The shift to 6G communications: vision and requirements

The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.

An efficient attribute-based hierarchical data access control scheme in cloud computing

Security issues in cloud computing have become a hot topic in academia and industry, and CP-ABE is an effective solution for managing and protecting data. When data is shared in cloud computing, they usually have multiple access structures that have hierarchical relationships. However, existing CP-ABE algorithms do not consider such relationships and just require data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To achieve fine-grained access control of multiple hierarchical files effectively, first we propose an efficient hierarchical CP-ABE algorithm whose access structure is linear secret sharing scheme. Moreover, we construct an attribute-based hierarchical access control scheme, namely AHAC. In our scheme, when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. The experiments show that AHAC has good security and high performance. Furthermore, when the quantity of encrypted data files increases, the superiority of AHAC will be more significant.

Social Internet of Things: vision, challenges, and trends

IoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.

A model-driven approach to ensure trust in the IoT

The Internet of Things (IoT) is a paradigm that permits smart entities to be interconnected anywhere and anyhow. IoT opens new opportunities but also rises new issues. In this dynamic environment, trust is useful to mitigate these issues. In fact, it is important that the smart entities could know and trust the other smart entities in order to collaborate with them. So far, there is a lack of research when considering trust through the whole System Development Life Cycle (SDLC) of a smart IoT entity. In this paper, we suggest a new approach that considers trust not only at the end of the SDLC but also at the start of it. More precisely, we explore the modeling phase proposing a model-driven approach extending UML and SysML considering trust and its related domains, such as security and privacy. We propose stereotypes for each diagram in order to give developers a way to represent trust elements in an effective way. Moreover, we propose two new diagrams that are very important for the IoT: a traceability diagram and a context diagram. This model-driven approach will help developers to model the smart IoT entities according to the requirements elicited in the previous phases of the SDLC. These models will be a fundamental input for the following and final phases of the SDLC.

The effect of eye movements and cultural factors on product color selection

A color is a powerful tool used to attract people’s attention and to entice them to purchase a product. However, the way in which a specific color influences people’s color selection and the role of their eye movements and cultural factors in this process remain unknown. In this study, to delve into this problem, we designed an experiment to determine the influence of specific colors on people’s product preferences by using an eye-tracking device, intending to identify the role of their eye movements and cultural factors. Based on the experimental data, a detailed influence path model was built to describe the effect of specific colors on product evaluations by an integrated moderation and mediation analysis. Our findings show that in the influence process, the effects of specific colors on product evaluations are mediated by eye movements. Additionally, cultural factors partly moderate the process as an influencing factor. The research findings from this study have important implications for user-centered product design and visual marketing management.

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visualizations. This research investigates the use of co-located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non-Shared) and how being placed at various position arrangements (Corner-to-Corner, Face-to-Face, and Side-by-Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 participants divided into 6 groups (2 modes $$\times$$ × 3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side-by-Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them.

Medical image processing with contextual style transfer

With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

Facial UV map completion for pose-invariant face recognition: a novel adversarial approach based on coupled attention residual UNets

Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.

Newspaper article-based agent control in smart city simulations

The latest research on smart city technologies mainly focuses on utilizing cities’ resources to improve the quality of the lives of citizens. Diverse kinds of control signals from massive systems and devices such as adaptive traffic light systems in smart cities can be collected and utilized. Unfortunately, it is difficult to collect a massive dataset of control signals as doing so in the real-world requires significant effort and time. This paper proposes a deep generative model which integrates a long short-term memory model with generative adversarial network (LSTM-GAN) to generate agent control signals based on the words extracted from newspaper articles to solve the problem of collecting massive signals. The discriminatory network in the LSTM-GAN takes continuous word embedding vectors as inputs generated by a pre-trained Word2Vec model. The agent control signals of sequential actions are simultaneously predicted by the LSTM-GAN in real time. Specifically, to collect the training data of smart city simulations, the LSTM-GAN is trained based on the Corpus of Contemporary American English (COCA) newspaper dataset, which contains 5,317,731 sentences, for a total of 93,626,203 word tokens, from written texts. To verify the proposed method, agent control signals were generated and validated. In the training of the LSTM-GAN, the accuracy of the discriminator converged to 50%. In addition, the losses of the discriminator and the generator converged from 4527.04 and 4527.94 to 2.97 and 1.87, respectively.