The Role of Social Distance in Narrative Persuasion for Risk Prevention

This research was designed to examine how narrative messages about safe driving in media can influence favorable persuasive outcomes related to driving without cell phone use. Based on the entertainment overcoming resistance model (EORM) and construal level theory (CLT), three hypotheses were proposed that considered the role of narrative engagement and persuasive resistance in increasing favorable outcomes associated with safe driving. For this study in particular, CLT and EORM predicted that a narrative featuring low social distance would be more effective in increasing favorable persuasive outcomes by increasing narrative engagement and decreasing persuasive resistance. It was also predicted that narrative engagement and persuasive resistance would mediate the relationship between social distance and persuasive outcomes. An experiment was conducted among college students using different versions of news stories as the stimuli to test the hypotheses. Results from a series of hierarchical regressions revealed that the low social distance narrative actually increased persuasive resistance, which was contrary to what was predicted. It was also found that one form of persuasive resistance was a significant mediator in the relationship between social distance and persuasive outcomes. This study suggests that when testing the propositions of construal level theory under the context of narratives, it is important to think about how CLT propositions will interact with narrative features and produce unique persuasive outcomes through narrative mechanisms

Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.Comment: Published in AAAI 201

An Optimal Energy Efficient Design of Artificial Noise for Preventing Power Leakage based Side-Channel Attacks

Side-channel attacks (SCAs), which infer secret information (for example secret keys) by exploiting information that leaks from the implementation (such as power consumption), have been shown to be a non-negligible threat to modern cryptographic implementations and devices in recent years. Hence, how to prevent side-channel attacks on cryptographic devices has become an important problem. One of the widely used countermeasures to against power SCAs is the injection of random noise sequences into the raw leakage traces. However, the indiscriminate injection of random noise can lead to significant increases in energy consumption in device, and ways must be found to reduce the amount of energy in noise generation while keeping the side-channel invisible. In this paper, we propose an optimal energy-efficient design for artificial noise generation to prevent side-channel attacks. This approach exploits the sparsity among the leakage traces. We model the side-channel as a communication channel, which allows us to use channel capacity to measure the mutual information between the secret and the leakage traces. For a given energy budget in the noise generation, we obtain the optimal design of the artificial noise injection by solving the side-channel's channel capacity minimization problem. The experimental results also validate the effectiveness of our proposed scheme

LMLFM: Longitudinal Multi-Level Factorization Machine

We consider the problem of learning predictive models from longitudinal data, consisting of irregularly repeated, sparse observations from a set of individuals over time. Such data often exhibit {\em longitudinal correlation} (LC) (correlations among observations for each individual over time), {\em cluster correlation} (CC) (correlations among individuals that have similar characteristics), or both. These correlations are often accounted for using {\em mixed effects models} that include {\em fixed effects} and {\em random effects}, where the fixed effects capture the regression parameters that are shared by all individuals, whereas random effects capture those parameters that vary across individuals. However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables. We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. We establish the convergence properties, and analyze the computational complexity, of LMLFM. We present results of experiments with both simulated and real-world longitudinal data which show that LMLFM outperforms the state-of-the-art methods in terms of predictive accuracy, variable selection ability, and scalability to data with large number of variables. The code and supplemental material is available at \url{https://github.com/junjieliang672/LMLFM}.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence, accepte

Age Differences in Stress and Coping: Problem-Focused Strategies Mediate the Relationship between Age and Positive Affect

The present study examined the different types of stressors experienced by adults of different ages, their coping strategies, and positive/negative affect. A mediation hypothesis of coping strategies was tested on the relationships between age and positive/negative affect. One-hundred and ninety-six community-dwelling adults (age range 18-89 years old) reported the most stressful situation they experienced in the past month and coping strategies. Levels of positive and negative affect in the past month were also measured. Content analysis revealed age differences in different types of stressors adults reported. Three types of coping strategies were found: problem-focused, positive emotion-focused, and negative emotion-focused coping. Older adults were less likely than younger adults to use problem-focused coping and reported lower levels of positive affect. Path analysis supported the mediation hypothesis, showing that problem-focused coping mediated the relationship between age and positive affect. Implications are discussed on the importance of promoting problem-focused coping among older adults