A Novel Approach to Complex Human Activity Recognition

Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main goal is to recognize a person\u27s activity using different technologies such as cameras, motion sensors, location sensors, and time. Human activity recognition is important in many areas such as pervasive computing, artificial intelligence, human-computer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences. There are numerous ubiquitous and pervasive computing systems where users\u27 activities play an important role. The human activity carries a lot of information about the context and helps systems to achieve context-awareness. In the rehabilitation area, it helps with functional diagnosis and assessing health outcomes. Human activity recognition is an important indicator of participation, quality of life and lifestyle. There are two classes of human activities based on body motion and function. The first class, simple human activity, involves human body motion and posture, such as walking, running, and sitting. The second class, complex human activity, includes function along with simple human activity, such as cooking, reading, and watching TV. Human activity recognition is an interdisciplinary research area that has been active for more than a decade. Substantial research has been conducted to recognize human activities, but, there are many major issues still need to be addressed. Addressing these issues would provide a significant improvement in different aspects of the applications of the human activity recognition in different areas. There has been considerable research conducted on simple human activity recognition, whereas, a little research has been carried out on complex human activity recognition. However, there are many key aspects (recognition accuracy, computational cost, energy consumption, mobility) that need to be addressed in both areas to improve their viability. This dissertation aims to address the key aspects in both areas of human activity recognition and eventually focuses on recognition of complex activity. It also addresses indoor and outdoor localization, an important parameter along with time in complex activity recognition. This work studies accelerometer sensor data to recognize simple human activity and time, location and simple activity to recognize complex activity

A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data

Causal Discovery (CD) is the process of identifying the cause-effect relationships among the variables from data. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study we introduce the common terminologies in causal discovery, and provide a comprehensive discussion of the approaches designed to identify the causal edges in different settings. We further discuss some of the benchmark datasets available for evaluating the performance of the causal discovery algorithms, available tools to perform causal discovery readily, and the common metrics used to evaluate these methods. Finally, we conclude by presenting the common challenges involved in CD and also, discuss the applications of CD in multiple areas of interest

An Empirical Analysis of the Influencing Factors of Adoption of Mobile Health Services in Bangladesh Based on Extended UTAUT Model

The aim of this study was to examine the critical factors affecting the adoption of mobile technologies in healthcare system of Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical framework, this study incorporates perceived reliability, price value as new factors that reflects the user’s reliability, beliefs and monetary concerns in the acceptance of mHealth services in the context of Bangladesh. A cross sectional survey questionnaire was used to collect data from 296 participants from general mHealth users in Bangladesh. The results demonstrate that performance expectancy, effort expectancy, social influence, facilitating condition & perceived reliability had significant influence on the intention to use mHealth services in Bangladesh. Surprisingly, price value (p\u3e0.05) had no significant influence on adoption of mHealth services. The insights from this study could benefit mHealth services providers, agencies and policy maker in implementing more effective marketing strategies to increase the acceptability of this service. With the proposed model, it is possible to develop better mHealth services to meet the requirements of the common people based on widely available Smartphone

Consumer Adoption of Mobile Financial Services (MFS) in Bangladesh: A Randomized Conjoint Experiment

Mobile Financial Services (MFS) has become the new frontier in many countries. Since 2013, MFS in Bangladesh has risen steeply. The study aims to examine the MFS adoption behavior through the lens of modified Technology Acceptance Model (TAM) utilizing Randomized Conjoint Experiment for establishing a causal relationship between the attributes and adoption behavior. This study is the first attempt to encompass the causal relationship while investigating adoption behavior. Data were collected through a structured survey questionnaire at two districts in Bangladesh, including 2400 responses from 240 respondents. It was found that distance and cost per transaction has a significant impact, while ease of use has a moderately significant influence on customer's adoption behavior. However, the findings also concluded that social influence and trust have no significant influence. To both regulators and service providers, the findings will provide insights into the customer's point of view and industry demand as well. The study also included some suggestions for future investigations

Effects of spiritual intelligence from Islamic perspective on emotional intelligence

Purpose – The aim of this study is to develop a spiritual intelligence scale from an Islamic perspective. In addition, this research explores the relationship of spiritual intelligence from an Islamic perspective with emotional intelligence among the students of higher learning institutions in Malaysia. Design/methodology/approach – Data were collected from 250 students from different higher learning institutions in Malaysia. Findings – The findings of the study show the relationships of spiritual intelligence from an Islamic perspective and emotional intelligence. Statistically significant relationships were found between four dimensions of spiritual intelligence from an Islamic perspective (e.g. transcendental awareness, meaning of life, patience and forgiveness) and emotional intelligence. Originality/value – The contribution of human intelligence in the development higher learning institution is remarkable. Most research found on spiritual intelligence is from traditional and Western perspective with very limited studies found from an Islamic perspective. The purpose of the current study is to construct a spiritual intelligence from an Islamic perspective and empirically validate the items. The study also looks for relationships of spiritual intelligence from an Islamic perspective and emotional intelligence among the students of higher learning institutions

The effects of spiritual intelligence and its dimensions on organizational citizenship behaviou

Purpose: Organizational citizenship behaviour may exist among employees who have inner feelings of having better work experiences by using their spiritual experiences, and also to nurture these by creating meaningful ethical work environments. These phenomena have not been sufficiently studied especially in the context of recent corporate scandals and ethical violations. For this reason, this study seeks to enrich the understanding of relationship of spiritual intelligence and its sub constructs on employee citizenship behaviour among the employees who are working in manufacturing and service organization in Malaysia. Design/methodology/approach: This paper examines the effect of spiritual intelligence and its dimensions on organizational citizenship behaviour among the employees who are working in manufacturing and service industries in Malaysia. Data were collected from 112 employees of the organization from 10 manufacturing and 10 service organization in Peninsular Malaysia. Findings and Originality/value: Multiple regression analyses have revealed that employee spiritual intelligence plays an important role for generating citizenship behaviour among employees. The two important dimensions namely critical existential thinking and transcendental awareness of spiritual intelligence are having great effect on organizational citizenship behaviour. Research limitations/implications: Scholars can develop new research agenda first to identify the nature of effects it might have on employee’s performance which can boost the ultimate goal of the organization. Practical implications: Through the finding of this empirical study, it is hoped that it can provide some preliminary assessment and knowledge of the effects of spiritual intelligence of employees and how they relate to the OCB. This would be vital for industrial development by adding relevant policies regarding enhancing employees’ OCB. Social implications: This study has the capacity to enhance management awareness concerning recruiting people in terms of spiritual intelligence. People from different culture with high level of citizenship behavior can able to get the job. Originality/value: Organizational management may consider making appropriate decisions for nurturing and developing the relevant dimensions of this intelligence that are lacking among the employees in order to inculcate the spirit of OCB and develop a better work environment. Implications of the research findings for management scholars as well as for management professionals are discussed at the end.Peer Reviewe

Awareness of occupational hazards in learning organizations: knowledge sharing behavior and sense of spirituality perspective

Purpose – The purpose of this research is to investigate the significant antecedents that influence students’ awareness of occupational hazards (AOHs) in their respective institutions. The researchers proposed a theoretical model consisting of three dimensions: knowledge sharing behavior (KSB), sense of spirituality (SS) and awareness of occupational hazards (AOHs). Design/methodology/approach – This study targets students of different public and private higher learning institutions in Bangladesh with a total of 260 respondents, utilizing a survey questionnaire as the data collection instrument to test the proposed conceptual model. The structural equation modeling approach was used to test the proposed model. Findings – The results show that SS has a mediating effect on KSB and AOHs at higher learning institutions. Originality/value – The study contributes for first time to the theoretical novelty of the body of the existing literature in the domains of students’ KSB, SS and AOHs. The study also provides insight on future research directions by helping in identifying gaps in literature in this field and higher learning institutions in Bangladesh

Bifurcation analysis of phytoplankton-fish model through parametric control by fish mortality rate and food transfer efficiency

An Algae-zooplankton fish model is studied in this article. First the proposed model is evaluated for positive invariance and boundedness. Then,the Routh-Hurwitz parameters and the Lyapunov function are used to determine the presence of a positive interior steady state and the criteria for plankton model stability (both local and global). Taylor’s sequence is also used to discuss Hopf bifurcation and the stability of bifurcated periodic solutions. The model’s bifurcation analysis reveals that Hopf-bifurcation can occur when mortality rate and food transfer efficiency are used as bifurcation parameters. Finally, we use numerical simulation to validate the analytical results

Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect strategies leads to unrealistic counterfactual estimations. Such models are also prone to bias due to time-varying confoundedness. Further, the complex non-linearity in Earth science data makes it infeasible to perform causal inference using existing marginal structural techniques. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science