A Lightweight Keypoint Matching Framework for Morphometric Landmark Detection

Abstract Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/WingLanmarkPredictor, currently supporting Windows, MacOS, and Linux.

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Towards a contemporary player learning in development framework for sports practitioners

International Journal of Sports Science & Coaching ◽

10.1177/17479541211002335 ◽

2021 ◽

pp. 174795412110023

Author(s):

Mark O Sullivan ◽

Carl T Woods ◽

James Vaughan ◽

Keith Davids

Keyword(s):

Linear Process ◽

Task Environment ◽

Coach Education ◽

Development Framework ◽

Novel Approach ◽

Non Linear ◽

And Performance ◽

Learning Development ◽

High Level ◽

User Friendly

As it is appreciated that learning is a non-linear process – implying that coaching methodologies in sport should be accommodative – it is reasonable to suggest that player development pathways should also account for this non-linearity. A constraints-led approach (CLA), predicated on the theory of ecological dynamics, has been suggested as a viable framework for capturing the non-linearity of learning, development and performance in sport. The CLA articulates how skills emerge through the interaction of different constraints (task-environment-performer). However, despite its well-established theoretical roots, there are challenges to implementing it in practice. Accordingly, to help practitioners navigate such challenges, this paper proposes a user-friendly framework that demonstrates the benefits of a CLA. Specifically, to conceptualize the non-linear and individualized nature of learning, and how it can inform player development, we apply Adolph’s notion of learning IN development to explain the fundamental ideas of a CLA. We then exemplify a learning IN development framework, based on a CLA, brought to life in a high-level youth football organization. We contend that this framework can provide a novel approach for presenting the key ideas of a CLA and its powerful pedagogic concepts to practitioners at all levels, informing coach education programs, player development frameworks and learning environment designs in sport.

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Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00549-3 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Xiang Li ◽

Jianzheng Liu ◽

Jessica Baron ◽

Khoa Luu ◽

Eric Patterson

Keyword(s):

Focal Length ◽

Ground Truth ◽

Detection Methods ◽

Viewing Angle ◽

Deep Convolutional Neural Networks ◽

Landmark Detection ◽

High Performing ◽

Detection Techniques ◽

And Performance ◽

Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

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Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

Applied Sciences ◽

10.3390/app11125685 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5685

Author(s):

Hosam Aljihani ◽

Fathy Eassa ◽

Khalid Almarhabi ◽

Abdullah Algarni ◽

Abdulaziz Attaallah

Keyword(s):

Attack Detection ◽

Detection Methods ◽

Software Systems ◽

Distributed Software ◽

Detection Techniques ◽

Detection Systems ◽

Novel Approach ◽

Validation Experiment ◽

Critical Issues ◽

Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

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Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models

Organizational Research Methods ◽

10.1177/1094428121993228 ◽

2021 ◽

pp. 109442812199322

Author(s):

Ali Shamsollahi ◽

Michael J. Zyphur ◽

Ozlem Ozkok

Keyword(s):

Recent Work ◽

Dynamic Systems ◽

Quantitative Methods ◽

Simulated Dataset ◽

Impulse Responses ◽

Panel Models ◽

Long Run ◽

Short Run ◽

User Friendly ◽

Over Time

Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861 ). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods.

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Revisiting knowledge distillation for light-weight visual object detection

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211022877 ◽

2021 ◽

Vol 43 (13) ◽

pp. 2888-2898

Author(s):

Tianze Gao ◽

Yunfeng Gao ◽

Yu Li ◽

Peiyuan Qin

Keyword(s):

Object Detection ◽

Essential Element ◽

Detection Algorithm ◽

Positive Sample ◽

Detection Methods ◽

Visual Object ◽

Light Weight ◽

Model Compression ◽

Novel Approach ◽

Knowledge Distillation

An essential element for intelligent perception in mechatronic and robotic systems (M&RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.

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PathScore: a web tool for identifying altered pathways in cancer data

10.1101/067090 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen G. Gaffney ◽

Jeffrey P. Townsend

Keyword(s):

Web Application ◽

Somatic Mutations ◽

Supplementary Information ◽

Web Tool ◽

Cancer Data ◽

Link Type ◽

Novel Approach ◽

Supplementary Material ◽

User Friendly ◽

Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

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Enhancing Intelligent Anemia Detection via Unifying Global and Local Views of Conjunctiva Image with Two-Branch Neural Networks

10.21203/rs.3.rs-1170958/v1 ◽

2022 ◽

Author(s):

Lijuan Zheng ◽

Shaopeng Liu ◽

Senping Tian ◽

Jianhua Guo ◽

Xinpeng Wang ◽

...

Keyword(s):

Network Architecture ◽

Clinical Symptoms ◽

Detection Methods ◽

Testing Methods ◽

Neural Network Architecture ◽

Real World Application ◽

Algorithmic Framework ◽

Comparable Accuracy ◽

Global And Local ◽

Detection Effect

Abstract Anemia is one of the most widespread clinical symptoms all over the world, which could bring adverse effects on people's daily life and work. Considering the universality of anemia detection and the inconvenience of traditional blood testing methods, many deep learning detection methods based on image recognition have been developed in recent years, including the methods of anemia detection with individuals’ images of conjunctiva. However, existing methods using one single conjunctiva image could not reach comparable accuracy in anemia detection in many real-world application scenarios. To enhance intelligent anemia detection using conjunctiva images, we proposed a new algorithmic framework which could make full use of the data information contained in the image. To be concrete, we proposed to fully explore the global and local information in the image, and adopted a two-branch neural network architecture to unify the information of these two aspects. Compared with the existing methods, our method can fully explore the information contained in a single conjunctiva image and achieve more reliable anemia detection effect. Compared with other existing methods, the experimental results verified the effectiveness of the new algorithm.

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Contribution to a Mixed Assessment of Patient Safety Culture

10.21203/rs.3.rs-36536/v1 ◽

2020 ◽

Author(s):

Youcef Oussama Fourar ◽

Mebarek Djebabra ◽

Wissal Benhassine ◽

Leila Boubaker

Keyword(s):

Patient Safety ◽

Safety Culture ◽

Quantitative Methods ◽

Principal Component ◽

Patient Safety Culture ◽

International Standards ◽

Systematic Evaluation ◽

Maturity Model ◽

Combined Method ◽

Novel Approach

Abstract Purpose: The evaluation of patient safety culture is conducted using quantitative methods based on the use of questionnaires and qualitative ones focused on the deployment of cultural maturity models. These methods are known to suffer from certain major limits. This article aims to overcome the difficulties encountered by both methods and to propose a novel approach to the assessment of PSC. Methodology: The approach proposed in this article consists of applying a combined method, based on Principal Component Analysis (PCA) and K-means algorithm, to group together PSC dimensions into macro-dimensions whose exploitation allows to overcome the difficulties encountered with dimensional analysis of PSC and then, serve as a basic support for the development of a patient safety culture maturity model. Findings: The results of the combined method PCA / k-means shows that PSC dimensions can be grouped into three macro-dimensions that were capitalized in a first place using factors related to the development of PSC and in a second place to develop a quantitative maturity matrix that helped in the identification of PSC maturity levels.Originality: The merit of our proposal is to work towards a quali-quantitative evaluation of safety culture recommended by a good number of researchers but, to our knowledge, few or no studies are devoted to this hybrid or systematic evaluation of safety culture. Thus, the results can also be projected to implicate PSC actors and to frame the evaluation pf PSC maturity by international standards.

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A Novel Approach For Detection of Neurological Disorders through Electrical Potential Developed in Brain

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2751-2759 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2751

Author(s):

Mohd Suhaib Kidwai ◽

S. Hasan Saeed

Keyword(s):

Neurological Disorders ◽

Electrical Potential ◽

Mathematical Concept ◽

Recurrence Plots ◽

Eeg Signals ◽

Novel Approach ◽

User Friendly

<p>This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots</p>

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