Persuasive system features have been widely adopted to encourage attitude and behaviour change. Recently, most social networking sites (SNS) adopt some form of persuasive system features that leverage social influence to deliberately induce prescribed behaviours in their users. However, studies on how these features can be used to promote knowledge sharing are inadequate; particularly, regarding how SNS that have been developed solely for academic purposes can adopt these features to promote knowledge sharing. To address this knowledge gap, this study integrates constructs from the social capital theory and persuasive systems design model to investigate the impact of persuasive social features on knowledge sharing among students of tertiary institutions on academic social networking sites. Data are quantitatively gathered from 218 respondents from tertiary institutions and statistically analyzed. The results suggest that perceived dialogue support and perceived social support have strong influences on knowledge sharing behaviour.

In recent years, there has been keen interest in the area of Internet of Things connected underground, and with this is the need to fully understand and characterize their operating environment. In this paper, a model, based on the Peplinski principle, for the propagation of waves in soils that takes into account losses attributable to the presence of local inhomogeneity is proposed. In the work, it is assumed that the inhomogeneities are obstacles such as stones or pebbles, of moderate size, all identical and randomly distributed in space. A new wave number is obtained through a combination of the multiple scattering theory and the Peplinski principle. Since the latter principle considers the propagation in a homogeneous medium (without obstacles), the wave number it provides is inserted into the one resulting from the former, the multiple scattering theory. The effective wave number thus obtained is compared numerically with that of Peplinski alone on the one hand and with that of multiple scattering alone on the other hand. The phase velocity and the loss tangent are analyzed against the particle concentration at the low-frequency Rayleigh limit condition ( k a ≲ 0.1 ) and against the frequency at two particle concentrations ( c = 0.2 and c = 0.4 ), two particle radii ( a = 0.55  cm and a = 1.10  cm), and 5% and 50% volumetric water content of the soil. Path losses are also compared to each other to examine the effects on transmission of soil containing obstacles. The results obtained suggest that the proposed model has better accuracy in estimating the wave number than previously used schemes.

Due to the sensitivity and amount of information stored on mobile devices, the need to protect these devices from unauthorized access has become imperative. Among the various mechanisms to manage access on mobile devices, this chapter focused on identifying research trends on biometric authentication schemes. The systematic literature review approach was adopted to guide future researches in the subject area. Consequently, seventeen selected articles from journals in three databases (IEEE, ACM digital library, and SpringerLink) were reviewed. Findings from the reviewed articles indicated that touch gestures are the predominant authentication technique used in mobile devices, particularly in android devices. Furthermore, mimic attacks were identified as the commonest attacks on biometric authentic schemes. While, robust authentication techniques such as dental occlusion, ECG (electrocardiogram), palmprints and knuckles were identified as newly implemented authentication techniques in mobile devices.

There is a long-held belief that deterrence mechanisms are more useful in developing countries. Evidence on this belief is anecdotal rather than empirical. In this chapter, individual compliance to information system security policy (ISSP) is examined through the lenses of deterrence theory. The effects of certainty of detection and severity of punishment on attitude towards compliance and also ISSP compliance behaviour are investigated. A survey questionnaire was distributed to gather responses from 432 individuals who are staff of a public university in Ghana. The data was analysed using partial least square structural equation modelling (PLS-SEM). The results indicate that severity of punishment has a positive effect on attitude towards compliance and ISSP compliance behaviour. However, certainty of detection neither affected attitude towards compliance nor ISSP compliance behaviour. It is recommended that organizations enhance the severity of sanctions imposed on those who violate ISSPs. Future studies should explore how users apply neutralization techniques to evade sanctions.

This study proposes a sound classification model for natural disasters. Deep learning techniques, a convolutional neural network (CNN) and long short-term memory (LSTM), were used to train two individual classifiers. The study was conducted using a dataset acquired online1 and truncated at 0.1 s to obtain a total of 12 937 sound segments. The result indicated that acoustic signals are effective for classifying natural disasters using machine learning techniques. The classifiers serve as an alternative effective approach to disaster classification. The CNN model obtained a classification accuracy of 99.96%, whereas the LSTM obtained an accuracy of 99.90%. The misclassification rates obtained in this study for the CNN and LSTM classifiers (i.e., 0.4% and 0.1%, respectively) suggest less classification errors when compared to existing studies. Future studies may investigate how to implement such classifiers for the early detection of natural disasters in real time.