A concept of biomorphic neuroprocessor that implements hardware spiking neural network for traditional tasks of information processing and can simulate operation of brain cortical column or its fragment is proposed. The key units of hardware neural network are memory and logic matrices, previously developed on the basis of composite memristor-diode crossbar. These matrices provide high element integration and energy efficiency compared to known neuroprocessors and individual matrices. Such efficiency is achieved by application of mixed analogdigital computations, including those that use memristors integrated in composite memristor-diode crossbars. Neuron electrical model was constructed on the basis of these matrices and the Hodgkin-Huxley biomorphic model for neuron membrane. Unlike existing neural networks with synapses based on discrete memristors, the generation of new association was demonstrated in memristor-diode crossbar by SPICE modeling of associative self-learning processes. The adaptation procedure for biomorphic neural network program to neuroprocessor hardware is defined. In essence, presented neuroprocessor is a prototype of new generation computers with artificial intelligence.
Keywords: Biomorphic neuroprocessor | Biomorphic software and hardware electrical | models of neuron | Composite memristor-diode crossbar | Memory matrix | Logic matrix | Associative self-learning

Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism

Condorcet consistent rules were originally developed for preference aggregation in the theory of social choice. Nowadays these rules are applied in a variety of fields such as discrete multi-criteria analysis, defence and security decision support, composite indicators, machine learning, artificial intelligence, queries in databases or internet multiple search engines and theoretical computer science. The cycle issue, known also as Condorcets paradox, is the most serious problem inherent in this type of rules. Solutions for dealing with the cycle issue properly already exist in the literature; the most important one being the identification of the median ranking, often called the Kemeny ranking. Unfortunately its identification is a NP-hard problem. This article has three main objectives: (1) to clarify that the Kemeny median order has to be framed in the context of Condorcet consistent rules; this is important since in the current practice sometimes even the Borda count is used as a proxy for the Kemeny ranking. (2) To present a new exact algorithm, this identifies the Kemeny median ranking by providing a searching time guarantee. (3) To present a new heuristic algorithm identifying the Kemeny median ranking with an optimal trade-off between convergence and approximation .
Keywords : Decision analysis | Combinatorial optimisation | Social choice| Multiple criteria | Artificial intelligence| Defence and security| Big data

One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinsons disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.
Keywords: Quantitative magnetic resonance imaging | Susceptibility weighted imaging | T1 mapping | Quantitative susceptibility mapping | Multi-parametric magnetic resonance imaging | Strategically acquired gradient echo

Although building information modelling (BIM) has revolutionized building design and construction management, it is still time-consuming for a BIM project team to coordinate with designers to resolve clashes during the pre-construction stages. During the construction stage, shop drawings frequently have to be revised because of cognitive differences between the designers and constructors. These two groups of people view the resolution of such clashes from their own perspectives because of differences in their inherent knowledge and experience. To effectively improve the project delivery time, one option is to reduce the number of model revisions during the construction stage. This could be done by providing a BIM model as a reference. In this model, design clashes can be resolved from the perspective of the constructor before discussions in design coordination meetings to find compromises. In this work, an artificial intelligence system for such design clash resolution was developed with machine learning and heuristic optimizing techniques. In the experiment, we present a real case of a student residence, in which the mechanical, electrical, and plumbing systems in the basement are used to validate the effectiveness of the proposed system. The experimental results show the feasibility and effectiveness of the proposed system.
Keywords: Building information modelling | Design clash resolution | Knowledge extraction | Heuristic optimization