tutorials, extensive testing,
and practical features such as a natural language interface and SAT and SMT
formula generators, which also serve as a valuable opportunity for learners to
deepen their understanding. Our evaluation of EduSAT demonstrates its high
accuracy, achieving 100% correctness across all the implemented SAT and SMT
solvers. We release EduSAT as a python package in .whl file, and the source can
be identified at https://github.com/zhaoy37/SAT_Solver
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Smart cities operate on computational predictive frameworks that collect,
aggregate, and utilize data from large-scale sensor networks. However, these
frameworks are prone to multiple sources of data and algorithmic bias, which
often lead to unfair prediction results. In this work, we first demonstrate
that bias persists at a micro-level both temporally and spatially by studying
real city data from Chattanooga, TN. To alleviate the issue of such bias, we
introduce Fairguard, a micro-level temporal logic-based approach for fair smart
city policy adjustment and generation in complex temporal-spatial domains. The
Fairguard framework consists of two phases: first, we develop a static
generator that is able to reduce data bias based on temporal logic conditions
by minimizing correlations between selected attributes. Then, to ensure
fairness in predictive algorithms, we design a dynamic component to regulate
prediction results and generate future fair predictions by harnessing logic
rules. Evaluations show that logic-enabled static Fairguard can effectively
reduce the biased correlations while dynamic Fairguard can guarantee fairness
on protected groups at run-time with minimal impact on overall performance.Comment: This paper was accepted by the 8th ACM/IEEE Conference on Internet of
Things Design and Implementatio
CitySpec with Shield: A Secure Intelligent Assistant for Requirement Formalization
An increasing number of monitoring systems have been developed in smart
cities to ensure that the real-time operations of a city satisfy safety and
performance requirements. However, many existing city requirements are written
in English with missing, inaccurate, or ambiguous information. There is a high
demand for assisting city policymakers in converting human-specified
requirements to machine-understandable formal specifications for monitoring
systems. To tackle this limitation, we build CitySpec, the first intelligent
assistant system for requirement specification in smart cities. To create
CitySpec, we first collect over 1,500 real-world city requirements across
different domains (e.g., transportation and energy) from over 100 cities and
extract city-specific knowledge to generate a dataset of city vocabulary with
3,061 words. We also build a translation model and enhance it through
requirement synthesis and develop a novel online learning framework with
shielded validation. The evaluation results on real-world city requirements
show that CitySpec increases the sentence-level accuracy of requirement
specification from 59.02% to 86.64%, and has strong adaptability to a new city
and a new domain (e.g., the F1 score for requirements in Seattle increases from
77.6% to 93.75% with online learning). After the enhancement from the shield
function, CitySpec is now immune to most known textual adversarial inputs
(e.g., the attack success rate of DeepWordBug after the shield function is
reduced to 0% from 82.73%). We test the CitySpec with 18 participants from
different domains. CitySpec shows its strong usability and adaptability to
different domains, and also its robustness to malicious inputs.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0313
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
Reward design is a key component of deep reinforcement learning, yet some
tasks and designer's objectives may be unnatural to define as a scalar cost
function. Among the various techniques, formal methods integrated with DRL have
garnered considerable attention due to their expressiveness and flexibility to
define the reward and requirements for different states and actions of the
agent. However, how to leverage Signal Temporal Logic (STL) to guide
multi-agent reinforcement learning reward design remains unexplored. Complex
interactions, heterogeneous goals and critical safety requirements in
multi-agent systems make this problem even more challenging. In this paper, we
propose a novel STL-guided multi-agent reinforcement learning framework. The
STL requirements are designed to include both task specifications according to
the objective of each agent and safety specifications, and the robustness
values of the STL specifications are leveraged to generate rewards. We validate
the advantages of our method through empirical studies. The experimental
results demonstrate significant reward performance improvements compared to
MARL without STL guidance, along with a remarkable increase in the overall
safety rate of the multi-agent systems
Integrating Voice-Based Machine Learning Technology into Complex Home Environments
To demonstrate the value of machine learning based smart health technologies,
researchers have to deploy their solutions into complex real-world environments
with real participants. This gives rise to many, oftentimes unexpected,
challenges for creating technology in a lab environment that will work when
deployed in real home environments. In other words, like more mature
disciplines, we need solutions for what can be done at development time to
increase success at deployment time. To illustrate an approach and solutions,
we use an example of an ongoing project that is a pipeline of voice based
machine learning solutions that detects the anger and verbal conflicts of the
participants. For anonymity, we call it the XYZ system. XYZ is a smart health
technology because by notifying the participants of their anger, it encourages
the participants to better manage their emotions. This is important because
being able to recognize one's emotions is the first step to better managing
one's anger. XYZ was deployed in 6 homes for 4 months each and monitors the
emotion of the caregiver of a dementia patient. In this paper we demonstrate
some of the necessary steps to be accomplished during the development stage to
increase deployment time success, and show where continued work is still
necessary. Note that the complex environments arise both from the physical
world and from complex human behavior