Automated Hypotheses Generation via Combinatorial Causal Optimization

Even though numerous tools and technologies have been developed to meet this need with various degrees of success, a conceptual framework is needed to fully realize the value of those tools and technologies. The authors propose Complex System (CS) to be the logical foundation of such a framework. Since translational research is a spiral and dynamic process. With the CS mindset, they designed a multi-layer architecture called HyGen (Hypotheses Generation Framework) to address the challenges faced by translational researchers. In order to evaluate the framework, the authors carried out heuristic and quantitative tests in Colorectal Cancer disease area. The results demonstrate the potential of this hybrid approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases, which may eventually lead to the discovery of novel disease targets, biomarkers and therapies.

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The effects of subjective time pressure and individual differences on hypotheses generation and action prioritization in police investigations.

Journal of Experimental Psychology Applied ◽

10.1037/a0032148 ◽

2013 ◽

Vol 19 (1) ◽

pp. 83-93 ◽

Cited By ~ 14

Author(s):

Laurence Alison ◽

Bernadette Doran ◽

Matthew L. Long ◽

Nicola Power ◽

Amy Humphrey

Keyword(s):

Individual Differences ◽

Time Pressure ◽

Subjective Time ◽

Police Investigations ◽

Hypotheses Generation

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A cognitive model’s view of animal cognition

Current Zoology ◽

10.1093/czoolo/57.4.499 ◽

2011 ◽

Vol 57 (4) ◽

pp. 499-513 ◽

Cited By ~ 4

Author(s):

Sidney D’mello ◽

Stan Franklin

Keyword(s):

Empirical Research ◽

Cognitive Processes ◽

Computational Models ◽

Animal Species ◽

Animal Cognition ◽

Cognitive Model ◽

Field Of Study ◽

Working Model ◽

Hypotheses Generation

Abstract Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can make toward organizing and assimilating this literature, as well as toward identifying important concepts and their interrelations. Using the LIDA model as an example, a framework is described within which to integrate the diverse research in animal cognition. Such a framework can provide both an ontology of concepts and their relations, and a working model of an animal’s cognitive processes that can compliment active empirical research. In addition to helping to account for a broad range of cognitive processes, such a model can help to comparatively assess the cognitive capabilities of different animal species. After deriving an ontology for animal cognition from the LIDA model, we apply it to develop the beginnings of a database that maps the cognitive facilities of a variety of animal species. We conclude by discussing future avenues of research, particularly the use of computational models of animal cognition as valuable tools for hypotheses generation and testing.

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An Advanced Automatic Electronic Diagnosis System

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v1i1.1575 ◽

2015 ◽

Vol 1 (1) ◽

Author(s):

Prabhakar Dubey ◽

Mahendra Kumar

Keyword(s):

Complex System ◽

Personal Experience ◽

Digital Circuits ◽

Diagnostic Process ◽

Diagnostic Equipment ◽

Diagnosis System ◽

Technical Diagnosis ◽

Design Engineers ◽

General Terms ◽

Hypotheses Generation

Every complex system is liable to faults and failures. In the most general terms, a fault is any change in a system that prevents it from operating in the proper manner. Here, the diagnosis of catastrophic defects in complex digital circuits. In fact, today the technical diagnosis is great challenge for design engineers because diagnostic problems are generally under determinate. It is also a deductive process with one set of data creating, in general, unlimited number of hypotheses among which one should try to get the solution. So the diagnosis methods are based on proprietary knowledge and personal experience, although they were built into integrated diagnostic equipment. The approach proposed here is an alternative to existing solutions, and it is expected to encompass all phases of the diagnostic process: symptom detection, hypotheses generation, and hypotheses discrimination.

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A Granular Unified Min-Max Fuzzy-Neuro Framework for Learning Fuzzy Systems

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2009.p0520 ◽

2009 ◽

Vol 13 (5) ◽

pp. 520-528 ◽

Cited By ~ 2

Author(s):

Mokhtar Beldjehem ◽

Keyword(s):

Cognitive Processes ◽

Fuzzy Systems ◽

Main Idea ◽

Fuzzy Rules ◽

Approximation Procedure ◽

Unified Framework ◽

Fine Grained ◽

Neuro Fuzzy ◽

Fuzzy Partitions ◽

Hypotheses Generation

We propose a novel computational granular unified framework that is cognitively motivated for learning if-then fuzzy weighted rules by using a hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract or learn fuzzy rules from only input-output examples by integrating some useful concepts from the human cognitive processes and adding some interesting granular functionalities. This learning scheme uses an exhaustive search over the fuzzy partitions of involved variables, automatic fuzzy hypotheses generation, formulation and testing, and approximation procedure of Min-Max relational equations. The main idea is to start learning from coarse fuzzy partitions of the involved variables (both input and output) and proceed progressively toward fine-grained partitions until finding the appropriate partitions that fit the data. According to the complexity of the problem at hand, it learns the whole structure of the fuzzy system, i.e. conjointly appropriate fuzzy partitions, appropriate fuzzy rules, their number and their associated membership functions.

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Psychosocial and Quality of Life Outcomes of Prosthetic Auricular Rehabilitation with CAD/CAM Technology

International Journal of Dentistry ◽

10.1155/2014/393571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Chi Keung Tam ◽

Colman Patrick McGrath ◽

Samuel Mun Yin Ho ◽

Edmond Ho Nang Pow ◽

Henry Wai Kuen Luk ◽

...

Keyword(s):

Quality Of Life ◽

Body Image ◽

Treatment Outcomes ◽

Satisfaction With Life ◽

The Body ◽

Life Outcomes ◽

Cad Cam ◽

Hypotheses Generation ◽

Innovative Techniques

Introduction. The psychosocial and quality of life (QoL) of patients with deformed or missing ears are frequently compromised. The aim of this study is to develop innovative techniques using CAD/CAM technology in prosthetic auricular rehabilitation and provide improvement in the treatment outcomes, including their psychology and QoL.Methods. This is a preliminary clinical cohort study. Six patients requesting for auricular reconstruction were recruited and rehabilitated with implant-supported prosthesis using CAD/CAM technology. Different treatment outcomes including QoL and psychological changes were assessed at different time points.Results. A significant reduction in severity of depressive symptoms(P=0.038)and an improving trend of satisfaction with life were found at 1 year postoperatively when compared with the preoperative findings. The domain scores in ‘‘Body image’’, ‘‘Family/friends/strangers’’, and ‘‘Mood’’ were also significantly higher(P<0.05)at 1 year postoperatively than 1 week postoperatively. However, only 50% of the patients wear their auricular prosthesis regularly.Conclusion. This preliminary study has confirmed that implant-supported auricular prosthesis could induce improvement in the psychology and QoL with statistically significant differences in the domains of the body image, social interaction, and mood. Our present findings can inform research design and hypotheses generation of future studies.

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An Analytical Survey on Diagnosis Algorithm in Generic Defect Large-Scale Failures in Computer Networks

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i3.587 ◽

2018 ◽

Vol 8 (3) ◽

pp. 28

Author(s):

Jaychand Vishwakarma ◽

Sakil Ahmad Ansari

Keyword(s):

Large Scale ◽

Negative Symptoms ◽

Threshold Value ◽

Dynamic Threshold ◽

Ranking Algorithm ◽

Probability Models ◽

Generation Algorithm ◽

Diagnosis Algorithm ◽

Hypotheses Generation ◽

Positive And Negative Symptoms

We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose pruning and thresholding along with a dynamic threshold value selector, to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies.

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