Stories as Technology: Past, Present, and Future

2021 ◽  
Author(s):  
Roger’s Bacon ◽  
Sergey Samsonau ◽  
Dario Krpan ◽  
◽  
Keyword(s):  
Social Cognitive ◽  
Fine Grained ◽  
Good Story ◽  
High Level ◽  
Future Technologies

What is it about a good story that causes it to have life-changing effects on one person and not another? I wonder if future technologies will enable us to develop the type of truly deep and fine-grained understanding of stories as social, cognitive, and emotional technologies that might allow us to answer this question with a high-level of precision.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

One-IPC high-level simulation of microthreaded many-core architectures

2016 ◽  
Vol 31 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Irfan Uddin
Keyword(s):  
Design Space Exploration ◽  
Instruction Set ◽  
Efficient Design ◽  
Simulation Framework ◽  
Fine Grained ◽  
Detailed Simulation ◽  
High Level ◽  
Many Core ◽  
The Cost ◽  
Multiple Clusters

The microthreaded many-core architecture is comprised of multiple clusters of fine-grained multi-threaded cores. The management of concurrency is supported in the instruction set architecture of the cores and the computational work in application is asynchronously delegated to different clusters of cores, where the cluster is allocated dynamically. Computer architects are always interested in analyzing the complex interaction amongst the dynamically allocated resources. Generally a detailed simulation with a cycle-accurate simulation of the execution time is used. However, the cycle-accurate simulator for the microthreaded architecture executes at the rate of 100,000 instructions per second, divided over the number of simulated cores. This means that the evaluation of a complex application executing on a contemporary multi-core machine can be very slow. To perform efficient design space exploration we present a co-simulation environment, where the detailed execution of instructions in the pipeline of microthreaded cores and the interactions amongst the hardware components are abstracted. We present the evaluation of the high-level simulation framework against the cycle-accurate simulation framework. The results show that the high-level simulator is faster and less complicated than the cycle-accurate simulator but with the cost of losing accuracy.

scholarly journals Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Jennifer Sleeman ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Machine Learning ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Supervised Machine Learning ◽  
Coreference Resolution ◽  
Multiple Sources ◽  
Fine Grained ◽  
High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

An Empirical Study on Vocabulary Learning Strategies by Jordanian FFL University Students

2018 ◽  
Vol 63 (2) ◽  
pp. 294-315
Author(s):  
Reem Ibrahim Rabadi ◽  
Batoul Al-Muhaissen
Keyword(s):  
Learning Strategies ◽  
Language Learners ◽  
Undergraduate Students ◽  
Social Cognitive ◽  
Vocabulary Learning ◽  
Descriptive Analysis ◽  
Social Strategies ◽  
Vocabulary Learning Strategies ◽  
High Level ◽  
Individual Strategies

Abstract This study explores the use of Vocabulary Learning Strategies (VLSs) by Jordanian undergraduate students majoring French as a Foreign Language (FFL) at Jordanian universities. The vocabulary learning strategies (Memory, Determination, Social, Cognitive, and Metacognitive) were used in this study following Schmitt’s taxonomy. A five-point Likert-scale questionnaire containing 37 items adapted from Schmitt’s (1997) Vocabulary Learning Strategies Questionnaire (VLSQ) administered to 840 FFL undergraduates randomly selected from seven Jordanian universities. The descriptive analysis showed that the participants of the study regardless of their year of study were medium strategy users overall. The results revealed that Memory strategies were the most frequently employed strategies, whereas the Social strategies were the least frequently used ones. Although the participants were medium strategy users, the results of the VLSQ disclosed that some individual strategies were employed at a high level. Accordingly, detecting these strategies will be beneficial to language instructors to improve effective vocabulary teaching techniques and to motivate language learners to use them more frequently.

Explaining continuous improvement

2021 ◽  
pp. 197-210
Author(s):  
John Toner ◽  
Barbara Gail Montero ◽  
Aidan Moran
Keyword(s):  
Continuous Improvement ◽  
Focused Attention ◽  
Fine Grained ◽  
Skill Maintenance ◽  
And Performance ◽  
Situational Demands ◽  
High Level ◽  
Conscious Processes ◽  
Automated Procedures

The final chapter synthesizes the arguments presented over the course of the book by suggesting that skill execution continues to be governed by conscious processes even after performers have attained a high level of expertise. It argues that skill-focused attention is necessary if experts are to eschew proceduralization and react flexibly to ‘crises’ and fine-grained changes in situational demands. In doing so, it discusses the role played by conscious control, reflection, and bodily awareness in maintaining performance proficiency. It suggests that skill maintenance and continuous improvement are underpinned by the use of both automated procedures (acknowledging that these are inherently active and flexible) and metacognitive knowledge. The chapter concludes by briefly considering how skill-focused attention needs to be applied in both training and performance contexts in order to facilitate continuous improvement.

scholarly journals An SDN-Based Connectivity Control System for Wi-Fi Devices

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Duc-Thang Nguyen ◽  
Taehong Kim
Keyword(s):  
Control System ◽  
Service Providers ◽  
Connection Management ◽  
Fine Grained ◽  
Connectivity Information ◽  
Wireless Environment ◽  
Remote Services ◽  
Iot Devices ◽  
And Performance ◽  
High Level

In recent years, the prevalence of Wi-Fi-enabled devices such as smartphones, smart appliances, and various sensors has increased. As most IoT devices lack a display or a keypad owing to their tiny size, it is difficult to set connectivity information such as service set identifier (SSID) and password without any help from external devices such as smartphones. Moreover, it is much more complex to apply advanced connectivity options such as SSID hiding, MAC ID filtering, and Wi-Fi Protected Access (WPA) to these devices. Thus, we need a new Wi-Fi network management system which not only facilitates client access operations but also provides a high-level authentication procedure. In this paper, we introduce a remote connectivity control system for Wi-Fi devices based on software-defined networking (SDN) in a wireless environment. The main contributions of the proposed system are twofold: (i) it enables network owner/administrator to manage and approve connection request from Wi-Fi devices through remote services, which is essential for easy connection management across diverse IoT devices; (ii) it also allows fine-grained access control at the device level through remote control. We describe the architecture of SDN-based remote connectivity control of Wi-Fi devices. While verifying the feasibility and performance of the proposed system, we discuss how the proposed system can benefit both service providers and users.

Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

2020 ◽  
pp. 2150031
Author(s):  
Weichun Liu ◽  
Xiaoan Tang ◽  
Chenglin Zhao
Keyword(s):  
Correlation Filter ◽  
Coarse Grained ◽  
Dynamic Feature ◽  
Semantic Features ◽  
Low Level ◽  
Fine Grained ◽  
Semantic Embedding ◽  
Training Stage ◽  
Online Tracking ◽  
High Level

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

scholarly journals Hybrid Attention Network for Language-Based Person Search

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5279
Author(s):  
Yang Li ◽  
Huahu Xu ◽  
Junsheng Xiao
Keyword(s):  
Image Features ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Semantic Features ◽  
Retrieval Task ◽  
Attention Network ◽  
Fine Grained ◽  
Person Search ◽  
High Level ◽  
Language Description

Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively and accurately. Third, a cross-modal attention mechanism and a joint loss function for cross-modal learning, which can pay more attention to the relevant parts between text and image features. It can better exploit both the cross-modal and intra-modal correlation and can better solve the problem of cross-modal heterogeneity. Extensive experiments have been conducted on the CUHK-PEDES dataset. Our approach obtains higher performance than state-of-the-art approaches, demonstrating the advantage of the approach we propose.

scholarly journals Modelling transmission and control of the COVID-19 pandemic in Australia

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sheryl L. Chang ◽  
Nathan Harding ◽  
Cameron Zachreson ◽  
Oliver M. Cliff ◽  
Mikhail Prokopenko
Keyword(s):  
Computational Simulation ◽  
International Travel ◽  
School Closures ◽  
Agent Based ◽  
Social Distancing ◽  
Fine Grained ◽  
Trade Offs ◽  
Travel Restrictions ◽  
High Level ◽  
And Control

Abstract There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.

The Factor Structure of Social Cognition in Schizophrenia: A Focus on Replication With Confirmatory Factor Analysis and Machine Learning

2020 ◽  
pp. 216770262095152
Author(s):  
Philipp Riedel ◽  
William P. Horan ◽  
Junghee Lee ◽  
Gerhard S. Hellemann ◽  
Michael F. Green
Keyword(s):  
Machine Learning ◽  
Social Cognition ◽  
Factor Structure ◽  
Social Cognitive ◽  
Linear Models ◽  
Factor Model ◽  
Confirmatory Factor Analyses ◽  
Attributional Bias ◽  
Confirmatory Factor ◽  
High Level

Social cognition has become a major focus in psychosis research aimed at explaining heterogeneity in functional outcome and developing interventions oriented to functional recovery. However, there is still no consensus on the structure of social cognition in psychosis, and research in this area has been plagued by lack of replication. Our first goal was to replicate the factor structure of social cognition using nearly identical tasks in independent samples. Our second goal was to externally validate the factors as they relate to nonsocial cognition and various symptoms in the prediction of functioning using machine learning. Confirmatory factor analyses validated a three-factor model for social cognition in psychosis (low-level, high-level, attributional bias factor). A least absolute shrinkage and selection operator regression and cross-validation provided evidence for external validity of data-driven linear models including the social-cognitive factors, nonsocial cognition, and symptoms. We addressed the replicability problems that have impeded research in this area, and our results will guide future psychosis studies.

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