scholarly journals Robust Recognition and Segmentation of Human Actions Using HMMs with Missing Observations

2005 ◽  
Vol 2005 (13) ◽  
Author(s):  
Patrick Peursum ◽  
Hung H. Bui ◽  
Svetha Venkatesh ◽  
Geoff West
Keyword(s):  
Missing Observations ◽  
Human Actions ◽  
Robust Recognition

Discrimination of Complex Human Actions by Pigeons and Humans

2013 ◽  
Author(s):  
Robert G. Cook ◽  
Muhammad A. Qadri
Keyword(s):  
Human Actions

Intentions and their Effects on the Legality of Dispositions and Contracts

2002 ◽  
Vol 4 (1) ◽  
pp. 130-141
Author(s):  
Abdullah Muhammad al-Shami
Keyword(s):  
Basic Principle ◽  
Islamic Law ◽  
Human Action ◽  
Basic Principles ◽  
Human Actions ◽  
Special Place ◽  
Legal Contract ◽  
Islamic Jurisprudence ◽  
Legal Opinions ◽  
The Right

In Islamic law judgements on any human action are usually evaluated in terms of the intention involved. Accordingly, the rules of substantive issues have to be accommodated under the basic principles of Islamic jurisprudence. The understanding of these principles by the juristic scholar is highly rewarding because it will lead the muftī to the right path in deriving legal opinions from the original sources. The basic principle of Islamic jurisprudence, which stipulates that ‘all actions depend on intentions,’ has played an important role in the construction of Islamic jurisprudence. Moreover, this rule has a special place in the theory of Islamic legal contract. So what is the effect of intention in the validity of human actions and legal contracts? It is known that pure intention has significant effects on spiritual worship and legal contracts of transaction. It also gives guidance for earning rewards from Almighty Allah. This article concentrates on the effect of intention in perpetual worship, the concept of action and intention in Islamic legal works, the kind of contract with all its components, and the jurists' views on the effects of intention in human action and legal contract along with their discussion and counter-arguments.

Konsep dan Praktik Pelestarian Lingkungan dalam Hukum Islam

2020 ◽  
Author(s):  
Evra Willya ◽  
Sabil Mokodenseho ◽  
Muh. Idris ◽  
Nasruddin Yusuf
Keyword(s):  
Social Movements ◽  
Islamic Law ◽  
Well Being ◽  
Future Generations ◽  
Social Activities ◽  
Environment Pollution ◽  
Human Relations ◽  
Basic Principles ◽  
Human Actions ◽  
The Earth

In the Al Qur’an, Allah SWT describes the damages and destructions caused by human actions on land and sea. Therefore, humans are obliged to maintain and preserve their environment for future generations. This obligation aims at protecting their interests, due to the various beneficial sources of life attributed to humans existence on earth. In order to maintain the balance and harmony of human relations with nature, and to realize order and social well-being, Islamic Law upholds some basic principles of social activities, to guarantee an orderly, balanced, and harmonious life for the development and social movements life in a stable and orderly manner. Subsequently, environment pollution and damage to the earth are eradicated, thereby, restoring balance and harmony.

Theological Implications from Attachment Theory

2018 ◽  
Author(s):  
Natalia Marandiuc
Keyword(s):  
Attachment Theory ◽  
The Self ◽  
Human Actions ◽  
Perceived Safety ◽  
Human Subjectivity ◽  
Self Actualization

The chapter places theological anthropologies that focus on the connectedness of the self in dialogue with key findings and claims advanced by attachment theorists. One of the most amply researched and pragmatically employed frameworks in contemporary neuropsychology, attachment theory contends that human subjectivity is the product of human attachments. Attachment figures provide an environment of perceived safety within which and out of which the self can pursue other activities in freedom; should attachment needs remain unmet, human actions would be inhibited. Self-actualization depends upon secure attachments that home the self. In fact, the term “home” is a key technical concept for attachment theory: secure attachments constitute a secure home for the self.

scholarly journals The Importance of the Human Factor in Safety for the Transport of Dangerous Goods

2021 ◽  
Vol 18 (14) ◽  
pp. 7525
Author(s):  
Sylwia Agata Bęczkowska ◽  
Iwona Grabarek
Keyword(s):  
Human Error ◽  
Human Factor ◽  
Factor Model ◽  
Heuristic Methods ◽  
Simulation Studies ◽  
Optimal Route ◽  
Human Actions ◽  
Dangerous Goods ◽  
Undesirable Event ◽  
Transport Of Dangerous Goods

This article discusses the issues related to the safety for the transport of dangerous goods by road. Research on accidents in transport unambiguously points to the human factor, which is the most responsible for causing accidents. Determining the causes of driver unreliability in the human−vehicle−environment system requires thorough research. Unfortunately, in this case, experimental research with human involvement is limited in scope. This leaves modeling and simulation of the behavior of the human factor, i.e., the driver transporting dangerous goods. The human being, because of its complexity, is a challenging element to parameterize. The literature presents various attempts to model human actions. Herein, the authors used heuristic methods, specifically fuzzy set techniques, to build a human factor model. In these models, human actions were specified using a verbal or linguistic description. The specificity of the fuzzy sets allowed for “naturally” limiting the “precision” in describing human behavior. The model was built based on the author’s questionnaire and expert research, based on which individual features were selected. Then, the traits were assigned appropriate states. The output parameter of the model was λL—the intensity of human error. The obtained values of the intensity of the accident caused by the driver’s error were implemented into the author’s method of risk assessment. They constituted one of the factors determining the probability of an accident in the transport of dangerous goods, which allowed for determining the optimal route for the transport of these goods characterized by the lowest risk of an undesirable event on the route. The article presents the model’s assumptions, structure, and the features included in the model, all of which have the most significant influence on shaping the intensity of human error. The results of the simulation studies showed a diversified effect of the analyzed characteristics on the driver’s efficiency.

scholarly journals Discussion on Competition for Spatial Statistics for Large Datasets

2021 ◽  
Author(s):  
Roman Flury ◽  
Reinhard Furrer
Keyword(s):  
Spatial Statistics ◽  
Covariance Function ◽  
Likelihood Function ◽  
Large Datasets ◽  
Missing Observations ◽  
Covariance Model ◽  
Compactly Supported ◽  
Covariance Tapering ◽  
Simple Kriging ◽  
Estimated Parameters

AbstractWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.

scholarly journals Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 845
Author(s):  
Dongheun Han ◽  
Chulwoo Lee ◽  
Hyeongyeop Kang
Keyword(s):  
Activity Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Training Data ◽  
Measurement Unit ◽  
Gravitational Acceleration ◽  
The Neural Network ◽  
Typical Data ◽  
Robust Recognition ◽  
Gravity Acceleration

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).

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