A short survey on modern virtual environments that utilize AI and synthetic data

Within a rather abstract computational framework Artificial Intelligence (AI) may be defined as intelligence exhibited by machines. In computer science, though, the field of AI research defines itself as the study of “intelligent agents.” In this context, interaction with popular virtual environments, as for instance in virtual game playing, has gained a lot of focus recently in the sense that it provides innovative aspects of AI perception that did not occur to researchers until now. Such aspects are typically formed by the computational intelligent behavior captured through interaction with the virtual environment, as well as the study of graphic models and biologically inspired learning techniques, like, for instance, evolutionary computation, neural networks, and reinforcement learning. In this short survey paper, we attempt to provide an overview of the most recent research works on such novel, yet quite interesting, research domains. We feel that this topic forms an attractive candidate for fellow researchers that came into sight over the last years. Thus, we initiate our study by presenting a brief overview of our motivation and continue with some basic information on recent virtual graphic models utilization and the state-of-the-art on virtual environments, which constitutes two clearly identifiable components of the herein attempted summarization. We then continue, by briefly reviewing the interesting video games territory, and by discerning and discriminating its useful types, thus envisioning possible further utilization scenarios for the collected information. A short discussion on the identified trends and a couple of future research directions conclude the paper

Transform-based graph topology similarity metrics

Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and supply chains, social media, computational aesthetics, and transportation networks. In this paradigm, signal processing methodologies are applied to the adjacency matrix, seen as a two-dimensional signal. Fundamental operations of this type include graph sampling, the graph Laplace transform, and graph spectrum estimation. In this context, topology similarity metrics allow meaningful and efficient comparisons between pairs of graphs or along evolving graph sequences. In turn, such metrics can be the algorithmic cornerstone of graph clustering schemes. Major advantages of relying on existing signal processing kernels include parallelism, scalability, and numerical stability. This work presents a scheme for training a tensor stack network to estimate the topological correlation coefficient between two graph adjacency matrices compressed with the two-dimensional discrete cosine transform, augmenting thus the indirect decompression with knowledge stored in the network. The results from three benchmark graph sequences are encouraging in terms of mean square error and complexity especially for graph sequences. An additional key point is the independence of the proposed method from the underlying domain semantics. This is primarily achieved by focusing on higher-order structural graph patterns

Approximate High Dimensional Graph Mining With Matrix Polar Factorization: A Twitter Application

At the dawn of the Internet era graph analytics play an important role in high- and low-level network policymaking across a wide array of fields so diverse as transportation network design, supply chain engineering and logistics, social media analysis, and computer communication networks, to name just a few. This can be attributed not only to the size of the original graph but also to the nature of the problem parameters. For instance, algorithmic solutions depend heavily on the approximation criterion selection. Moreover, iterative or heuristic solutions are often sought as it is a high dimensional problem given the high number of vertices and edges involved as well as their complex interaction. Replacing under constraints a directed graph with an undirected one having the same vertex set is often sought in applications such as data visualization, community structure discovery, and connection-based vertex centrality metrics. Polar decomposition is a key matrix factorization which represents a matrix as a product of a symmetric positive (semi)definite factor and an orthogonal one. The former can be an undirected approximation of the original adjacency matrix. The proposed graph approximation has been tested with three Twitter graphs with encouraging results with respect to density, Fiedler number, and certain vertex centrality metrics based on matrix power series. The dataset was hosted in an online MongoDB instance

Simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake

Consensus protocols constitute an important part in virtually any blockchain stack as they safeguard transaction validity and uniqueness. This task is achieved in a distributed manner by delegating it to certain nodes which, depending on the protocol, may further utilize the computational resources of other nodes. As a tangible incentive for nodes to verify transactions many protocols contain special reward mechanisms. They are typically inducement prizes aiming at increasing node engagement towards blockchain stability. This work presents the fundamentals of a probabilistic blockchain simulation tool for studying large transaction volumes over time. Two consensus protocols, the proof of work and the delegate proof of stake, are compared on the basis of the reward distribution and the probability bound of the reward exceeding its expected value. Also, the reward probability as a function of the network distance from the node initiating the transaction is studied

Higher Order Trust Ranking of LinkedIn Accounts with Iterative Matrix Methods

Trust is a fundamental sociotechnological mainstay of the Web today. There is substantial evidence about this since netizens implicitly or explicitly agree to trust virtually every Web service they use ranging from Web-based mail to e-commerce portals. Moreover the methodological framework for trusting individual netizens, primarily their identity and communications, has considerably progressed. Nevertheless, the core of fact checking for human generated content is still far from being substantially automated as most proposed smart algorithms capture inadequately fundamental human traits. One such case is the evaluation of the profile trustworthiness of LinkedIn members based on publicly available attributes available from the platform itself. A trusted profile may indirectly indicate a more suitable candidate since its contents can be easily verified. In this article a first order graph search mechanism for discovering LinkedIn trusted profiles based on a random walker is extended to higher order ranking based on a combination of functional and connectivity patterns. Results are derived for the same benchmark dataset and the first- and higher-order approaches are compared in terms of accuracy

Building Trusted Startup Teams from LinkedIn Attributes: A Higher Order Probabilistic Analysis

© 2020 IEEE. Startups arguably contribute to the current business landscape by developing innovative products and services. The discovery of business partners and employees with a specific background which can be verified stands out repeatedly as a prime obstacle. LinkedIn is a popular platform where professional milestones, endorsements, recommendations, and skills are posted. A graph search algorithm with a BFS and a DFS strategy for seeking trusted candidates in LinkedIn is proposed. Both strategies rely on a metric for assessing the trustworthiness of an account according to LinkedIn attributes. Also, a stochastic vertex selection mechanism reminiscent of preferential attachment guides search. Both strategies were verified against a large segment of the vivid startup ecosystem of Patras, Hellas. A higher order probabilistic analysis suggests that BFS is more suitable. Findings also imply that emphasis should be given to local networking events, peer interaction, and to tasks allowing verifiable credit for the respective work