Abstract A massive amount of information explosion over the internet has caused a possible difficulty of information overload. To overcome this, Recommender systems are systematic tools that are rapidly being employed in several domains such as movies, travel, E-commerce, and music. In the existing research, several methods have been proposed for single-user modeling, however, the massive rise of social connections potentially increases the significance of group recommender systems (GRS). A GRS is one that jointly recommends a list of items to a collection of individuals based on their interests. Moreover, the single-user model poses several challenges to recommender systems such as data sparsity, cold start, and long tail problems. On the contrary hand, another hotspot for group-based recommendation is the modeling of user preferences and interests based on the groups to which they belong using effective aggregation strategies. To address such issues, a novel “KGR” group recommender system based on user-trust relations is proposed in this study using kernel mapping techniques. In the proposed model, user-trust networks or relations are exploited to generate trust-based groups of users which is one of the important behavioral and social aspects. More precisely, in KGR the group kernels and group residual matrices are exploited as well as seeking a multi-linear mapping between encoded vectors of group-item interactions and probability density function indicating how groups will rate the items. Moreover, to emphasize the relevance of individual preferences of users in a group to which they belong, a hybrid approach is also suggested in which group kernels and individual user kernels are merged as additive and multiplicative models. Furthermore, the proposed KGR is validated on two different trust-based datasets including Film Trust and CiaoDVD. In addition, KGR outperforms with an RMSE value of 0.3306 and 0.3013 on FilmTrust and CiaoDVD datasets which are lower than the 1.8176 and 1.1092 observed with the original KMR.

Real-time surveillance systems have become a necessity of today's life owing to their relevance in the contemporary era for security reasons to ensure a secure and safe environment. Presently, Person re-identification (Re-ID)-based surveillance systems are becoming increasingly more prevalent and sophisticated since they do not require human intervention and are more reliable to deploy in public spaces leveraging multi-camera networks. However, one of the major problems in Person ReID is the visual appearance i-e the appearance of a person in an image is greatly affected by different camera views. As a result, the discriminative set of features must be learned in a deep learning model in order to re-identify persons from opposing camera viewpoints. To address this challenge, we propose an image/text-retrieval-based Person ReId method in which both visual and text-based features are exploited to carry out person re-identification. More precisely, the textual descriptions of the images are taken into account as text features with Glove Word Embedding followed by 1D-MAPCNN and fused with image-level features extracted using the GoogLeNet model. In addition, the feature discriminability is enhanced using local importance-based pooling (LIP) layers in which adaptive significance weights are learned during downsampling. Moreover, from two different modalities, feature refinement is done during training with the help of attention mechanisms using the Convolutional Block Attention module (CBAM) and the proposed shared attention neural network. It is observed that LIP layers along with both vision and textual features are playing a key role in acquiring discriminative features even if the visual appearance of the same person is greatly affected due to camera pose conditions. The proposed method is validated on the CUHK-PADES dataset and has 15.34% and 24.39% rank-1 improvement in text and image-based retrievals.

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.