Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection

Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider. Previous research on secure reasoning using multi-party computation (MPC) has proven to be impractical for LLM applications due to its time-consuming and communication-intensive nature. While lightweight anonymization techniques can protect private information in prompts through substitution or masking, they fail to recover sensitive data replaced in the LLM-generated results. In this paper, we expand the application scenarios of anonymization techniques by training a small local model to de-anonymize the LLM's returned results with minimal computational overhead. We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization, respectively. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models. Furthermore, we conduct experiments to evaluate HaS's usability in translation and classification tasks. The experimental findings demonstrate that the HaS framework achieves an optimal balance between privacy protection and utility

Some Properties of the Generalized Stuttering Poisson Distribution and Its Applications

Based on the probability generating function of stuttering Poisson distribution (SPD), this paper considers some equivalent propositions of SPD. From this, we show that some distributions in the application of non-life insurance actuarial science are SPD, such as negative binomial distribution, compound Poisson distribution etc.. By weakening condition of equivalent propositions of SPD, we define the generalized SPD and prove that any non-negative discrete random variable X with P{X = 0} > 0.5 obey generalized SPD. Then, we discuss the waiting time distribution of generalized stuttering Poisson process. We consider cumulant estimation of generalized SPD's parameters. As an application, we use SPD with four parameters (4th SPD) to fit auto insurance claim data. The fitting results show that 4th SPD is more accurate than negative binomial and Poisson distribution

Materials and Designs for Heat Harvesting and Thermal Management of Asphalt Pavements

Asphalt pavements are subjected to annual, seasonal, and daily temperature fluctuations, which can lead to cracks and even failure of the pavements. Additionally, snow removal in winter on highways and parking lots in the cold-climate region is often challenging and the current snow removal approaches (salt and plowing) are neither efficient enough nor environmental-friendly. Here we propose a multifunctional system that utilizes solar and geothermal energy for heat harvesting and temperature regulation of the pavements, which allows self-de-icing in winter, cooling in summer, reduced maintenance cost, and extended life span. This new pavement technology consists of an underground heat exchanger, circulation pumps, thermal tubes, a photovoltaic system, and thermally conductive pavement overly. This presentation will focus on investigation of the thermal, electrical, and mechanical performance of the asphalt materials modified with conductive additives including carbon nanotubes and graphene nanoplatelets. Using sonication combined with an oil bath and a mechanical shear mixer, we can achieve a homogenous dispersion of the conductive modifiers in asphalt binders, which is verified by a digital microscope. Our results show that the combination of carbon nanotubes and graphene nanoplates can enhance the thermal conductivity of the asphalt binders more than any of the single-phase addition. More work on the electrical conductivity improvement in using these modifiers are underway. These modified asphalt binders are expected to increase asphalt pavements’ overall thermal conductivity, which is an integral part of the multifunctional pavement system

Tree-ring stable carbon isotope-based June-September maximum temperature reconstruction since AD 1788, north-west Thailand

The first study of tree-ring stable carbon isotopes in Thailand has demonstrated that stable carbon isotope in northwestern Thailand represents a promising proxy for the temperature reconstruction of core-monsoon periods. A tree-ring delta C-13 chronology was constructed based on four cores covering the period of 1788-2013. After removing the long-term decreasing trend reflecting atmospheric CO2 concentrations, the Delta C-13 chronology was able to capture both temperature and hydro-climate signals Delta C-13 chronology showed particularly strong and significant negative correlation (r = -0.62, p < 0.0001) with June-September maximum temperature (CRU TS 3.24). The maximum temperature was reconstructed, which explained 37.8% of the variance in the instrumental maximum temperatures over the period of 1901-2013. The maximum temperature reconstruction revealed that four cooler and three warmer periods, as well as a slightly increasing temperature trend, occurred during the late seventeenth to mid-eighteenth centuries, which were followed by severe temperature fluctuations during the twentieth century century. While the sea surface temperature anomaly in the Indian Ocean might not affect the maximum temperature, its unstable relationship with the El Nino-Southern Oscillation (ENSO) was detected. In addition, a close relationship was observed between the maximum temperature and ENSO during the negative phase of the Pacific Decadal Oscillation (PDO), but this relationship was lost during the positive phase of the PDO

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

Detecting phone-related pedestrian distracted behaviours via a two-branch convolutional neural network

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phonerelated distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phonerelated pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods

Global attractivity and permanence of a SVEIR epidemic model with pulse vaccination and time delay

AbstractIn this study, we propose a new SVEIR epidemic disease model with time delay, and analyze the dynamic behavior of the model under pulse vaccination. Pulse vaccination is an effective strategy for the elimination of infectious disease. Using the discrete dynamical system determined by the stroboscopic map, we obtain an ‘infection-free’ periodic solution. We also show that the ‘infection-free’ periodic solution is globally attractive when some parameters of the model under appropriate conditions. The permanence of the model is investigated analytically. Our results indicate that a large vaccination rate or a short pulse of vaccination or a long latent period is a sufficient condition for the extinction of the disease

Secure Software Engineering Education: Knowledge Area, Curriculum and Resources

This paper reviews current efforts and resources in secure software engineering education, with the goal of providing guidance for educators to make use of these resources in developing secure software engineering curriculum. These resources include Common Body of Knowledge, reference curriculum, sample curriculum materials, hands-on exercises, and resources developed by industry and open source community. The relationship among the Common Body of Knowledge proposed by the Department of Homeland Security, the Software Engineering Institute at Carnegie Mellon University, and ACM/IEEE are discussed. The recent practices on secure software engineering education, including secure software engineering related programs, courses, and course modules are reviewed. The course modules are categorized into four categories to facilitate the adoption of these course modules. Available hands-on exercises developed for teaching software security are described and mapped to the taxonomy of coding errors. The rich resources including various secure software development processes, methods and tools developed by industry and open source community are surveyed. A road map is provided to organize these resources and guide educators in adopting these resources and integrating them into their courses