The effect of psychological reactance on acceptance of campaign message: A case of stop texting while driving campaign in college students

This study is to investigate how the psychological reactance generates impact on acceptance of the campaign message of stop texting while driving among college students. A total of 180 undergraduate students completed the online survey asking for their cognitive and affective responses to the high- or low-threat campaign messages. Three hypotheses were tested among strength of reactance, degree of threat to freedom, amount of negative attitudes, and behavioral intention. This study found that: (1) In both high-threat and low-threat conditions, degree of threat to freedom one perceived is positively related to strength of reactance this individual experiences; (2) People who experienced stronger reactance had more negative attitudes toward the campaign message in high-threat condition, while in low-threat condition the result was not significant; (3) No significant result supports the assumption that strength of reactance is negatively related to the behavioral intention to follow the advocacy in the campaign message

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

Refill Friction Stir Spot Welding Of Dissimilar Alloys

Lightweight alloy materials such as magnesium and aluminum alloys are frequently employed in the automotive and manufacturing industry in order to improve vehicle fuel economy. This creates a pressing need for joining of these materials to each other, as well as to steels. Given the drastic difference in thermal and mechanical properties of these materials and the limited solubility of aluminum or magnesium in steel, dissimilar alloy fusion welding is exceptionally difficult. Refill friction stir spot welding (RFSSW) is a solid-state joining technology which connects two materials together with minimal heat input or distortion. The RFSSW process involves a three-piece non-consumable tool with independently controlled sleeve and pin components, which rotate simultaneously at a constant speed with the sleeve penetrating into only the top sheet. Joining of Al 5754 alloy and DP 600 plate using friction stir seam welding is investigated. Two travel speeds of the shoulder are used to compare the mechanical and microstructural properties of the two kinds of welds made. Scanning electron microscopy (SEM) and optical microscopy are utilized to characterize the microstructure. Mechanical properties are evaluated using tensile testing. Joining of Al 6063-T6 and Zn coated DP 600 steel using RFSSW is studied. Spot welds could reach a maximum overlap shear load of 3.7 kN when using a tool speed of 2100 RPM, a 2.5 s welding time and 1.1 mm of penetration into the upper Al 6063 sheet. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were conducted to characterize the interface, which revealed that zinc layer was displaced into the interface and upper sheet, which may facilitate the bonding between the two sheets. Microhardness measurements reveal that the fracture path propagates through the soft heat affected zone of the Al alloy during overlap shear testing. Joining of Mg alloys ZEK with Zn coated DP 600 steel sheets by RFSSW is also studied here. In joints between ZEK 100 and DP 600, the maximum overlap shear fracture load is 4.7 kN, when a 1800 RPM tool speed, 3.0 s welding time and 1.5 mm penetration into the upper ZEK 100 sheet is applied. SEM and transmission electron microscopy (TEM) revealed that a continuous layer of FeAl2 particles accommodate bonding of the ZEK 100 and DP 600 sheets, which appears to have originated from the galvanized coating on the DP 600. If the zinc layer is removed then the maximum overlap shear fracture load is 3.1 kN. X-ray diffraction analysis of the interface between the Mg alloy and the DP 600 steel on the Mg side also revealed that intermetallic (IMCs) such as FeAl2 existed as an interfacial layer between the two sheets. It can be revealed from the displacement curve that the absorbed energy of the weld made under the condition of 1800 RPM, 3.5s, and 1.5mm of plunge depth in tensile testing up to failure point is approximately 2.73J

Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

Function signature recovery is important for many binary analysis tasks such as control-flow integrity enforcement, clone detection, and bug finding. Existing works try to substitute learning-based methods with rule-based methods to reduce human effort.They made considerable efforts to enhance the system's performance, which also bring the side effect of higher resource consumption. However, recovering the function signature is more about providing information for subsequent tasks, and both efficiency and performance are significant. In this paper, we first propose a method called Nimbus for efficient function signature recovery that furthest reduces the whole-process resource consumption without performance loss. Thanks to information bias and task relation (i.e., the relation between parameter count and parameter type recovery), we utilize selective inputs and introduce multi-task learning (MTL) structure for function signature recovery to reduce computational resource consumption, and fully leverage mutual information. Our experimental results show that, with only about the one-eighth processing time of the state-of-the-art method, we even achieve about 1% more prediction accuracy over all function signature recovery tasks

New algorithms for solving high-dimensional time-dependent optimal control problems and their applications in infectious disease models

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringChih-Hang 'John' WuInfectious diseases have been the primary cause of human death worldwide nowadays. The optimal control strategy for infectious disease has attracted increasing attention, becoming a significant issue in the healthcare domain. Optimal control of diseases can affect the progression of diseases and achieve high-quality healthcare. In previous studies, massive efforts on the optimal control of diseases have been made. However, some infectious diseases' mortality is still high and even developed into the second highest cause of mortality in the US. According to the limitations in existing research, this research aims to study the optimal control strategy via some industrial engineering techniques such as mathematical modeling, optimization algorithm, analysis, and numerical simulation. To better understand the optimal control strategy, two infectious disease models (epidemic disease, sepsis) are studied. Complex nonlinear time-series and high-dimensional infectious disease control models are developed to study the transmission and optimal control of deterministic SEIR or stochastic SIS epidemic diseases. In addition, a stochastic sepsis control model is introduced to study the progression and optimal control for sepsis system considering possible medical measurement errors or system uncertainty. Moreover, an improved complex nonlinear sepsis model is presented to more accurately study the sepsis progression and optimal control for sepsis system. In this dissertation, some analysis methods such as stability analysis, bifurcation analysis, and sensitivity analysis are utilized to help reader better understand the model behavior and the effectiveness of the optimal control. The significant contributions of this dissertation are developing or improving nonlinear complex disease optimal control models and proposing several effective and efficient optimization algorithms to solve the optimal control in those researched disease models, such as an optimization algorithm combining machine learning (EBOC), an improved Bayesian Optimization algorithm (IBO algorithm), a novel high-dimensional Bayesian Optimization algorithm combining dimension reduction and dimension fill-in (DR-DF BO algorithm), and a high-dimensional Bayesian Optimization algorithm combining Recurrent Neural Network (RNN-BO algorithm). Those algorithms can solve the optimal control solution for complex nonlinear time-series and high-dimensional systems. On top of that, numerical simulation is used to demonstrate the effectiveness and efficiency of the proposed algorithms

Saliency-Augmented Memory Completion for Continual Learning

Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.Comment: Published at SIAM SDM 2023. 15 pages, 6 figures. Code: https://github.com/BaiTheBest/SAM

The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms

Large language models (LLMs) have garnered significant attention across various research disciplines, including the wireless communication community. There have been several heated discussions on the intersection of LLMs and wireless technologies. While recent studies have demonstrated the ability of LLMs to generate hardware description language (HDL) code for simple computation tasks, developing wireless prototypes and products via HDL poses far greater challenges because of the more complex computation tasks involved. In this paper, we aim to address this challenge by investigating the role of LLMs in FPGA-based hardware development for advanced wireless signal processing. We begin by exploring LLM-assisted code refactoring, reuse, and validation, using an open-source software-defined radio (SDR) project as a case study. Through the case study, we find that an LLM assistant can potentially yield substantial productivity gains for researchers and developers. We then examine the feasibility of using LLMs to generate HDL code for advanced wireless signal processing, using the Fast Fourier Transform (FFT) algorithm as an example. This task presents two unique challenges: the scheduling of subtasks within the overall task and the multi-step thinking required to solve certain arithmetic problem within the task. To address these challenges, we employ in-context learning (ICL) and Chain-of-Thought (CoT) prompting techniques, culminating in the successful generation of a 64-point Verilog FFT module. Our results demonstrate the potential of LLMs for generalization and imitation, affirming their usefulness in writing HDL code for wireless communication systems. Overall, this work contributes to understanding the role of LLMs in wireless communication and motivates further exploration of their capabilities