Heuristic Method to Improve Systematic Collection of Terminology

2016 ◽  
pp. 337-351
Author(s):  
Vineta Arnicane ◽  
Guntis Arnicans ◽  
Juris Borzovs
Keyword(s):  
Heuristic Method ◽  
Systematic Collection

A ‘truly amiable gentleman’: new light on the life and work of Marmaduke Tunstall (1743–1790) of Wycliffe, North Yorkshire

1998 ◽  
Vol 25 (2) ◽  
pp. 221-235 ◽  
Author(s):  
M.J. BOYD ◽  
L. JESSOP
Keyword(s):  
Published Data ◽  
The South ◽  
Systematic Collection

Marmaduke Tunstall (1743–1790) was a naturalist, antiquary and collector. Active in London during the 1760s and early 1770s, he built up an extensive Library and a Museum that was particularly notable for its systematic collection of British birds. Tunstall corresponded with several of the leading British naturalists, and with Linnaeus, and made his collections available for study to several authors. At the age of 33, Tunstall retired to a country estate at Wycliffe on the south bank of the Tees, where he spent the rest of his life. Newly-discovered information is incorporated with previously-published data, to provide a new account of Marmaduke Tunstall's life and activities, as a background to other studies on his family and his collections.

A Heuristic Method Based on Multi-Objective Optimization Concept for Solving RNA Multiple Alignment

2017 ◽  
Vol 10 (5) ◽  
pp. 371
Author(s):  
Arakil Chentoufi ◽  
Abdelhakim El Fatmi ◽  
Molay Ali Bekri ◽  
Said Benhlima ◽  
Mohamed Sabbane
Keyword(s):  
Heuristic Method ◽  
Multiple Alignment ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

2021 ◽  
Vol 11 (6) ◽  
pp. 2511
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R. Muhammad Atif Azad
Keyword(s):  
State Of The Art ◽  
Heuristic Method ◽  
Good Explanation ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Models ◽  
Boundary Values ◽  
Class Label ◽  
Input Space ◽  
Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map

2021 ◽  
Vol 1756 (1) ◽  
pp. 012005
Author(s):  
Bowen Yang ◽  
Lei Yuan ◽  
Jin Yan ◽  
Zhiming Ding ◽  
Zhi Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Heuristic Method ◽  
Temporal Characteristics

scholarly journals A SYSTEMATIC COLLECTION OF NATURAL INTERACTIONS FOR IMMERSIVE MODELING FROM BUILDING BLOCKS

2021 ◽  
Vol 1 ◽  
pp. 283-292
Author(s):  
Jakob Harlan ◽  
Benjamin Schleich ◽  
Sandro Wartzack
Keyword(s):  
User Interfaces ◽  
Building Blocks ◽  
Hand Tracking ◽  
Spatial Concepts ◽  
Natural User Interfaces ◽  
Modeling Systems ◽  
Block Based ◽  
Natural Interactions ◽  
Tracking Technology ◽  
Systematic Collection

AbstractThe increased availability of affordable virtual reality hardware in the last years boosted research and development of such systems for many fields of application. While extended reality systems are well established for visualization of product data, immersive authoring tools that can create and modify that data are yet to see widespread productive use. Making use of building blocks, we see the possibility that such tools allow quick expression of spatial concepts, even for non-expert users. Optical hand-tracking technology allows the implementation of this immersive modeling using natural user interfaces. Here the users manipulated the virtual objects with their bare hands. In this work, we present a systematic collection of natural interactions suited for immersive building-block-based modeling systems. The interactions are conceptually described and categorized by the task they fulfil.

scholarly journals Heuristic rank selection with progressively searching tensor ring network

2021 ◽  
Author(s):  
Nannan Li ◽  
Yu Pan ◽  
Yaran Chen ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compression Ratio ◽  
State Of The Art ◽  
Heuristic Method ◽  
Ring Networks ◽  
Ring Network ◽  
Narrow Region ◽  
Network Search ◽  
Deep Networks ◽  
Evolutionary Phase

AbstractRecently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.

Identity and change of a field: A literature analysis of the concept of social mobility

2021 ◽  
pp. 053901842110221
Author(s):  
Magda Nico
Keyword(s):  
Social Sciences ◽  
Literature Review ◽  
Life Course ◽  
Social Mobility ◽  
Golden Age ◽  
Literature Analysis ◽  
Scientific Publications ◽  
Diachronic Analysis ◽  
The Life Course ◽  
Systematic Collection

Social mobility is one of the concepts which is the most intrinsically bound to sociology. Hence, the diachronic analysis of this concept contributes to our understanding of sociology and the way that the discipline has changed, as it turned to individual social trajectories according to different topics. Aimed at contributing to this understanding, I’ve developed a literature review based on a systematic collection of the scientific publications in social sciences directly addressing social mobility. A database with conceptual and methodological variables was compiled (N=1054) and worked on. Distinct periods in the life course of this concept have been identified, with the emergence of a scattered concept (1920–1959), the golden age of social mobility (1960–1989), followed by a period of fragmentation and resistance (1990–2012). These three periods are characterized by different methodological and geographical hegemonies, flows and volumes of publications, and also by different tendencies and theoretical and disciplinary rivalries.

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