Mothers\u27 Adaptation to Caring for a New Baby

To date, most research on parents\u27 adjustment after adding a new baby to their family unit has focused on mothers\u27 initial transition to parenthood. This past research has examined changes in mothers\u27 marital satisfaction and perceived well-being across the transition, and has compared their prenatal expectations to their postnatal experiences. This project assessed first-time and experienced mothers\u27 stress and satisfaction associated with parenting, their adjustment to competing demands, and their perceived well-being longitudinally before and after the birth of a baby. Additionally, how maternal and child-related variables influenced the trajectory of mothers\u27 postnatal adaptation was assessed. These variables included mothers\u27 age, their education level, their prenatal expectations and postnatal experiences concerning shared infant care, their satisfaction with the division of infant caregiving, and their perceptions of their infant\u27s temperament. Mothers (N = 136) completed an online survey during their third trimester and additional online surveys when their baby was approximately 2, 4, 6, and 8 weeks old.;First-time mothers prenatally expected a more equal division of infant caregiving between themselves and their partners than did experienced mothers. Both first-time and experienced mothers reported less assistance from their partners than they had prenatally expected. Additionally, they experienced almost twice as many violated expectations than met expectations. Growth curve modeling revealed that a cubic function of time best fit the trajectory of mothers\u27 postnatal parenting satisfaction. Mothers reported less parenting satisfaction at 4 weeks, compared to 2 and 6 weeks, and reported stability in their satisfaction between 6 and 8 weeks. A quadratic function of time best fit the trajectories of mothers\u27 postnatal parenting stress and adjustment to the demands of their baby. Mothers reported more stress and difficulty adjusting to their baby\u27s demands at 4 and 6 weeks, compared to 2 and 8 weeks. A linear function of time best fit the trajectories of mothers\u27 adjustment to home demands, generalized state anxiety, and depressive symptoms. Mothers reported less difficulty meeting home demands, less generalized anxiety, and fewer depressive symptoms across the postnatal period. Mothers\u27 violated expectations were associated with level differences in all aspects of mothers\u27 postnatal adaptation except their adjustment to home demands. Specifically, more violated expectations, in number or in magnitude, were associated with poorer postnatal adaptation. Mothers\u27 violated expectations were not associated with the slope of mothers\u27 postnatal adaptation trajectories. Exploratory models revealed that other maternal and child-related variables also impacted the level and slope of mothers\u27 postnatal adaptation.;Overall, first-time and experienced mothers were more similar than different in regards to their postnatal adaptation. This study suggests that prior findings concerning adults\u27 initial transition to parenthood may also apply to adults during each addition of a new baby into the family unit. Additionally, mothers who reported less of a mismatch between their expectations and experiences concerning shared infant care had fewer issues adapting the postnatal period. Thus, methods to increase the assistance mothers receive from their partner should be sought. Limitations of this study and suggestions for future research are also discussed

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks

Labeled-Image CAPTCHA: concept of a secured and universally useful CAPTCHA

Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used online security tool that ensures that a computer program is not posing as a human user. While smart programs with advanced image processing capability have already cracked picture based captcha systems there is a need for making the test harder. This paper presents a design prototype of a simplified type of labeled-image captcha where a picture of a common animal or household item is marked with a number of different labels and the users will be asked to provide the correct label for specific parts of the picture. Due to human’s familiarity with body shapes and part names of such common pictures, they will easily identify a specific organ/parts of the picture. Such labeled-image captcha tests are expected to be very easy for human users regardless of their culture, age, gender, educational background and other discriminations but tough for the bots and automated computer programs

Labeled-Image CAPTCHA: concept of a secured and universally useful CAPTCHA

Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used online security tool that ensures that a computer program is not posing as a human user. While smart programs with advanced image processing capability have already cracked picture based captcha systems there is a need for making the test harder. This paper presents a design prototype of a simplified type of labeled-image captcha where a picture of a common animal or household item is marked with a number of different labels and the users will be asked to provide the correct label for specific parts of the picture. Due to human’s familiarity with body shapes and part names of such common pictures, they will easily identify a specific organ/parts of the picture. Such labeled-image captcha tests are expected to be very easy for human users regardless of their culture, age, gender, educational background and other discriminations but tough for the bots and automated computer programs

Foundations, Properties, and Security Applications of Puzzles: A Survey

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay---though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this paper, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.Comment: This article has been accepted for publication in ACM Computing Survey

Using Spammers\u27 Computing Resources for Volunteer Computing

Spammers are continually looking to circumvent counter-measures seeking to slow them down. An immense amount of time and money is currently devoted to hiding spam, but not enough is devoted to effectively preventing it. One approach for preventing spam is to force the spammer\u27s machine to solve a computational problem of varying difficulty before granting access. The idea is that suspicious or problematic requests are given difficult problems to solve while legitimate requests are allowed through with minimal computation. Unfortunately, most systems that employ this model waste the computing resources being used, as they are directed towards solving cryptographic problems that provide no societal benefit. While systems such as reCAPTCHA and FoldIt have allowed users to contribute solutions to useful problems interactively, an analogous solution for non-interactive proof-of-work does not exist. Towards this end, this paper describes MetaCAPTCHA and reBOINC, an infrastructure for supporting useful proof-of-work that is integrated into a web spam throttling service. The infrastructure dynamically issues CAPTCHAs and proof-of-work puzzles while ensuring that malicious users solve challenging puzzles. Additionally, it provides a framework that enables the computational resources of spammers to be redirected towards meaningful research. To validate the efficacy of our approach, prototype implementations based on OpenCV and BOINC are described that demonstrate the ability to harvest spammer\u27s resources for beneficial purposes

Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions

The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and data are strong defense systems against malicious bots. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication process to confirm that the user is a human hence, access is granted. This paper provides an in-depth survey on CAPTCHAs and focuses on two main things: (1) a detailed discussion on various CAPTCHA types along with their advantages, disadvantages, and design recommendations, and (2) an in-depth analysis of different CAPTCHA breaking techniques. The survey is based on over two hundred studies on the subject matter conducted since 2003 to date. The analysis reinforces the need to design more attack-resistant CAPTCHAs while keeping their usability intact. The paper also highlights the design challenges and open issues related to CAPTCHAs. Furthermore, it also provides useful recommendations for breaking CAPTCHAs

Graffiti Networks: A Subversive, Internet-Scale File Sharing Model

The proliferation of peer-to-peer (P2P) file sharing protocols is due to their efficient and scalable methods for data dissemination to numerous users. But many of these networks have no provisions to provide users with long term access to files after the initial interest has diminished, nor are they able to guarantee protection for users from malicious clients that wish to implicate them in incriminating activities. As such, users may turn to supplementary measures for storing and transferring data in P2P systems. We present a new file sharing paradigm, called a Graffiti Network, which allows peers to harness the potentially unlimited storage of the Internet as a third-party intermediary. Our key contributions in this paper are (1) an overview of a distributed system based on this new threat model and (2) a measurement of its viability through a one-year deployment study using a popular web-publishing platform. The results of this experiment motivate a discussion about the challenges of mitigating this type of file sharing in a hostile network environment and how web site operators can protect their resources