Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.

Successful property management, which can be done by individual owners or by professional property managers, has numerous interrelated complex business processes: tenant attraction, screening, leasing, tenant move-in, property maintenance, retention/lease renewals, tenant move-out, and reputation management. Property managers must be highly skilled communicators and thoroughly understand the legal implications of housing and contract law in their operating environments. The lack of affordable solutions available to small and mid-sized property managers drives their use of fragmented technology solutions provided by numerous technology vendors that employ redundant data-gathering methods, putting them at a competitive disadvantage. This research paper explores the information systems needs of property managers in contrast to the solutions available to them in the marketplace.

Blockchain is being promoted as the platform to disrupt business as usual in many transaction-heavy sectors. Questions remain about the future standards of blockchain, their feature set, functionality, and the willingness of organisations to disrupt their existing revenue streams via blockchain. The most significant near-term promise that affords industry is the cost and complexity savings via the standardization of their technology infrastructure stacks. This paper explores the benefits available of reduced costs and complexity via the adoption of blockchain.

Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.

A possibility of speeding up the job scheduling by a heuristic based on the shortest processing period approach is studied in the paper. The scheduling problem is such that the job volume and job priority weight are increasing as the job release date increases. Job preemptions are allowed. Within this model, the input for the heuristic is formed by either ascending or descending job order. Therefore, an estimator of relative difference in duration of finding an approximate schedule by these job orders is designed. It is ascertained that the job order results in different time of computations when scheduling at least a few hundred jobs. The ascending-order solving becomes on average by 1 % to 2.5 % faster when job volumes increase steeply. As the steepness of job volumes decreases, this gain vanishes and, eventually, the descending-order solving becomes on average faster by up to 4 %. The gain trends of both job orders slowly increase as the number of jobs increases.

The evidence theory is ascribed to a specific kind of uncertainty. In this theory, uncertainty refers to the fact that the element of our interest (the true world) may be included in subsets of other similar elements (possible worlds). In the original evidence theory, the estimates of the basic probability masses for the focal elements are given in an unambiguous form. In practice, to obtain such estimates is often difficult or even impossible. In such a situation, the relevant estimates are given in the interval or fuzzy form. The goal of the paper is to present and analyse the calculation procedures for determination of the belief functions and plausibility functions in the evidence theory for cases when the initial estimates are given in the interval or fuzzy form.

United States based companies have invested more heavily in cloud services than companies in other nations. Despite numerous benefits claimed by cloud service providers, many organisations are still uncertain about the implementation of cloud computing. The aim of this study is to gain a better understanding of the factors that are important to the usage adoption decision of this information technology delivery model and the implications for organisations in the United States. Decision makers were surveyed to discover which attributes were important in their determination of cloud computing utilisation. Using a sample of executives from manufacturing and service sectors in the United States, multiple factors contributing to the recent growth in cloud computing have been identified using a factor analysis. Four factors emerged from the data analysis include extrinsic motivation, intrinsic motivation, perceived risks, and resource constraint.

During the past years, a rapid growth has been seen in the descriptive approaches to decision choice. As opposed to normative expected utility theory, these approaches are based on the subjective perception of probabilities by the individuals, which takes place in real situations of risky choice. The modelling of this kind of perceptions is made on the basis of probability weighting functions. In cumulative prospect theory, which is the focus of this paper, decision prospect outcome weights are calculated using the obtained probability weights. If the value functions are constructed in the sets of positive and negative outcomes, then, based on the outcome value evaluations and outcome decision weights, generalised evaluations of prospect value are calculated, which are the basis for choosing an optimal prospect.

A problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations for every player. A form of expert estimations is suggested. The form is of binary type, wherein the expert’s judgment is either 1 or 0. This type is the easiest to be implemented in social networks. For most social networks, 1 can be a “like” (the currently evaluated situation is advantageous), and 0 is a “dislike” (disadvantageous). A method of processing expert estimations is substantiated. Two requirements are provided for obtaining disambiguous payoff averages along with the clustered payoff matrices.

Nowadays the Internet of Things (IoT) is one of the
\nmost trending technologies. It is expected that by year 2020 there
\nwill be 50 billion of Internet-connected devices. Fields like smart
\ncities and smart homes largely rely on IoT phenomena by using a
\nwide variety of sensors for data collection, analysis and
\ncorresponding actions. The paper describes how this trending and
\nrelatively new technology is applied at Riga Technical University
\nfor educational purposes.