Atom : Novel data capturing technique for robotic teleoperation

2021 ◽  
Author(s):  
Bestin Antu ◽  
T Amal ◽  
Janet Joby ◽  
Retty George
Keyword(s):  
Data Capturing ◽  
Robotic Teleoperation

The Datalogical Drug Mule

2017 ◽  
Vol 3 (3) ◽  
pp. 9-29
Author(s):  
Juan Llamas-Rodriguez
Keyword(s):  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Data Driven ◽  
Biological Processes ◽  
Cultural Narratives ◽  
Government Documents ◽  
Human Labor ◽  
Information And Communication ◽  
Data Capturing ◽  
Legal Structures

Borders and bodies are increasingly regulated by data-capturing mechanisms spread across the world through information and communication technologies. This article traces the features and implications of such a border-body datalogical entanglement through the figure of the drug mule. It analyzes government documents and recorded case studies to argue that this figure emerges from an assemblage of cultural narratives, legal structures, human labor, technical practices, and biological processes. The datalogical drug mule is already implicated in a struggle over what, and how, data is meaningful and actionable. Investigating this figure allows us to begin disentangling the data-driven mechanisms that constitute modern borders and bodies while at the same time accounting for analog continuities in contemporary practices of border security.

scholarly journals When ‘You’ Means ‘I’: The German 2Nd Ps.Sg. Pronoun Du between Genericity and Subjectivity

2018 ◽  
Vol 4 (1) ◽  
pp. 280-309
Author(s):  
Peter Auer ◽  
Anja Stukenbrock
Keyword(s):  
18Th Century ◽  
The Other ◽  
Subjective Interpretation ◽  
Close Analysis ◽  
Data Capturing ◽  
The One

Abstract In this paper, we first present a close analysis of conversational data, capturing the variety of non-addressee deictic usages of du in contemporary German. From its beginnings, it has been possible to use non-addressee deictic du not only for generic statements, but also for subjective utterances by a speaker who mainly refers to his or her own experiences. We will present some thoughts on the specific inferences leading to this interpretation, making reference to Buhler’s deixis at the phantasm. In the second part of the paper, we show that non-addressee deictic du (‘thou’) as found in present-day German is not an innovation but goes back at least to the 18th century. However, there is some evidence that this usage has been spreading over the last 50 years or so. We will link non-addressee deictic du back historically to the two types of “person-shift” for du discussed by Jakob Grimm in his 1856 article “Uber den Personenwechsel in der Rede” [On person shift in discourse]. Grimm distinguishes between person shift in formulations of “rules and law” on the one hand, and person shift in what he calls “thou-monologue” on the other. The subjective interpretation of non-addressee-deictic du in present-day German may have originated from these “thou-monologues”

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

Post-Render Warp with Late Input Sampling Improves Aiming Under High Latency Conditions

2020 ◽  
Vol 3 (2) ◽  
pp. 1-18
Author(s):  
Joohwan Kim ◽  
Pyarelal Knowles ◽  
Josef Spjut ◽  
Ben Boudaoud ◽  
Morgan Mcguire
Keyword(s):  
Task Performance ◽  
Completion Time ◽  
Source Code ◽  
Experimental Methodology ◽  
Task Completion ◽  
Controlled Environment ◽  
Content Creation ◽  
Remote Rendering ◽  
Streaming Services ◽  
Robotic Teleoperation

End-to-end latency in remote-rendering systems can reduce user task performance. This notably includes aiming tasks on game streaming services, which are presently below the standards of competitive first-person desktop gaming. We evaluate the latency-induced penalty on task completion time in a controlled environment and show that it can be significantly mitigated by adopting and modifying image and simulation-warping techniques from virtual reality, eliminating up to 80% of the penalty from 80 ms of added latency. This has potential to enable remote rendering for esports and increase the effectiveness of remote-rendered content creation and robotic teleoperation. We provide full experimental methodology, analysis, implementation details, and source code.

On the role of the anthropocentric factor in the realization of the derivational potential of names of ungulates as motivating lexical units

2021 ◽  
pp. 191-210
Author(s):  
Nikolay D. Golev ◽  
◽  
Irina P. Falomkina ◽  
Keyword(s):  
Native Speakers ◽  
Russian Language ◽  
Search System ◽  
Proposed Model ◽  
Data Capturing ◽  
Building Behavior ◽  
Building System ◽  
Google Search ◽  
The Russian Language

The paper is dedicated to describing the word-building system of the Russian language in terms of its vocabulary. Lexical factors are discussed influencing the formation of lexical units’ potential as motivating units of word-building processes and relations and the realization of this potential in language activities. Of most interest for the authors are anthropocentric determinants, most of which are coordinating the lexical system and, through its mediation, the word-building system with the worldview of native speakers of the Russian language. The proposed model of derivational development of vocabulary provides such coordination through studying the deep-seated process of conceptualization of the words that are the potential motivators of neologisms. This study identifies the word frequency as an external manifestation of conceptualization. The frequency data were obtained from Google search system statistical data. Capturing not only usual but also occasional and potential words, this source is an effective tool for studying word-building processes and their results. This study has unveiled the interrelation between the language worldview of native speakers of Russian and their “word-building behavior” in language activities. The worldview has been found, first of all, to be determined by the pragmatic factor, which primarily influences the usage of a word in the speech reflected by its frequency. The frequency ranks lexical units due to their derivational potential and thereby provides a researcher with a reliable instrument for its study.

Implementation of the visual data processing algorithms for onboard computing units

2021 ◽  
Vol 9 (2) ◽  
pp. 106-111
Author(s):  
Sergey Sokolov ◽  
Andrey Boguslavsky ◽  
Sergei Romanenko
Keyword(s):  
Vision System ◽  
Fpga Implementation ◽  
Visual Data ◽  
Dense Optical Flow ◽  
Data Capturing ◽  
Computation Algorithm ◽  
Modern Experience ◽  
Computing Module ◽  
Xilinx Fpga

According to the short analysis of modern experience of hardware and software for autonomous mobile robots a role of computer vision systems in the structure of those robots is considered. A number of configurations of onboard computers and implementation of algorithms for visual data capturing and processing are described. In original configuration space the «algorithms-hardware» plane is considered. For software designing the realtime vision system framework is used. Experiments with the computing module based on the Intel/Altera Cyclone IV FPGA (implementation of the histogram computation algorithm and the Canny's algorithm), with the computing module based on the Xilinx FPGA (implementation of a sparse and dense optical flow algorithms) are described. Also implementation of algorithm of graph segmentation of grayscale images is considered and analyzed. Results of the first experiments are presented.

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