Autonomous taxies are in high demand for smart city scenario. Such taxies have a well specified path to travel. Therefore, these vehicles only required two important parameters. One is detection parameter and other is control parameter. Further, detection parameters require turn detection and obstacle detection. The control parameters contain steering control and speed control. In this paper a novel autonomous taxi model has been proposed for smart city scenario. Deep learning has been used to model the human driver capabilities for the autonomous taxi. A hierarchical Deep Neural Network (DNN) architecture has been utilized to train various driving aspects. In first level, the proposed DNN architecture classifies the straight and turning of road. A parallel DNN is used to detect obstacle at level one. In second level, the DNN discriminates the turning i.e. left or right for steering and speed controls. Two multi layered DNNs have been used on Nvidia Tesla K 40 GPU based system with Core i-7 processor. The mean squared error (MSE) for the detection parameters viz. speed and steering angle were 0.018 and 0.0248 percent, respectively, with 15 milli seconds of realtime response delay.
Social network data usually contain different types of errors. One of them is missing data due to actor non-response. This can seriously jeopardize the results of analyses if not appropriately treated. The impact of missing data may be more severe in valued networks where not only the presence of a tie is recorded, but also its magnitude or strength. Blockmodeling is a technique for delineating network structure. We focus on an indirect approach suitable for valued networks. Little is known about the sensitivity of valued networks to different types of measurement errors. As it is reasonable to expect that blockmodeling, with its positional outcomes, could be vulnerable to the presence of non-respondents, such errors require treatment. We examine the impacts of seven actor non-response treatments on the positions obtained when indirect blockmodeling is used. The start point for our simulation are networks whose structure is known. Three structures were considered: cohesive subgroups, core-periphery, and hierarchy. The results show that the number of non-respondents, the type of underlying blockmodel structure, and the employed treatment all have an impact on the determined partitions of actors in complex ways. Recommendations for best practices are provided. © 2016 Elsevier B.V.
In this article a combination of two modern aspects of games development is considered: (i) the impact of high quality graphics and virtual reality (VR) user adaptation to believe in realness of in-game events by user’s own eyes; (ii) modeling an enemy’s behavior under automatic computer control, called BOT, which reacts similarly to human players. We consider a First-Person Shooter (FPS) game genre, which simulates an experience of combat actions. We describe some tricks to overcome simulator sicknesses in a shooter with respect to Oculus Rift and HTC Vive headsets. We created a BOT model that strongly reduces the conflict and uncertainty in matching human expectations. BOT passes VR game Alan Turing test with 80% threshold of believable human-like behavior.
This chapter analyses the nature of the Brazilian socio-political protests that sparked in 2013 and are still going on today. The focus on determining the main drivers of the movement, protesters’ demands, new forms of collective action and the resulting political changes allows me to trace an important change in the Brazilian democracy as a whole. These protests are neither a one-shot deal, nor an institutionalized social movement. I argue that they rather represent a demand of protesters for participation in the permanent dialogue between the power and the public on every single issue that troubles at least some groups of the society. In this sense, such protests may indicate a completely novel era in the Brazilian democracy that renders representative democracy obsolete and insufficient, while the demands for participatory democracy are being increasingly voiced. Importantly, this mode of protesting proves rather efficient in terms of real changes in politics it brought.
Modern bibliographic databases contain significant amount of information on publication activities of research communities. Researchers regularly encounter challenging task of selecting a co-author for joint research publication or searching for authors, whose papers are worth reading. We propose a new recommender system for finding possible collaborator with respect to research interests. The recommendation problem is formulated as a link prediction within the co-authorship network. The network is derived from the bibliographic database and enriched by the information on research papers obtained from Scopus and other publication ranking systems.
In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing, loan words are not easily recognized and should be evaluated separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network texts. Proposed method is based on the idea of simultaneous scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area.
In this paper, we discuss a semi-dense depth map interpolation method based on convolutional neural network. We propose a compact neural network architecture with loss function defined as Euclidean distance in the feature space of VGG-16 neural network used for deep visual recognition. The suggested solution shows state-of-art performance on synthetic and real datasets. Together with LSD-SLAM, the method could be used to provide a dense depth map for interaction purposes, such as creating a first person game in AR/MR or perception module for autonomous vehicle.
This paper evaluates the policy impact of analytical communities in three Russian regions (Karelia, Tatarstan and Saratov). Based on the existing methods to assess the political power of think tanks, the authors develop a method to evaluate this impact. The authors test this method using the empirical data and findings from interviews, workshops with representatives of analytical communities of the three regions, and from observations and assessments of experts in regional politics. In conclusion, the authors argue that the capacity of analytical communities to impact policy change in a region depends on the level of political competition and pluralism and democratic institutions in the region; the level of consolidation of the analytical community, its autonomous political status and authority.
Definitions: State is a type of polity that is characterized by two main dimensions: “statehood” and “stateness.” Statehood is the recognition of the state by other states as independent nation, equal to others to participate on international arena; receiving and having the “statehood” for country mean that it is a part of the “concert of nations,” such as the member of the United Nations organization. Stateness is a state capacity to sustain its territory, nation, and citizens’ welfare; it is not enough being recognized by other states as such, but also important to support this status in time
We consider deep reinforcement learning algorithms for playing a game based on video input. We compare choosing proper hyper-parameters in deep Q-network model and model-free episodic control focused on reusing of successful strategies. The evaluation was made based on Pong video game implemented in Unreal Engine 4.
We have considered core approaches to the problem of fictional objects. For each model authors covered the problem whether everything fictional exists or not in terms of evaluation, separating groups of objects, quantifying or existing in modal worlds. The article contains brief overview of the approaches for dealing with fictional objects and evaluating statements containing fictional objects as their part.
MorphoRuEval-2017 is an evaluation campaign designed to stimulate the development of the automatic morphological processing technologies for Russian, both for normative texts (news, fiction, nonfiction) and those of less formal nature (blogs and other social media). This article compares the methods participants used to solve the task of morphological analysis. It also discusses the problem of unification of various existing training collections for Russian language.
Our research focuses at the structure of a research community of Russian scientists involved into network studies, which is studied by means of analysis of articles published in Russian-language journals. The direction of network studies in Russia is quite new form of research methodology - however, in recent years we can observe the growing number of scientists working at this direction and institutionalized forms of their cooperation. Studying the structure of these researchers` community is important for the field’s development. This paper is the first report on the research, that is why it focuses on methodological issues. It covers the description of method of citation (reference) analysis that we use and the process on data collection from eLibrary.ru resource, as well as present some brief overview of collected data (based on analysis of 8,000 papers). It is concluded by representation of future steps of the research.
Organizational citizenship behavior (OCB) is an important management construct. Despite previous investigations in relation to social capital, the role of networks in its emergence has received only limited attention. In this paper we investigate the relationship between OCB, with data collected from supervisors evaluating their subordinates; several types of organizational networks (professional, friendship, support, supervisor-subordinate), and several other constructs (collected from the employees themselves), shown to affect OCB in the past. All data were collected at a large insurance company in Russia. Outcomes of this study have several important implications. First, the impact of networks on manifestation of OCB depends not only on the strength of network ties, but on types of network. Second, interorganizational relationships are complex and consist of several levels of mediated relationships. Results of this study can impact the theoretical understanding of OCB and have practical implications for the supervisor-subordinate relationships in the workplace.
In this paper we suggest the first systematic review and com- pare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in DotA 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that pur- pose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best re- sults from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.
Nowadays, the number of people using social network site increases every day. The social networking sites, such as Facebook or Twitter, are sources of human interaction, where users are allowed to create and share their activities, thoughts and place different information about themselves. However, most of this information remains unnoticed. In this work, we propose a machine learning approach to predict Big-Five personality using information from users’ accounts from the social network. The predictions can be used in different areas such as psychology, business, marketing.
In this paper, we present novel winning team predicting models and compare the accuracy of the obtained prediction with TrueSkill model of ranking individual players impact based on their impact in team victory for the two most popular online games: Dota 2 and Counter-Strike: Global Offensive.
The paper presents a short summary on the applications of the quantum logic categorical constructions to the natural language processing. We give a brief overview on the topic of quantum logic in general, and in natural language processing, in particular. As a result, we discuss comparison of sentences and their representation in quantum logic formalism. The examples of using quantum diagrams are considered in order to understand text analysis in terms of quantum logic techniques.
Modern co-authorship networks contain hidden patterns of researchers interaction and publishing activities. We aim to provide a system for selecting a collaborator for joint research or an expert on a given list of topics. We have improved a recommender system for finding possible collaborator with respect to research interests and predicting quality and quantity of the anticipated publications. Our system is based on a co-authorship network derived from the bibliographic database, as well as content information on research papers obtained from SJR Scimago, staff information and the other features from the open data of researchers profiles. We formulate the recommendation problem as a weighted link prediction within the co-authorship network and evaluate its prediction for strong and weak ties in collaborative communities.