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Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling [article]

Byungsoo Kim, Hwanjo Yu, Gary Geunbae Lee
2016 arXiv   pre-print
First, we exploit long short-term memory (LSTM) networks to extract higher-level features along the shortest dependency paths, connecting headwords of relations and arguments.  ...  In particular, feedback negative sampling picks highly negative samples among non-positive samples through a model trained with positive samples.  ...  In this paper, we propose a novel Open IE system that automatically extracts features using long short-term memory (LSTM) networks.  ... 
arXiv:1605.07918v1 fatcat:et3xk6z5jvdq5hn7nsspue43re

Lower‐level associations in Gilles de la Tourette syndrome: convergence between hyperbinding of stimulus and response features and procedural hyperfunctioning theories

Adam Takacs, Alexander Münchau, Dezso Nemeth, Veit Roessner, Christian Beste
2021 European Journal of Neuroscience  
An integrated theoretical account of hyperbinding and hyperlearning in GTS allows to formulate predictions for the emergence, activation and long-term persistence of tics in GTS.  ...  Open access funding enabled and organized by Project DEAL.  ...  binding of S-R features and acquisition of long-term S-R contingencies (i.e., the probability or degree of an association).  ... 
doi:10.1111/ejn.15366 pmid:34155701 fatcat:4fwqln6l2vc2jbscepx6lygbse

AT THE INTERFACE: DYNAMIC INTERACTIONS OF EXPLICIT AND IMPLICIT LANGUAGE KNOWLEDGE

Nick C. Ellis
2005 Studies in Second Language Acquisition  
Flawed output can prompt focused feedback by way of recasts that present learners with psycholinguistic data Thanks to Rod Ellis for first suggesting that I try to write this and to the staff and students  ...  , the bulk of language acquisition is implicit learning from usage+ Most knowledge is tacit knowledge; most learning is implicit; the vast majority of our cognitive processing is unconscious+ Implicit  ...  + This research has centered on the role of the phonological loop in the acquisition of vocabulary and formulaic utterances, the ways in which phonological long-term memory supports short-term rehearsal  ... 
doi:10.1017/s027226310505014x fatcat:nahp42s66fasxgd32xtgsuidyi

Web 2.0 Technology Meets Mobile Assisted Language Learning

Min Jung Jee
2011 IALLT Journal of Language Learning Technologies  
A number of representative mobile Web 2.0 technologies will be examined and their applications to language pedagogy will be elucidated in conjunction with relevant paradigms of second language acquisition  ...  In terms of feedback, there are two types of feedback, positive and negative. They both play critical, though different, roles in L2 acquisition.  ...  Moreover, since it uses visual cues, it might be better for long-term memory. (See the following endnote to learn how to start a Flickr project).  ... 
doi:10.17161/iallt.v41i1.8482 fatcat:73nn5pjcdjd25dowjy2vhxmdxu

Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing [article]

Ana Filipa Geros, Ricardo Cruz, Fabrice de Chaumont, Jaime S Cardoso, Paulo Aguiar
2022 bioRxiv   pre-print
Integration with Arduino microcontrollers creates an easy-to-use control platform providing low-latency feedback signals based on the deep learning automatic classification of animal behavior.  ...  This open-software/open-hardware platform can boost the development of customized protocols for automated behavioral research, and support ever more sophisticated, reliable and reproducible behavioral  ...  , and (optional) convolutional Long Short-Term Memory (ConvLSTM) layers, learn spatiotemporal features.  ... 
doi:10.1101/2022.02.22.481410 fatcat:3pblepslgvb5lio7dza2y6hzee

Front Matter: Volume 10646

Ivan Kadar
2018 Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII  
These two-number sets start with 00, 01, 02, 03, 04,  ...  Architectures to incorporate temporal processing in include Recurrent Neural Networks (RNN) and Long Short Term (memory) Networks (LSTM).  ...  However, the use of CNNs for temporal processing is best represented in Long Short Term Memory Networks (LSTM) [20] [21] [22] .  ... 
doi:10.1117/12.2500434 fatcat:wfvvakrbsrfrbiiglzdvnp34o4

Move to Smart Learning Environment: Exploratory Research of Challenges in Computer Laboratory and Design Intelligent Virtual Laboratory for eLearning Technology

Saima Munawar, Saba Khalil Toor, Muhammad Aslam, Muhammad Hamid
2018 Eurasia Journal of Mathematics, Science and Technology Education  
The sample size was (N= 161) drawn from a stratified sampling method for analysis of four strata.  ...  It is needed for practical based courses, through experimentation with the help of artificial intelligence (AI) paradigms.  ...  Different memories have been involved in the processing and storage of knowledge, such as a short-term considered as temporary memory storage for a short period of time and a long-term memory as permanent  ... 
doi:10.29333/ejmste/85036 fatcat:7sb362n5irc4fpqedaz2zlrbhy

Teaching Machines to Converse [article]

Jiwei Li
2020 arXiv   pre-print
The ability of a machine to communicate with humans has long been associated with the general success of AI.  ...  challenges: they tend to output dull and generic responses; they lack a consistent or a coherent persona; they are usually optimized through single-turn conversations and are incapable of handling the long-term  ...  The memory components in memory networks can embed both long-term memory (e.g., common sense facts about the world) and short-term context (e.g., the last few turns of dialog).  ... 
arXiv:2001.11701v1 fatcat:ym74xbxnfrea7aaj7y5opnxopy

Cognitive Processing of Information with Visitor Value in Cultural Heritage Environments. The Case of the SEE TCP SAGITTARIUS 2011–2014

Dorothea Papathanasiou-Zuhrt, Daniel Fernando Weiss-Ibáñez
2014 Procedia Economics and Finance  
A significance assessment facilitates the selectio presupposes a limited working memory capacity to deal w hold mental representations that vary in their degree of adapted to the conditions regulating  ...  The Human Memory Processor consists of Sensory Memory (SM), Short-Term Memory (STM), Working Memory (WM) and Long-Term Memory (LTM).  ...  : < 1 sec SHORT TERM MEMORY Finite Storage-Retrieval Capacity • manipulates visual and auditory data • organizes and integrates data with existing knowledge • governs and directs attention  ... 
doi:10.1016/s2212-5671(14)00509-7 fatcat:5lt7j4uljzh77jly3oopnencqq

In Defense of Tasks and TBLT: Nonissues and Real Issues

Michael H. Long
2016 Annual Review of Applied Linguistics  
Section 2 responds to five alleged problems with TBLT's psycholinguistic rationale, section 3 to six at the classroom level, and section 4 to three claimed problems with implementing TBLT in specific contexts  ...  Acknowledgments I thank the editor, Alison Mackey, and two anonymous reviewers for their careful reading and valuable feedback on the original version of this article. note  ...  system via the massive practice required for automatization, or that if accomplished, the new underlying knowledge will morph into the separate implicit system.  ... 
doi:10.1017/s0267190515000057 fatcat:ltagkt7yhrfv7cq2euep5gpize

The Neuroanatomical, Neurophysiological and Psychological Basis of Memory: Current Models and Their Origins

Eduardo Camina, Francisco Güell
2017 Frontiers in Pharmacology  
The main forms of memory presented include sensory memory, short-term memory, and long-term memory.  ...  Short-term memory (or memory) refers to information processed in a short period of time.  ...  The rats were presented with a sample of an odor in one specific place along the edge of a large open field.  ... 
doi:10.3389/fphar.2017.00438 pmid:28713278 pmcid:PMC5491610 fatcat:oynwh2tbl5ewrktlwhag5w2n6y

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

Daniel J. Franklin, Stephen Grossberg
2016 Cognitive, Affective, & Behavioral Neuroscience  
How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitiveemotional learning?  ...  A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.  ...  Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution  ... 
doi:10.3758/s13415-016-0463-y pmid:27905080 pmcid:PMC5272895 fatcat:4afpasdlqzerrgicjandvacaza

Long Short-Term Network Based Unobtrusive Perceived Workload Monitoring with Consumer Grade Smartwatches in the Wild [article]

Deniz Ekiz, Yekta Said Can, Cem Ersoy
2019 arXiv   pre-print
We showed that Long Short-Term Memory Network outperforms traditional classifiers on discrimination of low and high workload with smartwatches in the wild.  ...  We present an unobtrusive, comfortable, pervasive and affordable Long Short-Term Memory Network based continuous workload monitoring system based on a smartwatch application that monitors the perceived  ...  Long Short-Term Memory Network (LSTM) is a particular type of RNN. It was defined by Hochreiter and Schmidhuber in 1997 [7] to alleviate the long-term dependency problem of RNN [8] .  ... 
arXiv:1912.00019v1 fatcat:3gwyqxn4yrbxrhwk2p4q56aqfe

Spacetimes with Semantics (III) - The Structure of Functional Knowledge Representation and Artificial Reasoning [article]

Mark Burgess
2017 arXiv   pre-print
By assigning interpretations to phenomena, from observers to observed, we may approach a simple description of knowledge-based functional systems, with direct practical utility.  ...  These notes present a unified view of mostly well-known results; they allow us to see information models, knowledge representations, machine learning, and semantic networking (transport and information  ...  None of these necessarily endorse or agree with the presentation here.  ... 
arXiv:1608.02193v4 fatcat:epmifbmo35ccfnx6je7sl6ffeq

Electrocortical Evidence for Long-Term Incidental Spatial Learning Through Modified Navigation Instructions [chapter]

Anna Wunderlich, Klaus Gramann
2018 Lecture Notes in Computer Science  
The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions.  ...  Participants' spatial knowledge of the previously unknown virtual city was tested three weeks later.  ...  whether modified navigation instructions can also lead to long-term improvement of spatial knowledge.  ... 
doi:10.1007/978-3-319-96385-3_18 fatcat:fues2bvogjgnxlf7k6os7pwrty
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