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Sign languages are the main way for people from deaf community to communicate with other people. In this paper, we have compared several real-time sign language dactyl recognition systems using deep convolutional neural networks. Our system is able to recognize words from natural language gestured using signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. For ASL, we trained on dataset prepared by Massey University, Institute of Information and Mathematical Sciences, for RSL we collect our own dataset, which we aim to enlarge together with RSL community in Russia. The results showed 100% accuracy for ASL Massey dataset, while RSL recognition quality is behind sufficient quality due to much more complex nature of real-world RSL dataset.
It has recently been shown that first-order- and datalog-rewritability of ontology-mediated queries (OMQs) with expressive ontologies can be checked in NExpTime using a reduction to CSPs. In this paper, we present a case study for OMQs with Boolean conjunctive queries and a fixed ontology consisting of a single covering axiom 𝐴 -> 𝐹 v 𝑇, A -> F v T, possibly supplemented with a disjointness axiom for T and F. The ultimate aim is to classify such OMQs according to their data complexity: AC0, L, NL, P or coNP. We report on our experience with trying to distinguish between OMQs in P and coNP using the reduction to CSPs and the Polyanna software for finding polymorphisms.
A graph database is a digraph whose arcs are labelled with symbols from a fixed alphabet. A regular graph pattern (RGP) is a digraph whose edges are labelled with regular expressions over the alphabet. RGPs model navigational queries for graph databases, more precisely, conjunctive regular path queries. A match of a navigational RGP query in the database is witnessed by a special navigational homomorphism of the RGP to the database. We study the complexity of deciding the existence of a homomorphism between two RGPs. Such homomorphisms model a strong type of containment between two navigational RGP queries. We show that this problem can be solved by an EXPTIME algorithm (while general query containment in this context is EXPSPACE-complete). We also study the problem for restricted RGPs over a unary alphabet, that arise from some applications like XPath, and prove that certain interesting cases are polynomial-time solvable.
An increasing number of algorithms in deep reinforcement learning area creates new challenges for environments, particularly, for their comprehensive analysis and searching application areas. The key purpose of this article is to provide an extensible environment for researches. We consider a Match-3 game, which has simple gameplay, but challenging game design for engaging players. The article provides metrics for evaluation of agents and corresponding baselines in different scenarios.
In this work, we study the effect of combining existent improvements for Deep Q-Networks (DQN) in Markov Decision Processes (MDP) and Partially Observable MDP (POMDP) settings. Combinations of several heuristics, such as Distributional Learning and Dueling architectures improvements, for MDP are well-studied. We propose a new combination method of simple DQN extensions and develop a new model-free reinforcement learning agent, which works with POMDP and uses well-studied improvements from fully observable MDP. To test our agent we choose the VizDoom environment, which is old first person shooter, and the Health Gathering scenario. We prove that improvements used in MDP setting may be used in POMDP setting as well and our combined agents can converge to better policies. We develop an agent with combination of several improvements showing superior game performance in practice. We compare our agent with Recurrent DQN using Prioritized Experience Replay and Snaphot Ensembling agent and get approximately triple increase in per episode reward.
We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.
Depth map super-resolution is a challenging computer vision problem. In this paper, we present two deep convolutional neural networks solving the problem of single depth map super-resolution. Both networks learn residual decomposition and trained with specific perceptual loss improving sharpness and perceptive quality of the upsampled depth map. Several experiments on various depth super-resolution benchmark datasets show state-of-art performance in terms of RMSE, SSIM, and PSNR metrics while allowing us to process depth super-resolution in real time with over 25-30 frames per second rate.