LAUSR

As the famous slogan ‘‘Connecting People’’ indicates, a lot of the developments in novel technologies intensify the relationship between people without necessarily enhancing technologies that are close to the nature of human beings. Examples can be easily found in recent computing paradigms, such as Cloud Computing that advances network infrastructure for data storage and resource sharing, or the Internet of Things that investigates the intelligence and awareness of objects involved in the network. From this point of view, the existing state-of-the-art solutions, in realms of artificial intelligence and/or computational intelligence, significantly differ from the human competence level in many research domains. Even though it is generally not clear whether human-like approach would show its upper-hand over existing methods, the exploration of this research path seems to be advantageous and challenging. Human-like intelligence is arguably one of the most powerful approaches for the optimization and design of complex Web, as well as its correlated issues so as to meet the requirements and growing needs of new challenging applications (and services as well). Although the state of the art behind the computational intelligence has already reached an impressive scale, there remain quite a lot of attractive research topics, with consideration of human-like or human-oriented factors, for further and promising developments. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues. Totally, 28 papers are suggested to EiC for acceptance from 44 manuscript submissions. The selected papers are summarized as follows. Zhang et al. [1] proposed an adaptive congestion control protocol (ACCP) which is divided into two phases to control network congestion before affecting network performance. Hou et al. [2] presented a methodology for identifying low-carbon travel block, which can be used to identify the built environment conducive to residents’ lowcarbon travel. Cui et al. [3] introduced the process of material deterioration of existing reinforced concrete structures and evaluated its effects on their seismic performance by proposing the three-dimensional fragility curve based on hybrid sensing method. Cui and Li [4] represent the probabilities of the basic events by functions. The variables of the function are n influencing factors on the basic events. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is proposed by Hu et al. [5] to solve the inversion problem of prestack seismic big data. Song et al. [6] proposed a method named multiple-order semantic relation extraction (MOSRE), which applies for multiple orders, a conceptual expression used in formal logistics, to build semantic patterns for extracting information from hybrid unstructured texts in the open domain with deep semantic analyses. Xie and Peng [7] applied recently emerged ensemble learning methods to predict the burned area of forest fires and the occurrence of large-scale forest fires using the forest fire dataset from the University of California, Irvine machine learning repository collected from the northeastern region of Portugal. Song et al. [8] used some natural language processing ways, such as word embedding and combining semantic, to let the machine realize the content of supervision video and then focus on the civil engineering supervision video retrieval annotated by supervision engineer. Mou et al. [9] investigated the single-machine inverse scheduling problem with adjusted & Zheng Xu [email protected]