scholarly journals Modelling auditory attention

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160101 ◽  
Author(s):  
Emine Merve Kaya ◽  
Mounya Elhilali
Keyword(s):  
Computational Models ◽  
Selection Process ◽  
Relevant Information ◽  
Auditory Attention ◽  
Cognitive Resources ◽  
Cocktail Party Problem ◽  
Modelling Studies ◽  
Pertinent Information ◽  
Current Article ◽  
Auditory Scenes

Sounds in everyday life seldom appear in isolation. Both humans and machines are constantly flooded with a cacophony of sounds that need to be sorted through and scoured for relevant information—a phenomenon referred to as the ‘cocktail party problem’. A key component in parsing acoustic scenes is the role of attention, which mediates perception and behaviour by focusing both sensory and cognitive resources on pertinent information in the stimulus space. The current article provides a review of modelling studies of auditory attention. The review highlights how the term attention refers to a multitude of behavioural and cognitive processes that can shape sensory processing. Attention can be modulated by ‘bottom-up’ sensory-driven factors, as well as ‘top-down’ task-specific goals, expectations and learned schemas. Essentially, it acts as a selection process or processes that focus both sensory and cognitive resources on the most relevant events in the soundscape; with relevance being dictated by the stimulus itself (e.g. a loud explosion) or by a task at hand (e.g. listen to announcements in a busy airport). Recent computational models of auditory attention provide key insights into its role in facilitating perception in cluttered auditory scenes. This article is part of the themed issue ‘Auditory and visual scene analysis’.

ARTIFICIAL INTELLIGENCE IN DENTISTRY: A MILESTONE

2021 ◽  
pp. 49-52
Author(s):  
Gaurvi Vikram Kamra ◽  
Ankur Sharma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Treatment Planning ◽  
Computational Models ◽  
Improve Patient Care ◽  
Relevant Information ◽  
Documented Information ◽  
Improve Patient

The concept of "articial intelligence" (AI) refers to machines that are capable of executing human-like tasks. AI can also be dened as a eld concerned with computational models that can reason and act intelligently. Perspicacious software for data computation has become a necessity as the amount of documented information and patient data has increased dramatically. The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview. AI has been used in a variety of ways, from processing of data and locating relevant information to using neural networks for diagnosis and the introduction of augmented reality and virtual reality in dental education. AI-based apps will improve patient care by relieving the dental workforce of tedious routine duties, improving population health at lower costs, and eventually facilitating individualized, anticipatory, prophylactic, and collaborative dentistry. The convergence of AI and digitization has ushered in a new age in dentistry, with tremendously promising future prospects.The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview.

Using ICT to Overcome Constraints in the Agriculture Value Chain

2013 ◽  
pp. 689-699
Author(s):  
Siegfried Kofi Debrah ◽  
Isaac Kwadwo Asare
Keyword(s):  
Value Chain ◽  
Smallholder Farmers ◽  
Relevant Information ◽  
Monitoring And Control ◽  
Farming Communities ◽  
Development Partner ◽  
Management Context ◽  
Pertinent Information ◽  
Real Time Information ◽  
And Control

Development partner efforts and private sector initiatives on ICT applications in agriculture have brought new opportunities for farmers and traders to reduce transaction costs and increase incomes. The applications are primarily used for linking actors in the agricultural value chain, accessing real time information on prices, buyers and sellers, transport and haulage, and other relevant information services in the agricultural value chain. Limited evidence from Ghana and elsewhere show that cell phone applications have resulted in increased incomes but the impacts and sustainability of other ICT applications have proven elusive. The role of ICT in overcoming the key constraints in the agricultural value chain and for making evidence-based decisions will be greatly enhanced if farmers, aggregators, and other stakeholders in the value chain pay attention to their business scope and schedule planning, executing, monitoring and control, procurement, risk planning, and stakeholder communications in a “project management” context. When this is done, ICT applications will facilitate supply chain management through sharing of timely and pertinent information on producers, buyers and other services, thereby helping to promote industry competitiveness. The major challenges to the widespread use and sustainability of ICT remain access to the appropriate ICT tool, poor road and storage infrastructure (particularly in the farming communities) and illiteracy on the part of the majority of smallholder farmers.

Answer First or Google First? Using the Internet in ways that Enhance, not Impair, One’s Subsequent Retention of Needed Information

2020 ◽  
pp. 147572572096159
Author(s):  
Saskia Giebl ◽  
Stefany Mena ◽  
Benjamin C. Storm ◽  
Elizabeth Ligon Bjork ◽  
Robert A. Bjork
Keyword(s):  
Relevant Information ◽  
Choice Test ◽  
Cognitive Resources ◽  
Related Information ◽  
Needed Information ◽  
Technological Advances ◽  
New Information ◽  
Subsequent Recall ◽  
Programming Knowledge ◽  
Problem Solving Task

Technological advances have given us tools—Google, in particular—that can both augment and free up our cognitive resources. Research has demonstrated, however, that some cognitive costs may arise from our reliance on such external memories. We examined whether pretesting—asking participants to solve a problem before consulting Google for needed information—can enhance participants’ subsequent recall for the searched-for content as well as for relevant information previously studied. Two groups of participants, one with no programming knowledge and one with some programming knowledge, learned several fundamental programming concepts in the context of a problem-solving task. On a later multiple-choice test with transfer questions, participants who attempted the task before consulting Google for help out-performed participants who were allowed to search Google right away. The benefit of attempting to solve the problem before googling appeared larger with some degree of programming experience, consistent with the notion that some prior knowledge can help learners integrate new information in ways that benefit its learning as well as that of previously studied related information.

An Attention-Based Framework for Context Identification in Autonomous Robots

2017 ◽  
Author(s):  
Maria Alessandra Montironi ◽  
Harry H. Cheng
Keyword(s):  
Information Content ◽  
Computational Models ◽  
Markov Models ◽  
Relevant Information ◽  
Recursive Estimation ◽  
Sensor Data ◽  
Infrared Range ◽  
Contextual Knowledge ◽  
Visual Sensor

Being able to correctly assess the context it is currently acting in is a very important ability for every autonomous robot performing a task in a real world scenario such as navigating, manipulating an object or interacting with a user. Sensors are the primary interface with the external world and the means through which contextual knowledge is generated. Humans and animals use cognitive processes such as attention to selectively process perceived task-relevant information and to recognize the context they are currently acting in. Biologically inspired computational models of attention have been developed in recent years to be used as interpretation keys of mainly visual sensor data. This paper presents a new framework for situation assessment that expands existing computational models of attention by providing a unified methodology to interpret and combine data from different sources. The method utilizes probabilistic state estimation techniques such as Bayesian recursive estimation, Kalman filter, and hidden Markov models to interpret features extracted from sensor data and formulate hypotheses about different aspects of the task the robot is performing or of the environment it is currently acting in. The concept of Bayesian surprise is also used to mark the information content of each new hypothesis. A weight that takes into account the confidence in the estimate that generated the hypothesis, its information content, and the quality of the data is then calculated. The methodology presented in this paper is general and allows to consistently apply the framework to data from different types of sensors and to then combine their hypotheses. Once formulated, hypotheses can then be used for context-based reasoning and plan adaptation. The framework was implemented on a small two-wheel differential drive robot equipped with a camera, an ultrasonic and two infrared range sensors. Three different sets of results that evaluate the performance of different features of the framework are presented. First, the method has been applied to detect a target object and to distinguish it from similar objects. Second, the hypotheses strength calculation method has been characterized by isolating the effect of belief, surprise, and of the quality of the data. Third, the combination of hypotheses from different modules has been evaluated in the context of environment classification.

Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events: Methods for item selection in industry-sponsored oncology clinical trials

2018 ◽  
Vol 15 (6) ◽  
pp. 616-623 ◽  
Author(s):  
Peter C Trask ◽  
Amylou C Dueck ◽  
Elisabeth Piault ◽  
Alicyn Campbell
Keyword(s):  
Clinical Trials ◽  
Adverse Events ◽  
Patient Reported Outcomes ◽  
Selection Process ◽  
Late Phase ◽  
Relevant Information ◽  
Specific Patient ◽  
Patient Reported ◽  
Oncology Clinical Trials ◽  
The Common

As new cancer treatment regimens demonstrate increased potential to improve patients’ survival, more focus is directed toward the quality of that extension of life and to obtaining additional information from patients regarding their experience with treatment. The utility of capturing patient-reported treatment-related symptoms to complement traditional clinician-rated symptomatic adverse event reporting is well-documented. The National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events is an item library aimed at capturing patient-reported symptoms to inform the patient perspective on a treatment’s tolerability. The U.S. Food and Drug Administration has recommended using the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events in clinical trials. A practical guideline is needed to inform a priori selection of specific Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events items for use in any given industry-sponsored oncology clinical trial. Standardizing this selection process will foster systematic and consistent data collection as part of drug development and enhance our knowledge on how to use patient-relevant information as part of a treatment’s risk/benefit assessment. This article presents methods and consensus recommendations for selecting specific Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events items to include in early-phase and late-phase oncology clinical trials.

WhatsApp groups: channel for sharing information among LIS professionals in Nigeria

2020 ◽  
Vol 38 (4) ◽  
pp. 805-820
Author(s):  
Obiora Kingsley Udem ◽  
Doris U. Aghoghovwia ◽  
Ebikabowei Emmanuel Baro
Keyword(s):  
Virtual Communities ◽  
Information Science ◽  
Relevant Information ◽  
Library And Information Science ◽  
Content Type ◽  
Job Advertisements ◽  
Shared Information ◽  
Pertinent Information ◽  
Professional Information ◽  
Training Opportunities

Purpose The purpose of this study is to determine the type of information Library and Information Science professionals share in the WhatsApp groups in Nigeria. Design/methodology/approach The study adopted a quantitative content analysis research design. With a total of 739 participants, 1,385 posts of six different WhatsApp groups of Library and Information Science professionals for three months were analyzed. Findings The study found that the most shared information among the Library and Information Science professionals in Nigeria is post on professional information. This demonstrates that librarians are determined to share professional information among them to promote the profession. This was followed by political information basically on the Nigerian Library Association national executives’ election, and job advertisements related to the library. Although a few members violate the rules by posting the kind of information not required in the WhatsApp group, the erring members are quickly called to order and warned by the WhatsApp group administrator. Social implications Professional ties can grow among information specialists and library practitioners through participation in virtual communities such as WhatsApp group. The implication of this work is in showing that social media especially WhatsApp groups can be used as a knowledge sharing mechanism to share timely, current and relevant information among professionals in different occupations. Originality/value Findings on the use of WhatsApp group in sharing professional information will inform several other Library and Information Science professionals in other countries of the need to adopt this channel to disseminate timely information related to up-coming conferences, training opportunities, workshops, call for papers and so on among the professionals. The results of this paper are valuable for anyone interested in an avenue to share or receive much quicker and pertinent information that saves the time of professionals in any occupation.

scholarly journals Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

2021 ◽  
Vol 17 (7) ◽  
pp. e1009139
Author(s):  
Yonatan Sanz Perl ◽  
Carla Pallavicini ◽  
Ignacio Pérez Ipiña ◽  
Athena Demertzi ◽  
Vincent Bonhomme ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Relevant Information ◽  
Complete Characterization ◽  
Brain State ◽  
Whole Brain ◽  
Brain Injured ◽  
Level Of Consciousness ◽  
The Stability ◽  
Injured Patients

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

scholarly journals Evaluation of emergency evacuation in school buildings_ Protocol for a Systematic Review

2019 ◽  
Vol 3 (3) ◽  
pp. 75-81
Author(s):  
Raquel Martins ◽  
J. Duarte ◽  
Mário Vaz
Keyword(s):  
Systematic Review ◽  
Disaster Preparedness ◽  
Web Of Science ◽  
Selection Process ◽  
Relevant Information ◽  
Emergency Evacuation ◽  
Relevant Research ◽  
Eligibility Criteria ◽  
Review Protocol ◽  
Reduce Risk

Children are amongst the most vulnerable affected groups by natural and human-made disasters. Disaster preparedness education programs have been developed to help reduce risk and increase resilience for hazardous events. A better understanding is needed about children evacuation behaviour in schools and the time of evacuation. Therefore, a systematic review is proposed to search for relevant information about emergency evacuation response in schools. This systematic review protocol was developed to present adequate guidelines that can provide relevant research results to fulfil the sought objective. Sixteen databases will be accessed (Scopus, Web of Science, and ScienceDirect, are some examples) and a total of three keyword expressions will be used. The selection process will be thoroughly described, including detailed data treatment and used eligibility criteria, to contribute to the general research on this field.

scholarly journals Computational Mechanism for the Effect of Psychosis Community Treatment: A Conceptual Review From Neurobiology to Social Interaction

2021 ◽  
Vol 12 ◽  
Author(s):  
David Benrimoh ◽  
Andrew Sheldon ◽  
Ely Sibarium ◽  
Albert R. Powers
Keyword(s):  
Computational Models ◽  
Sensory Information ◽  
Community Treatment ◽  
Psychotic Symptoms ◽  
Cognitive Resources ◽  
The Social ◽  
Multiple Levels ◽  
New Treatments ◽  
Therapeutic Research ◽  
Putative Mechanism

The computational underpinnings of positive psychotic symptoms have recently received significant attention. Candidate mechanisms include some combination of maladaptive priors and reduced updating of these priors during perception. A potential benefit of models with such mechanisms is their ability to link multiple levels of explanation, from the neurobiological to the social, allowing us to provide an information processing-based account of how specific alterations in self-self and self-environment interactions result in the experience of positive symptoms. This is key to improving how we understand the experience of psychosis. Moreover, it points us toward more comprehensive avenues for therapeutic research by providing a putative mechanism that could allow for the generation of new treatments from first principles. In order to demonstrate this, our conceptual paper will discuss the application of the insights from previous computational models to an important and complex set of evidence-based clinical interventions with strong social elements, such as coordinated specialty care clinics (CSC) in early psychosis and assertive community treatment (ACT). These interventions may include but also go beyond psychopharmacology, providing, we argue, structure and predictability for patients experiencing psychosis. We develop the argument that this structure and predictability directly counteract the relatively low precision afforded to sensory information in psychosis, while also providing the patient more access to external cognitive resources in the form of providers and the structure of the programs themselves. We discuss how computational models explain the resulting reduction in symptoms, as well as the predictions these models make about potential responses of patients to modifications or to different variations of these interventions. We also link, via the framework of computational models, the patient's experiences and response to interventions to putative neurobiology.

scholarly journals Active Shift Attention Based Object Tracking System

2021 ◽  
Author(s):  
◽  
Aisha Ajmal
Keyword(s):  
Object Tracking ◽  
Computational Models ◽  
Vision System ◽  
Tracking System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Vision ◽  
Biological Mechanisms ◽  
Salient Regions

<p>The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis, object detection and object tracking.  Different object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with object tracking can be effective in increasing the performance by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.  The overall objective of this thesis is to develop a visual saliency based object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.   Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.</p>

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