Human oriented solutions for intelligent analysis, multimedia and communication systems

In recent years many user-oriented and personalized computing technologies have been developed, in which users are immersed in a virtual world and surrounded by processing units. Such computing technologies require distributed signals to be collected, and perform intelligent analysis with data fusion depending on user preferences and the surrounding environment. In such human-oriented analysis, it is also necessary to consider different user preferences and even behavioral factors, that influence the final computing results. Development of user-oriented computing approaches are especially apparent in virtual reality and interactive technologies, multimedia, and decision-making systems, as well as user-oriented security protocols. Such human-oriented protocols allow the intelligent analysis of a great amount of information, perform analytics processes, extract meaning and manage systems in a secure manner. These subjects, as well as a number of others, such as personalized protocols for data analysis and security, computing approaches based on behavioral or perceptual factors, and bio-inspired technologies for knowledge extraction, will form the topics of this Special Issue on “Human oriented solutions for intelligent analysis, multimedia and communication systems” in the journal Concurrency and Computation: Practice and Experience. For this Special Issue eleven articles of particular interest were selected, which present the most interesting research activities and results within the subject matter of this special issue. The article “Towards human oriented solutions for deep semantic data analysis” by Ogiela and Snasel,1 presents novel solutions for efficient semantic analysis of data on the basis of cognitive reasoning and an assessment of marketing preferences registered in the course of the human perception process. Obtaining information from data on the basis of its interpreted meaning, with a view to determine individual preferences, makes it possible to designate the set of features on whose occurrence (or absence) attention is focused and those features whose occurrence has an impact on ‘interest’ within a given piece of information, product, service, and so forth. The approach presented is based on application of cognitive resonance processes implemented in cognitive information systems. The article entitled “A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme” by Park et al.2 presents studies on a method for processing analysis modules that can enrich result datasets with context information based on a data abstraction model. Data abstraction provides not only capabilities for context-aware systems and users to inspect the context at four levels from raw datasets to situational relationships, but also supports unified context levels for each entity that can be deployed at any location where systems deal with context to provide dedicated services. The article “Customer-oriented sales modeling strategy in a big data environment” by Chen et al.3 presents a new idea for a data mining technology application in business services. Different factors are analyzed, which may affect the profit of shopping malls in a big data environment and the most critical factors are found by data mining technology. This allows different sales promotion strategies to be provided for merchants to facilitate the expansion of sales. In management, small profits and quick returns is a popular sales strategy used by many shopping malls to increase turnover. The article “An online cognitive authentication and trust evaluation application programming interface for cognitive security gateway based on distributed massive Internet of Things network” by Chen et al.4 presents a new online cognitive authentication and trust evaluation API for CSG based on distributed massive IoT network. An online identity generation API is proposed, together with the modified EPC Class 1 Gen2 tag translator which is used to create both provider's as well as client's online identities. The article entitled “Dealing with Noise in Crowdsourced GPS Human Trajectory Logging Data” by Adhinugraha et al.5 presents new solutions for classifying the noise that might be found from public GPS traces. More than 5300 trajectories that started in the state of Victoria, Australia, were considered, and noise was classified into four types: spike noise, point noise, track noise, and logical noise. The authors tested the behavior of noise when processed with convex hull-based non-map-matching preprocessing methods to reduce spikes, followed by granularity reduction to reduce point density. In the article “An Effective Architecture of Digital Twin System to Support Human Decision Making and AI-Driven Autonomy” by Mostafa et al.6 a data analytic maturity model is presented, which consists of four phases with ordered activities. It shows that any data analytic project needs to be gradually developed from foundations to powerful AI algorithms. The effort and time spent on a routine will create an exponential increase in business value. The digital twin starts in phase two which immediately follows the event that the big data infrastructure is established. It is started by shallowly replicating the characteristics, features and states of its physical twin, and then dives deeper to copy its behaviors, which is achieved by AI technologies, typically machine learning models. The article “On the Undetectability of Payloads generated through Automatic Tools: a Human-oriented Approach” by Carpentieri et al.7 describes tools for the automatic generation of custom executable payloads. Such payloads typically enable to improve the interaction between the security experts and the asset under evaluation. However, due to the actions they take (i.e., remote access, privilege escalation, and so forth), these payloads are most likely classified as malicious by AVs, thus preventing their execution on a system. This article aims to provide the research community with an awareness of the possible security threats that can arise from automated tools commonly used in security assessment processes. The article entitled “QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm” by Dutta et al.8 presents an efficient QoS-aware big service composition model using a distributed co-evolutionary algorithm in Spark. In the proposed model the authors designed a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective algorithm to compare the solutions of NSGA-III. They also discuss the parallel implementation of distributed co-evolutionary algorithm that makes the algorithms faster and scalable to find the near-optimal solution. The article entitled “Probability and Topic-Based Data Transmission Protocol” by Saito et al.9 presents the MPSFC model to efficiently implement the IoT, where mobile fog nodes such as vehicles communicate with other nodes over wireless networks. Here, each fog node calculates output data on input data received from other fog nodes and forwards the output data to other fog nodes in the epidemic routing way. The authors proposed the TTLBDT and PTBDT protocols and compared them with the TBDT protocol. The article “Implementation and evaluation of the information flow control for the Internet of Things” by Nakamura et al.10 describes the OI protocol and evaluates the authorization process of the OI protocol in terms of the execution time. In the evaluation, the authors make clear the features of the execution time of authorization processes for GET, PUT, POST, and DELETE operations in the OI protocol. The OI protocol was implemented on a hybrid device realized in Raspberry Pi 3 Model B+. The article entitled “Chatbots: Security, Privacy, Data Protection and Social Aspects” by Hasal et al.11 presents all security aspects concerning communication with chatbots. It provides a review describing important steps in chatbot design techniques considering the chatbot security. It also defines possible security threats and vulnerabilities, and presents detailed methods which allow the development of a safe chatbot platform. We believe that the articles included in this Special Issue will have a great impact for future scientific research, and also contribute to the studies conducted by other researchers and practitioners, who work in the area of advanced information processing systems and computer security. We would like to express my sincere appreciation of the valuable contributions made by all authors. We'd like also to express our special thanks to Professor David W. Walker from the School of Computer Science and Informatics, Cardiff University, UK, Editor-in-Chief of Concurrency and Computation: Practice and Experience, for allowing the publication of this Special Issue, and for his great support throughout the entire publication process. "Data sharing not applicable to this article as no datasets were generated or analysed during the current study"