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How users perceive password memorability is not well understood, despite passwords being studied thoroughly. We investigate the relationship between users' perceptions of the memorability of a number of passwords and the users' electroencephalogram (EEG) data collected using brain-computer interfaces (BCIs). In this study, we asked 75 participants to rank the comparative memorability of 15 blocks of 5 passwords each, while collecting their EEG data. Passwords had the same length but differed in characteristics and strength. We were able to find a consistency between how users perceive password memorability, the EEG signals elicited by the least and most memorable passwords from a list, and the actual measured strength of these passwords. Our results suggest that EEG signals could fill the gap in the absence of information about how users rank passwords, or password strength, with an 82% accuracy. [ABSTRACT FROM AUTHOR]

AbstractBackground:In Latin America, public health systems that manage and warrant the health of the population lack mechanisms and technological capabilities that enable them to accept and adopt initiatives focused to guide, look after, and improve the quality of life of millions of patients with diabetes who need attention and special care. However, the proposal presented here for a holistic, interactive, and persuasive model to facilitate self-care of diabetes patients (hiPAPD) is the first proposal in Panama, Central America, and the Caribbean Region to develop and implement information communications technology (ICT) platforms for the care of patients with chronic diseases such as diabetes.Methods:The process of experimentation was initiated with an agreement with all the staff of the project to comply with the international biomedical studies stipulations, having as reference the Declaration of Helsinki of the World Medical Association (Recommendations to Guide to Doctors in Biomedical Research on People).Results:After several months of evaluation and ongoing work the study obtained successful validation of the hiPAPD model. The project had the support of 107 patients with diabetes, their families, friends, doctors, nurses and nursing assistants, and social groups in rural communities. Finally, the project contributed to society with a highly innovative ICT environment that facilitates self-care of diabetes patients without financial resources and health.Conclusions:A timely health treatment at a decisive moment may be the difference in care for patients. Through the validation process conducted in this research initiative, it was demonstrated that the hiPAPD model, from the perspective of the patient with diabetes, relatives, friends, health workforce (nurses and nursing assistants), doctors, and societal contexts, allowed the improvement of the quality of life of patients with diabetes in poor rural zones of Panama. [ABSTRACT FROM AUTHOR]

Abstract: Cyber child pornography is an increasingly visible problem in society today. With the growth in home Personal Computer (PC) usage and more readily available access to the World Wide Web over the past decade, child pornographers have found a convenient venue for sharing horrific pictures of children being sexually abused. Also, police and lawyers around the globe have found that detecting and prosecuting cyber child pornographers have become onerous chores, often with a high failure rate of placing perpetrators behind bars. The methods currently employed by law enforcement officers to combat cyber child pornography may be considered to be primitive and inefficient. In this paper, we review the major social, legal, and technological issues facing citizens, lawmakers, and the police regarding cyber child pornography. We also propose a new technological approach for combating online child pornography. In particular, we propose a source address reputation system based on Bloom filters and a novel classification system utilizing a stochastic weak estimator, coupled with a linear classifier. We believe that our proposed method for identifying offensive online material would be attractive to law enforcement globally, because it can be implemented with acceptable overheads. [Copyright &y& Elsevier]

There has been a steep increase in the use of machine learning for various healthcare applications like the diagnosis of diseases, drug discovery, medical image analysis, etc. Machine learning solutions are proven to be more efficient and less time-consuming than conventional approaches. In this paper, we leverage the advantages of machine learning models to enable a humanoid robot to assist mental health patients. Facial expression and human voice are some of the most demonstrative ways to analyze human emotions, especially for the mentally challenged. We carry out this assistance by constantly monitoring the patient's voice (or audio) and facial expressions to predict human emotions. To implement the model of audio monitoring, we train three different machine learning and deep learning models to compare and choose the better model. Similarly, for facial recognition, we train a deep learning model using a specific dataset to predict facial expressions from the video captured in real time. We then integrate the better-performing machine learning models into a web interface for demonstration. [ABSTRACT FROM AUTHOR]

Abstract: A methodology is provided here to assist in the design of secure interactive applications. In particular, this methodology helps design an adequate security information feedback based on User Interface Patterns, the resulting feedback is then evaluated against a set of design/evaluation criteria called Human–Computer Interaction for Security (HCI-S). In case of a security issue the security information feedback is generally presented using the visual and auditory channels required to achieve an effective notifications, and it is explicitly specified in the design of user interfaces for secure web system. [Copyright &y& Elsevier]

Abstract: Acoustic Doppler Current Profiler data collected during three successive surveys in the Gulf of Cádiz in May–June 2001 have been used to analyse the surface circulation on the continental shelves of the Gulf of Cádiz and how this circulation matches the circulation in the ocean side of the Gulf. The wider and larger eastern continental shelf holds a cyclonic circulation bounded at the south by a shelf-break jet that is identified with the Huelva front. The coastal current that closes the gyre at the north is identified with the warm counterflow mentioned in the literature. Under westerly winds, this counter current recirculates toward the east while recent upwelled water near Cape Santa Maria is advected downstream by the shelf-break jet, leaving the cold signature at the surface that has been identified historically with the Huelva front. Under easterlies, part of the coastal counterflow invades the western continental shelf while the remaining recirculates eastward to close the cyclonic cell. The western continental shelf and slope is occupied by a larger-scale cyclonic eddy that extends into the deep ocean. This eddy has vertical length scale of hundreds of metres and is linked to the general wind forcing in the area. Both cyclonic structures are bounded at the south by a jet that enters in the Gulf of Cádiz moving around the second eddy and eastward to feed the Atlantic inflow through the Strait of Gibraltar. [Copyright &y& Elsevier]