Long-term knowledge acquisition using contextual information in a memory-inspired robot architecture

2016 ◽  
Vol 29 (2) ◽  
pp. 313-334 ◽  
Author(s):  
Ferdian Pratama ◽  
Fulvio Mastrogiovanni ◽  
Soon Geul Lee ◽  
Nak Young Chong
Keyword(s):  
Knowledge Acquisition ◽  
Contextual Information ◽  
Robot Architecture

scholarly journals Patients with Schizophrenia Remember that an Event has Occurred, but not When

1996 ◽  
Vol 168 (4) ◽  
pp. 427-431 ◽  
Author(s):  
Lydia Rizzo ◽  
Jean-Marie Danion ◽  
Martial Van Der Linden ◽  
Danielle Grangé
Keyword(s):  
Memory Impairment ◽  
Discrimination Task ◽  
Contextual Information ◽  
Normal Subjects ◽  
Memory Deficit ◽  
Temporal Context ◽  
Context Memory

BackgroundThe context memory deficit hypothesis of schizophrenia postulates that the long-term deficit associated with this disorder is related to a memory impairment for contextual information.MethodTo test this hypothesis, memory for temporal context was assessed in 33 patients with schizophrenia and 33 normal subjects, using a recency discrimination task.ResultsWhereas patients were able to recall and recognise target items, they were unable to recognise from among the target items those which had been most recently learned.ConclusionsSchizophrenia is associated with a temporal context memory deficit.

Towards Games for Knowledge Acquisition and Modeling

2010 ◽  
Vol 2 (4) ◽  
pp. 48-66 ◽  
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Machine Learning Techniques ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Modelling Approach ◽  
Representation Of Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

scholarly journals A recurrent neural network for urban long-term traffic flow forecasting

2020 ◽  
Vol 50 (10) ◽  
pp. 3252-3265 ◽  
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Djamel Djenouri ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Recurrent Neural Network ◽  
Parallel Implementation ◽  
Contextual Information ◽  
Small Cities ◽  
Neural Network Approach ◽  
Multiple Data ◽  
Big City

Abstract This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data.

Memory in multiple sclerosis: Contextual encoding deficits

2002 ◽  
Vol 8 (3) ◽  
pp. 395-409 ◽  
Author(s):  
ALLEN E. THORNTON ◽  
NAFTALI RAZ ◽  
KAREN A. TUCKER
Keyword(s):  
Multiple Sclerosis ◽  
Semantic Network ◽  
Contextual Information ◽  
Long Term Memory ◽  
Term Memory ◽  
Encoding Specificity ◽  
The Impact ◽  
Selective Impairment ◽  
Encoding And Retrieval

Long-term memory (LTM) is one of the diverse cognitive functions adversely affected by multiple sclerosis (MS). The LTM deficits have often been attributed to failure of retrieval, whereas encoding processes are presumed intact. However, support for this view comes primarily from studies in which encoding and retrieval operations were not investigated systematically. In the current study, we used an encoding specificity paradigm to examine the robustness of encoding in MS and to specifically evaluate the impact of the disease on contextual memory. We hypothesized that persons with MS would exhibit a selective impairment in retrieving items from LTM when required to generate new cue-target associations at encoding, but not when cues held a strong preexisting relationship to the targets. The findings supported the hypotheses. We conclude that the mnemonic deficits associated with MS affect both encoding and retrieval. Specifically, problems with binding of contextual information at encoding impair effective retrieval of memories. Nonetheless, access to these memories can be gained through preexisting associations organized in the semantic network. (JINS, 2002, 8, 395–409.)

scholarly journals Long-Term Influence of Incidental Emotions on the Emotional Judgment of Neutral Faces

2022 ◽  
Vol 12 ◽  
Author(s):  
Marta F. Nudelman ◽  
Liana C. L. Portugal ◽  
Izabela Mocaiber ◽  
Isabel A. David ◽  
Beatriz S. Rodolpho ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Previous Experiment ◽  
Negative Emotion ◽  
Contextual Information ◽  
Emotional Information ◽  
Facial Stimuli ◽  
Neutral Face ◽  
Negative Context ◽  
Important Evidence

Background: Evidence indicates that the processing of facial stimuli may be influenced by incidental factors, and these influences are particularly powerful when facial expressions are ambiguous, such as neutral faces. However, limited research investigated whether emotional contextual information presented in a preceding and unrelated experiment could be pervasively carried over to another experiment to modulate neutral face processing.Objective: The present study aims to investigate whether an emotional text presented in a first experiment could generate negative emotion toward neutral faces in a second experiment unrelated to the previous experiment.Methods: Ninety-nine students (all women) were randomly assigned to read and evaluate a negative text (negative context) or a neutral text (neutral text) in the first experiment. In the subsequent second experiment, the participants performed the following two tasks: (1) an attentional task in which neutral faces were presented as distractors and (2) a task involving the emotional judgment of neutral faces.Results: The results show that compared to the neutral context, in the negative context, the participants rated more faces as negative. No significant result was found in the attentional task.Conclusion: Our study demonstrates that incidental emotional information available in a previous experiment can increase participants’ propensity to interpret neutral faces as more negative when emotional information is directly evaluated. Therefore, the present study adds important evidence to the literature suggesting that our behavior and actions are modulated by previous information in an incidental or low perceived way similar to what occurs in everyday life, thereby modulating our judgments and emotions.

scholarly journals Compu Castel: An Instructional Tool for Teaching Firewall Concepts

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Mahmoud Mostafa
Keyword(s):  
Knowledge Acquisition ◽  
Video Game ◽  
Computer Network ◽  
Educational Games ◽  
Knowledge Retention ◽  
Educational Game ◽  
Short Term ◽  
Educational Video Game ◽  
The Impact

Firewall is an essential device in every computer network. It needs skillful professionals to accurately configure its rules for proper functioning. To help prepare these professionals, university level students need more engaging and attractive interactive tools to develop their skills.  For this regard, this paper presents the design, implementation and evaluation of "Compu Castel" educational video game that teaches firewall concepts. In addition to evaluating the impact of educational game on short-term knowledge acquisition, both, mid-term (after 2 months) and long-term (after 5 months) knowledge retention is analyzed. The results confirm that educational games affect positively short-term knowledge acquisition compared with traditional text based methods. Moreover, educational games enhance knowledge retention for mid-term and long-term periods.

scholarly journals Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation

2019 ◽  
Author(s):  
Zeping Yu ◽  
Jianxun Lian ◽  
Ahmad Mahmoody ◽  
Gongshen Liu ◽  
Xing Xie
Keyword(s):  
User Modeling ◽  
Short Term Memory ◽  
Contextual Information ◽  
Personalized Recommendation ◽  
Short Term ◽  
The Past ◽  
Visual Computing ◽  
Long Short Term Memory ◽  
Natural Way

User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users' short term preference. A natural way to improve the recommender is to combine both long-term and short-term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users' behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

scholarly journals Emulation: From Digital Artefact to Remotely Rendered Environments

2009 ◽  
Vol 4 (3) ◽  
pp. 146-155 ◽  
Author(s):  
Dirk Von Suchodoletz ◽  
Jeffrey Van der Hoeven
Keyword(s):  
Operating Systems ◽  
Contextual Information ◽  
Path Model ◽  
Primary Object ◽  
Computer Environment ◽  
Digital Artefact ◽  
Digital Objects ◽  
User Friendly ◽  
Long Term Preservation

Emulation used as a long-term preservation strategy offers the potential to keep digital objects in their original condition and experience them within their original computer environment. However, having just an emulator in place is not enough. To apply emulation as a fully fledged strategy, an automated and user-friendly approach is required. This cannot be done without knowledge and contextual information of the original software. This paper combines the existing concept of a view path, which captures the contextual information of software, together with new insights into improving the concept with extra metadata. It provides regularly updated instructions for archival management to preserve and access its artefacts. The view-path model requires extensions to the metadata set of the primary object of interest and depends on additionally stored secondary objects for environment recreation like applications or operating systems. This article also addresses a strategy of rendering digital objects by running emulation processes remotely. The advantage of this strategy is that it improves user convenience while maximizing emulation capability.

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