Marital status is associated with psychological well-being, with the married faring better than the formerly and never-married. However, this conclusion derives from research focusing more on negative than positive well-being. We examine the association between marital status and negative well-being, measured as depressive symptoms, and positive well-being, measured as autonomy, environmental mastery, personal growth, positive relations with others, self-acceptance, and purpose in life. Using Wave 2 of Midlife in the United States (2004–2006; n = 1,711), we find that the continuously married fare better on the negative dimension than do the formerly married. The results for some measures of positive well-being also reveal an advantage for the continuously married, compared with the formerly and the never-married. However, results for other positive measures indicate that the unmarried, and the remarried, fare better—not worse—than the continuously married. Further, some results suggest greater benefits for remarried or never-married women than men.

No abstract

No abstract

No abstract

Machine learning is the study of computer algorithms that improve automatically through experience and using data. Different from traditional machine learning techniques that rely on human experts to extract features from the data, deep learning uses multiple layers of artificial neural networks to progressively extract higher-level features from the raw input. Because of society uses and adopting digital data increasingly, the digital dependence also continues to grow. In modern society, we deal with big data day in and day out; machine learning and deep learning techniques are pivotal in processing and analyzing these big data, including but not limited to our daily experience ranging from shopping behavior to metadata of medical records to improve treatment and therapy in different medical fields. Putting aside the complicated and sophisticated mathematical equations, in this chapter, the authors introduce machine learning and deep learning techniques by going through several hands-on projects with Python.

The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.

No abstract

Aim: Although deep learning has been applied in image forgery detection, to our knowledge, it still falls short of a comprehensive comparison study in detecting seam-carving images in multimedia forensics by comparing the popular deep learning models, which is addressed in this study. Methods: To investigate the performance in detecting seam-carving-based image forgery with popular deep learning models that were used in image forensics, we compared nine different deep learning models in detecting untouched JPEG images, seam-insertion images, and seam removal images (three-class classification), and in distinguishing modified seam-carving images from untouched JPEG images (binary classification). We also investigate the different learning algorithms with the Efficientnet-B5 in adjusting the learning rate with three popular optimizers in deep learning. Results: Our study shows that EfficientNet performs the best among the nine different deep learning frameworks, followed by SRnet, and LFnet. Different algorithms for adjusting the learning rate result in different detection testing accuracy with Efficientnet-B5. In our experiments, decouples the optimal choice of weight decay factor from the setting of the learning rate (AdamW) is generally superior to Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD). Our study also indicates that deep learning is very promising for image forensics, such as the detection of image forgery. Conclusion: Deep learning is very promising in image forensics that is hardly discernable to human perceptions, but the performance varies over different learning models and frameworks. In addition to the models, the optimizer has a considerable impact on the final detection performance. We would recommend EfficientNet, LFnet and SRnet for seam-carving detection.