scholarly journals An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6862
Author(s):  
Raslan Kain ◽  
Hazem Hajj
Keyword(s):  
Viterbi Algorithm ◽  
User Behavior ◽  
Optimization Approach ◽  
Hierarchical Approach ◽  
Efficient Operation ◽  
Context Recognition ◽  
Average Improvement ◽  
Reward Functions ◽  
Combined Impact ◽  
The Impact

Mobile devices and sensors have limited battery lifespans, limiting their feasibility for context recognition applications. As a result, there is a need to provide mechanisms for energy-efficient operation of sensors in settings where multiple contexts are monitored simultaneously. Past methods for efficient sensing operation have been hierarchical by first selecting the sensors with the least energy consumption, and then devising individual sensing schedules that trade-off energy and delays. The main limitation of the hierarchical approach is that it does not consider the combined impact of sensor scheduling and sensor selection. We aimed at addressing this limitation by considering the problem holistically and devising an optimization formulation that can simultaneously select the group of sensors while also considering the impact of their triggering schedule. The optimization solution is framed as a Viterbi algorithm that includes mathematical representations for multi-sensor reward functions and modeling of user behavior. Experiment results showed an average improvement of 31% compared to a hierarchical approach.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals A  Method to Diagnose, Improve, and Evaluate Children’s Learning Using Wearable Devices Such as Mobile Devices in the IoT Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-25
Author(s):  
Mohammad Moradi ◽  
Kheirollah Rahsepar Fard
Keyword(s):  
English Language ◽  
Educational Status ◽  
Field Method ◽  
Learning Performance ◽  
Educational Method ◽  
Number Of Children ◽  
Self Confidence ◽  
Average Improvement ◽  
Impact Of Technology ◽  
The Impact

Every day we see an increasing tendency to use technology in education. In recent years, the impact of technology on the education process has received much attention. One of the important effects of technology is that it increases children’s motivation and self-confidence and increases group collaboration. The purpose of this paper is to transform the traditional classroom into a modern classroom in order to increase the ease and efficiency of the teaching process. The method includes phases of diagnosis and improvement. In the diagnose phase, the classroom is equipped with modern items such as Internet of Things (IoT) and game-based learning. In the improvement phase, the field method is used to extract and weight the effective criteria in improving the educational status. The proposed method has been tested on two English language kindergartens. The children tested were in the age group of 8 to 10 years. In the implementation of the proposed educational method in the first English language kindergarten, the average improvement of education and learning of children has almost doubled, which has been maintained by doubling the number of children tested in the implementation of the proposed educational method in the second English language kindergarten. As a result, the proposed educational method can increase the learning performance of children.

scholarly journals Topology Optimization of Deformable Bodies with Linear Dynamic Impact and Frictionless Contact Condition

2021 ◽  
Vol 11 (22) ◽  
pp. 10518
Author(s):  
Gil-Eon Jeong
Keyword(s):  
Topology Optimization ◽  
Optimization Approach ◽  
Dynamic Impact ◽  
Frictionless Contact ◽  
Linear Dynamic ◽  
Highly Nonlinear ◽  
Simple Part ◽  
Deformable Bodies ◽  
Increasing Demand ◽  
The Impact

There has been an increasing demand for the design of an optimum topological layout in several engineering fields for a simple part, along with a system that considers the relative behaviors between adjacent parts. This paper presents a method of designing an optimum topological layout to achieve a linear dynamic impact and frictionless contact conditions in which relative behaviors can be observed between adjacent deformable parts. The solid isotropic method with penalization (SIMP) method is used with an appropriate filtering scheme to obtain an optimum topological layout. The condensed mortar method is used to handle the non-matching interface, which inevitably occurs in the impact and contact regions, since it can easily apply the existing well-known topology optimization approach even in the presence of a non-matching interface. The validity of the proposed method is verified through a numerical example. In the future, the proposed optimization approach will be applied to more general and highly nonlinear non-matching interface problems, such as friction contact and multi-physics problems.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

scholarly journals A Markov model of urban evolution: Neighbourhood change as a complex process

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245357
Author(s):  
Daniel Silver ◽  
Thiago H. Silva
Keyword(s):  
Markov Model ◽  
Markov Models ◽  
Past Research ◽  
Hierarchical Approach ◽  
Evolutionary Trajectory ◽  
Complex Process ◽  
Trajectories Of Change ◽  
Neighbourhood Change ◽  
The Impact ◽  
Complexity Theories

This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada. First, we classify Toronto neighbourhoods into distinct groups using established geodemographic segmentation techniques, a relatively novel application in this geographic setting. Extending previous studies, we pursue a hierarchical approach to classifying neighbourhoods that situates many neighbourhood types within the city’s broader structure. Our hierarchical approach is able to incorporate a richer set of types than most past research and allows us to study how neighbourhoods’ positions within this hierarchy shape their trajectories of change. Second, we use Markov models to identify generative processes that produce patterns of change in the city’s distribution of neighbourhood types. Moreover, we add a spatial component to the Markov process to uncover the extent to which change in one type of neighbourhood depends on the character of nearby neighbourhoods. In contrast to the few studies that have explored Markov models in this research tradition, we validate the model’s predictive power. Third, we demonstrate how to use such models in theoretical scenarios considering the impact on the city’s predicted evolutionary trajectory when existing probabilities of neighbourhood transitions or distributions of neighbourhood types would hypothetically change. Markov models of transition patterns prove to be highly accurate in predicting the final distribution of neighbourhood types. Counterfactual scenarios empirically demonstrate urban complexity: small initial changes reverberate throughout the system, and unfold differently depending on their initial geographic distribution. These scenarios show the value of complexity as a framework for interpreting data and guiding scenario-based planning exercises.

scholarly journals Association rule hiding using integer linear programming

2021 ◽  
Vol 11 (4) ◽  
pp. 3451
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Association Rule ◽  
Real Life ◽  
Linear Program ◽  
Integer Linear Program ◽  
Optimization Approach ◽  
Integer Linear Program Formulation ◽  
The Impact

<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

scholarly journals Assessment of Building Automation and Control Systems in Danish Healthcare Facilities in the COVID-19 Era

2022 ◽  
Vol 12 (1) ◽  
pp. 427
Author(s):  
Jeanette Maria Pedersen ◽  
Farah Jebaei ◽  
Muhyiddine Jradi
Keyword(s):  
Air Quality ◽  
Ventilation System ◽  
Hot Water ◽  
Building Automation ◽  
Efficient Operation ◽  
Healthcare Facilities ◽  
Automation And Control ◽  
And Control ◽  
Indoor Conditions ◽  
The Impact

A well-designed and properly operated building automation and control system (BACS) is key to attaining energy-efficient operation and optimal indoor conditions. In this study, three healthcare facilities of a different type, age, and use are considered as case studies to investigate the functionalities of BACS in providing optimal air quality and thermal comfort. IBACSA, the first-of-its-kind instrument for BACS assessment and smartness evaluation, is used to evaluate the current systems and their control functionalities. The BACS assessment is reported and analyzed. Then, three packages of improvements were implemented in the three cases, focusing on (1) technical systems enhancement, (2) indoor air quality and comfort, and (3) energy efficiency. It was found that the ventilation system domain is the best performer in the three considered cases with an overall score of 52%, 89% and 91% in Case A, B, and C,, respectively. On the other hand, domestic hot water domain scores are relatively low, indicating that this is an area where Danish healthcare facilities need to provide more concentration on. A key finding indicated by the assessment performed is that the three buildings score relatively very low when it comes to the impact criteria of energy flexibility and storage.

scholarly journals INTERNET MARKETING SOLUTIONS TRANSFORMATION DUE TO CHANGES IN CONSUMER'S PERCEPTION OF INFORMATION

2021 ◽  
Author(s):  
Igors Babics
Keyword(s):  
User Behavior ◽  
Online Communication ◽  
Marketing Research ◽  
Modern Society ◽  
Internet Marketing ◽  
Online Marketing ◽  
Scientific Article ◽  
Human Thinking ◽  
Internet User Behavior ◽  
The Impact

We have outlined the main aspects of the modern socio-economic space that have led to transformation not only in the business sector but also in human thinking. We have examined the aspects of the studied problem expressed in modern scientific works and explained the need for further study of changes in the thinking processes of consumers in the field of transformation of applied Internet marketing solutions for the requests of Internet users. We have analyzed the dynamics and trends of changes in Internet user behavior, thereby identifying the key aspects that should be taken into account when companies create online marketing strategies. We have proposed a list of steps to optimize the marketing strategy of the business in line with new realities.The relevance of the study is due to the social processes of modern society resulting in the tendency to transform consumers' thinking. COVID-19 and self-isolation have had an impact on this phenomenon, accelerating the massive changeover to online communication and online shopping.The goal of this article is to describe the results of a study of changes in consumer thinking in connection with the transformation of realities caused by the global pandemic.The scientific novelty of the study lies in highlighting the peculiarities of information perception by modern consumers associated with the global pandemic, and in substantiating the ways of transforming Internet marketing solutions for companies in an altered reality.The theoretical importance of the research lies in a better understanding of the reasons and features of the transformation of information perception by consumers in modern realities, as well as in the analysis of scientific works to study the impact of informatization and computerization on society thinking, which can be used to study this component in marketing research, including in online marketing. This is the practical value of this work.The practical value of the study lies in identifying the features of the transformation of the thinking of modern consumers through visitors to the website of Cita Lieta ltd. at ceanocosmetics.com.Like in any scientific article, this one has its research limitations. The author explores the transformation of consumer thinking change using the data from the website analytics of one company in a particular niche.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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