Effects of Automated Messages on Internet Users Attempting to Access "Barely Legal" Pornography.

With the increasing number of individuals accessing online child sexual exploitation material (CSEM), there is an urgent need for primary prevention strategies to supplement the traditional focus on arrest and prosecution. We examined whether online warning messages would dissuade individuals from visiting a honeypot website purporting to contain barely legal pornography. Participants (n = 419) seeking the site were randomly assigned to one of five conditions; they went straight to the landing page (control; n = 100) or encountered a warning message advising of the potential harm to viewers (n = 74), potential harm to victims (n = 65), ability of police to track IP addresses (n = 81), or possible illegality of such pornography (n = 99). We measured the attempted click-through to the site. Attrition rates for the warning message conditions were 38% to 52%, compared with 27% for the control group. The most effective messages were those that warned that IP addresses can be traced (odds ratio [OR] = 2.64) and that the pornography may be illegal (OR = 2.99). We argue that warning messages offer a valuable and cost-effective strategy that can be scaled up to help reduce the accessing of CSEM online.

Security and Blockchain Convergence with Internet of Multimedia Things: Current Trends, Research Challenges and Future Directions.

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications.

A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS.

Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues.

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.

Accessible virtual reality therapy using portable media devices.

Simulated immersive environments displayed on large screens are a valuable therapeutic asset in the treatment of a range of psychological disorders. Permanent environments are expensive to build and maintain, require specialized clinician training and technical support and often have limited accessibility for clients. Ideally, virtual reality exposure therapy (VRET) could be accessible to the broader community if we could use inexpensive hardware with specifically designed software. This study tested whether watching a handheld non-immersive media device causes nausea and other cybersickness responses. Using a repeated measure design we found that nausea, general discomfort, eyestrain, blurred vision and an increase in salivation significantly increased in response to handheld non-immersive media device exposure.

Cybersickness and Anxiety During Simulated Motion: Implications for VRET.

Some clinicians have suggested using virtual reality environments to deliver psychological interventions to treat anxiety disorders. However, given a significant body of work on cybersickness symptoms which may arise in virtual environments - especially those involving simulated motion - we tested (a) whether being exposed to a virtual reality environment alone causes anxiety to increase, and (b) whether exposure to simulated motion in a virtual reality environment increases anxiety. Using a repeated measures design, we used Kim's Anxiety Scale questionnaire to compare baseline anxiety, anxiety after virtual environment exposure, and anxiety after simulated motion. While there was no significant effect on anxiety for being in a virtual environment with no simulated motion, the introduction of simulated motion caused anxiety to significantly increase, but not to a severe or extreme level. The implications of this work for virtual reality exposure therapy (VRET) are discussed.

Are sparse-coding simple cell receptive field models physiologically plausible?

Olshausen and Field (1996) developed a simple cell receptive field model for natural scene processing in V1, based on unsupervised learning and non-orthogonal basis function optimization of an overcomplete representation of visual space. The model was originally tested with an ensemble of whitened natural scenes, simulating pre-cortical filtering in the retinal ganglia and lateral geniculate nucleus, and the basis functions qualitatively resembled the orientation-specific responses of V1 simple cells in the spatial domain. In this study, the quantitative tuning responses of the basis functions in the spectral domain are estimated using a Gaussian model, to determine their goodness-of-fit to the known bandwidths of simple cells in primate V1. Five simulation experiments which examined key features of the model are reported: changing the size of the basis functions; using a complete versus over-complete representation; changing the sparseness factor; using a variable learning rate; and mapping the basis functions with a whitening spatial function. The key finding of this study is that across all image themes, basis function sizes, number of basis functions, sparseness factors and learning rates, the spatial-frequency tuning did not closely resemble that of primate area 17 -- the model results more closely resembled the unclassified cat neurones of area 19 with a single exception, and not area 17 as predicted.

Selecting parameters for phase space reconstruction of the electrocorticogram (ECoG).

The selection of parameters for phase space reconstruction of empirically observed data has been a source of criticism when estimating the correlation dimension (D2) from observed data rather than from the solution of differential equations, when analyzing noisy and potentially non-stationary signals, such as the electroencephalogram (EEG). The largely arbitrary selection of the time-delay reconstruction (T) of temporal dynamics, and for the embedding (M) of these series, has been widely criticized. This study adopted an analytic and statistical framework within which the scaling behavior of D2 with respect to T and M, could be examined over five data lengths (N = 4096, 8192, 12288, 16384, and 20480) over an 8 x 8 grid of cat EEG. It was found that D2 was invariant over all data lengths only within a very narrow T range (T = 10-16) for M = 4. A statistically significant T by M interaction was found using multiple analysis of variance, with D2 being highly correlated over T as a function of M. Finally, an examination of phase-randomized surrogates indicated that statistically significant differences existed between EEG and phase-randomized surrogates over all data lengths, with time delays (T = 10-16), indicating that the D2 for EEG is phase-dependent when it is invariant with respect to data length. The implications of these findings are discussed with respect to current models of ECoG generation, and their implication with respect to the integration in the brain.

A method for estimating long-range power law correlations from the electroencephalogram.

Recent research has found long-range electroencephalogram (EEG) power law correlations, indicating time scale invariance. However, the EEG is also rather noisy, displaying short-term decorrelation like white noise--i.e., what is scale invariant at one time period may disappear in the next. The paradoxical combination of short-range divergence, but long-range correlations, suggests that any long-range correlations detected in one sample may be spurious, since they could be related to amplitude fluctuations. To overcome this problem, this paper suggests a new technique for analysing EEG signals segmented by zero-crossings, using detrended fluctuation analysis (DFA), evaluated across two time periods (TIME) and different sites (SITE). A mean scaling exponent across all subjects and sites of alpha = 0.67 was observed. MANOVA analysis indicates no significant main effect for TIME or interaction with SITE, suggesting that the zero-crossing method may be successful in determining the fractal nature of EEG dynamics across relatively long time scales.

Estimating distributed coding efficiency in orthogonal models of facial processing.

Orthogonal facial processing models attempt to mimic the local decomposition performed in the visual cortex by simple cell receptive fields. The purpose of this study was to investigate how the neurophysiological validity of orthogonal models of facial processing could be improved by implementing a "whitening" filter, based on current knowledge of similar filtering that occurs in the retina. By using a metric known as the "distributed coding efficiency index" (DCE), this study demonstrates that an orthogonal facial processing model significantly increased coding efficiency when a low-pass, zero-phase whitening filter was applied. The extent to which orthogonal decomposition of filtered data represents a realistic V1 model is discussed.

Competition, inhibition, and semantic judgment errors in Parkinson's disease.

Semantic processing errors are symptoms of an up-regulation (schizophrenia) or degradation (Parkinsonism) of dopaminergic pathways. A recent connectionist model attributed errors in the schizophrenic processing of context to increased gain in competitive neural processes. This study extends this "gain hypothesis" by comparing the sensitivity to reduced gain of a simulation of semantic route activation to characteristic semantic judgment errors made by Parkinson's patients in an open search task. Under normal gain conditions, the dominant sense of polysemous words "wins" through competition and lateral inhibition at the word sense level (beta(inh)). For words with very different sense frequencies, decreasing gain by increasing beta(inh) resulted in the dominant word sense winning; however, for words with similar sense frequencies, increasing beta(inh) resulted in the dominant word sense winning only for low to moderate values. At high levels, no clear winner emerged after 200 epochs, with the least dominant sense reaching the maximum activation value. These results are discussed in the context of the Yerkes-Dodson Law, which may provide a theoretical basis for understanding normal and impaired semantic performance in catecholaminergic disorders.