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Topic Review
Signalling (Economics)
In contract theory, signalling (or signaling; see spelling differences) is the idea that one party (the agent) credibly conveys some information about itself to another party (the principal). Although signalling theory was initially developed by Michael Spence based on observed knowledge gaps between organisations and prospective employees, its intuitive nature led it to be adapted to many other domains, such as Human Resource Management, business, and financial markets. In Spence's job-market signaling model, (potential) employees send a signal about their ability level to the employer by acquiring education credentials. The informational value of the credential comes from the fact that the employer believes the credential is positively correlated with having the greater ability and difficulty for low ability employees to obtain. Thus the credential enables the employer to reliably distinguish low ability workers from high ability workers. The concept of signaling is also applicable in competitive altruistic interaction, where the capacity of the receiving party is limited.
  • 546
  • 21 Nov 2022
Topic Review
Value Co-Creation in Digital Innovation Ecosystems
The innovation ecosystem guides the transition from individual value creation to multi-actor value co-creation by coordinating the interests of multiple parties for cross-border cooperation and enhancing the efficiency of technological innovation and resource integration in the system.
  • 546
  • 16 May 2023
Topic Review
Classification Algorithms for Unifloral Honeys
Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst. 
  • 545
  • 05 Jul 2021
Topic Review
Genome by Multidimensional Scaling
The positions of enhancers and promoters on genomic DNA remain poorly understood. Chromosomes cannot be observed during the cell division cycle because the genome forms a chromatin structure and spreads within the nucleus. However, high-throughput chromosome conformation capture (Hi-C) measures the physical interactions of genomes. In previous studies, DNA extrusion loops  were directly derived from Hi-C heat maps. By using Multidimensional Scaling (MDS), we can easily locate enhancers and promoters more precisely.
  • 545
  • 31 Oct 2021
Topic Review
Cyber–Physical Systems Forensics
Cyber–Physical Systems (CPS) connect the physical world (systems, environments, and humans) with the cyber world (software, data, etc.) to intelligently enhance the operational environment they serve. CPS are distributed software and hardware components embedded in the physical world and possibly attached to humans. CPS are vulnerable to security risks, which requires incorporating appropriate forensics measures in the design and operations of these systems.
  • 545
  • 17 Dec 2021
Topic Review
Mill Architecture
The Mill architecture is a novel belt machine-based computer architecture for general-purpose computing. It has been under development since about 2003 by Ivan Godard and his startup Mill Computing, Inc., formerly named Out Of The Box Computing, in East Palo Alto, California. Mill Computing claims it has a "10x single-thread power/performance gain over conventional out-of-order superscalar architectures" but "runs the same programs, without rewrite". Mill Computing was founded by persons who formerly worked together on a family of digital signal processors (DSPs), the Philips Trimedia.
  • 545
  • 10 Oct 2022
Topic Review
Geoinformatics
The geoinformatics is the programming of applications, spatial data structures, and analyses of objects and space-time phenomena referred to the Earth surface, together with designing, developing, and maintaining the software and web services intended for modelling and analysing the spatial data.
  • 545
  • 19 Jun 2023
Topic Review
Sensor Data Fusion Algorithms
Sensor Data Fusion (SDT) algorithms and methods have been utilised in many applications ranging from automobiles to healthcare systems. They can be used to design a redundant, reliable, and complementary system with the intent of enhancing the system’s performance. SDT can be multifaceted, involving many representations such as pixels, features, signals, and symbols.
  • 545
  • 12 Dec 2023
Topic Review
KinectGaitNet
Gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. Researchers proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem.
  • 544
  • 20 Apr 2022
Topic Review
Multiscale-Deep-Learning Applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task.
  • 544
  • 26 Oct 2022
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