Header minimieren

Prof. Dr. Mouzhi Ge

Google Scholar: https://scholar.google.com/citations?user=tFLaZJYAAAAJ DBLP: https://dblp.uni-trier.de/pid/35/6880.html Scopus: https://www.scopus.com/authid/detail.uri?authorId=36666262900

Fakultät European Campus Rottal-Inn

Professorinnen und Professoren

Professor

Leiter der Forschungsgruppe Data Science und Intelligente Systeme

AA 221

0991/3615-8883

Publikationen

Sortierung:

Zeitschriftenartikel

F. Gedikli
Mouzhi Ge
D. Jannach

Explaining Online Recommendations Using Personalized Tag Clouds.

In: icom (vol. 10) , pg. 3-10

(2011)

DOI: 10.1524/icom.2011.0002

Abstract anzeigen

Recommender systems are sales-supporting applications that are usually integrated into online shops and are designed to point the visitor to products or services she or he might be interested in but has not bought yet. In the last decade, many techniques have been developed to improve the predictive accuracy of such systems. However, there are also factors other than accuracy that infl uence the user-perceived quality of such a system. In particular, system-generated explanations as to why a certain item has been recommended have shown to be a valuable tool to improve both the user's satisfaction and the system's effi ciency. This paper reports the results of a fi rst user study which was conducted to evaluate whether personalized tag clouds are an appropriate means to visually explain recommendations. The evaluation reveals that using tag clouds as explanation mechanism leads to higher user satisfaction and recommendation effi ciency than previous keyword-style explanations.

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
M. Helfert
D. Jannach

Information quality assessment: validating measurement dimensions and processes.

In:
J. Nandhakumar
M. Rossi
V. Tuunainen

pg. 75

(2011)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
F. Gedikli
D. Jannach

Placing High-Diversity Items in Top-N Recommendation Lists.

In:
S. Singh Anand
D. Jannach

(2011)

Beitrag in Sammelwerk/Tagungsband

F. Gedikli
Mouzhi Ge
D. Jannach

Understanding Recommendations by Reading the Clouds.

In:
T. Setzer
C. Huemer

Berlin Heidelberg: Springer-Verlag pg. 196-208

(2011)

Beitrag in Sammelwerk/Tagungsband

A. Borek
A. Parlikad
M. Helfert
Mouzhi Ge

An Information Oriented Framework for Relating IS/IT Resources and Business Value.

In:
X. Li
J. Zhang
Z. Zhang
J. Cordeiro
R. Zhang

SCITEPRESS – Science and Technology Publications, Lda pg. 358-367

(2011)

Beitrag in Sammelwerk/Tagungsband

D. Jannach
M. Zanker
Mouzhi Ge
M. Gröning

Recommender Systems in Computer Science and Information Systems - A Landscape of Research.

In:
P. Lops
C. Huemer

Berlin: Springer pg. 76-87

(2012)

Beitrag in Sammelwerk/Tagungsband

A. Borek
M. Helfert
Mouzhi Ge
A. Parlikad

IS/IT Resources and Business Value: Operationalization of an Information Oriented Framework.

In:
J. Filipe
J. Zhang
Z. Zhang
J. Cordeiro
R. Zhang

Berlin: Springer pg. 420-434

(2012)

Beitrag in Sammelwerk/Tagungsband

B. Rodriguez-Castro
Mouzhi Ge
M. Hepp

Alignment of Ontology Design Patterns: Class As Property Value, Value Partition and Normalisation.

In:
H. Panetto
S. Pearson
S. Bergamaschi
X. Zhou
P. Dadam
T. Dillon
R. Meersman
S. Rinderle-Ma
A. Ferscha
I. Cruz

Springer Nature pg. 682-699

(2012)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
D. Jannach
F. Gedikli
M. Hepp

Effects of the Placement of Diverse Items in Recommendation Lists.

In:
L. Maciaszek

SCITEPRESS – Science and Technology Publications, Lda pg. 201-208

(2012)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
D. Jannach
F. Gedikli

Bringing Diversity to Recommendation Lists - An Analysis of the Placement of Diverse Items.

In:
L. Maciaszek

SCITEPRESS – Science and Technology Publications, Lda pg. 293-305

(2012)

Beitrag in Sammelwerk/Tagungsband

M. Braunhofer
M. Elahi
Mouzhi Ge
F. Ricci
T. Schievenin

STS: Design of Weather-Aware Mobile Recommender Systems in Tourism.

In:
B. Carolis
C. Gena

(2013)

Beitrag in Sammelwerk/Tagungsband

K. Stoll
Mouzhi Ge
M. Hepp

Understanding the Impact of E-Commerce Software on the Adoption of Structured Data on the Web.

In:
W. Abramowicz

Berlin: Springer pg. 100-112

(2013)

European Campus Rottal-Inn

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
M. Helfert

Cost and Value Management for Data Quality.

In:
S. Sadiq

Berlin, Heidelberg: Springer Berlin Heidelberg, Springer, Imprint pg. 75-92

(2013)

European Campus Rottal-Inn

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
M. Helfert

A Design Science Oriented Framework for Experimental Research in Information Quality.

In:
C. Yu
W. Li
K. Liu
S. Gulliver

Springer Berlin Heidelberg pg. 145-154

(2014)

Beitrag (Tagungsband)

Proceedings of the First International Workshop on Decision Making and Recommender Systems (DMRS2014).

(2014)

Beitrag in Sammelwerk/Tagungsband

M. Braunhofer
M. Elahi
Mouzhi Ge
F. Ricci

Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems.

In:
P. Zaphiris
A. Ioannou

Cham [usw.]: Springer pg. 105-116

(2014)

Zeitschriftenartikel

F. Gedikli
D. Jannach
Mouzhi Ge

How should I explain? A comparison of different explanation types for recommender systems.

In: International Journal of Human-Computer Studies (vol. 72) , pg. 367-382

(2014)

DOI: 10.1016/j.ijhcs.2013.12.007

Abstract anzeigen

Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system. In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way. In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.

Beitrag in Sammelwerk/Tagungsband

M. Elahi
Mouzhi Ge
F. Ricci
D. Massimo
S. Berkovsky

Interactive Food Recommendation for Groups.

In:
L. Chen
J. Mahmud

(2014)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
F. Ricci
D. Massimo

Health-aware Food Recommender System.

In:
M. Zanker
H. Werthner
J. Golbeck
G. Semeraro

pg. 333-334

DOI: 10.1145/2792838.2796554

(2015)

Zeitschriftenartikel

Mouzhi Ge
M. Helfert

Impact of Information Quality on Supply Chain Decisions.

In: Journal of Computer Information Systems (vol. 53) , pg. 59-67

(2015)

DOI: 10.1080/08874417.2013.11645651

Abstract anzeigen

A number of studies suggest that making correct decisions depends on high-quality information; how information quality affects decision-making is still not fully understood. Following the multi-dimensional view of information quality, this paper investigates the effects of information accuracy, completeness, and consistency on decision-making. Results show that information accuracy and completeness affect decision quality significantly. Although the effect of information consistency on decision quality appears to be non-significant, consistency of information may intensify the contribution of accuracy, indicating that information accuracy and consistency influence decision quality jointly.

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
M. Elahi
I. Fernaández-Tobías
F. Ricci
D. Massimo

Using Tags and Latent Factors in a Food Recommender System.

In:
F. Grasso
P. Kostkova

pg. 105-112

DOI: 10.1145/2750511.2750528

(2015)

Beitrag in Sammelwerk/Tagungsband

M. Elahi
Mouzhi Ge
F. Ricci
I. Fernández-Tobías
S. Berkovsky
D. Massimo

Interaction Design in a Mobile Food Recommender System.

In:
J. ODonovan
P. Lops
P. Brusilovsky
G. Semeraro
N. Tintarev
A. Felfernig

pg. 49-52

(2015)

Beitrag (Tagungsband)

Proceedings of the 2nd International Workshop on Decision Making and Recommender Systems.

(vol. 1533)

(2015)

Beitrag in Sammelwerk/Tagungsband

A. Khalilijafarabad
M. Helfert
Mouzhi Ge

Developing a Data Quality Research Taxonomy - an organizational perspective.

In:
M. Piattini
A. Carretero
I. Caballero

pg. 176-186

(2016)

Beitrag in Sammelwerk/Tagungsband

M. Helfert
Mouzhi Ge

Big Data Quality - Towards an Explanation Model.

In:
M. Piattini
A. Carretero
I. Caballero

pg. 16-23

(2016)

Beitrag in Sammelwerk/Tagungsband

P. Štěpánek
Mouzhi Ge
L. Walletzký

IT-Enabled Digital Service Design Principles - Lessons Learned from Digital Cities.

In:
M. Themistocleous
V. Morabito

Cham, Switzerland: Springer pg. 186-196

(2017)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
T. Chondrogiannis

Assessing the Quality of Spatio-Textual Datasets in the Absence of Ground Truth.

In:
S. Rizzi
J. Darmont
R. Wrembel
K. Nørvåg
J. Gamper
G. Papadopoulos
M. Kirikova

Cham: Springer International Publishing, Springer, Imprint pg. 12-20

(2017)

Beitrag in Sammelwerk/Tagungsband

A. Kobusinska
A. Wolski
J. Brzezinski
Mouzhi Ge

P2P Web Browser Middleware to Enhance Service Oriented Computing — Analysis and Evaluation.

In:
IEEE Computer Society

pg. 58-65

DOI: 10.1109/SOCA.2017.16

(2017)

Zeitschriftenartikel

Mouzhi Ge
F. Persia

A Survey of Multimedia Recommender Systems: Challenges and Opportunities.

In: International Journal of Semantic Computing (vol. 11) , pg. 411-428

(2017)

DOI: 10.1142/S1793351X17500039

Abstract anzeigen

Multimedia information has been extensively growing from a variety of sources such as cameras or video recorders. In order to select the useful multimedia objects, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia objects, it is challenging to provide effective multimedia recommendations. In this paper, we therefore conduct a survey in both the multimedia information system and recommender system communities. We further focus on the works that span the two communities, especially the research on multimedia recommender systems. Based on our review, we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights of how to perform the follow-up research.

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
T. OBrien
M. Helfert

Predicting Data Quality Success - The Bullwhip Effect in Data Quality.

In:
A. Chaudhuri
B. Johansson
F. Sudzina
C. Møller

Cham: Springer International Publishing, Springer, Imprint pg. 157-165

(2017)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
F. Persia

Research Challenges in Multimedia Recommender Systems.

In:
IEEE Computer Society

pg. 344-347

DOI: 10.1109/ICSC.2017.31

(2017)

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova
S. Rakrak
S. Raghay
T. Pitner

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm.

In: Journal of Sensor and Actuator Networks (JSAN) (vol. 6) , pg. 25

(2017)

DOI: 10.3390/jsan6040025

Abstract anzeigen

Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers’ computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process.

Beitrag in Sammelwerk/Tagungsband

D. Massimo
M. Elahi
Mouzhi Ge
F. Ricci

Item Contents Good, User Tags Better: Empirical Evaluation of a Food Recommender System.

In:
F. Cena
M. Bieliková
E. Herder
M. Desmarais

ACM pg. 373-374

(2017)

Zeitschriftenartikel

Mouzhi Ge
H. Bangui
B. Buhnova

Big Data for Internet of Things: A Survey.

In: Future Generation Computer Systems (vol. 87) , pg. 601-614

(2018)

DOI: 10.1016/j.future.2018.04.053

Abstract anzeigen

With the rapid development of the Internet of Things (IoT), Big Data technologies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is extensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data approaches in each IoT domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.

Beitrag in Sammelwerk/Tagungsband

M. Popescu
Mouzhi Ge
M. Helfert

The Social Media Perception and Reality – Possible Data Quality Deficiencies between Social Media and ERP.

In:
Institute of Electrical and Electronics Engineers, Inc.

pg. 198-204

DOI: 10.5220/0006788801980204

(2018)

Zeitschriftenartikel

Mouzhi Ge
F. Persia

A Generalized Evaluation Framework for Multimedia Recommender Systems.

In: International Journal of Semantic Computing (vol. 12) , pg. 541-557

(2018)

DOI: 10.1142/S1793351X18500046

Abstract anzeigen

With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.

Beitrag in Sammelwerk/Tagungsband

Q. Yang
Mouzhi Ge
M. Helfert

Data Quality Problems in TPC-DI Based Data Integration Processes.

Springer International Publishing pg. 57-73

(2018)

Beitrag in Sammelwerk/Tagungsband

Q. Yang
Mouzhi Ge
M. Helfert

Guildelines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark.

Springer International Publishing pg. 135-144

(2018)

Zeitschriftenartikel

Mouzhi Ge
V. Dohnal

Quality Management in Big Data.

In: Informatics (vol. 5) , pg. 19

(2018)

DOI: 10.3390/informatics5020019

Abstract anzeigen

Due to the importance of quality issues in Big Data, Big Data quality management has attracted significant research attention on how to measure, improve and manage the quality for Big Data. This special issue in the Journal of Informatics thus tends to address the quality problems in Big Data as well as promote further research for Big Data quality. Our editorial describes the state-of-the-art research challenges in the Big Data quality research, and highlights the contributions of each paper accepted in this special issue.

Beitrag in Sammelwerk/Tagungsband

P. Štěpánek
Mouzhi Ge

Validation and Extension of the Smart City Ontology.

In:
Institute of Electrical and Electronics Engineers, Inc.

pg. 406-413

DOI: 10.5220/0006818304060413

(2018)

Beitrag in Sammelwerk/Tagungsband

H. Bangui
Mouzhi Ge
B. Buhnova

Exploring Big Data Clustering Algorithms for Internet of Things Applications.

pg. 269-276

DOI: 10.5220/0006773402690276

(2018)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
F. Persia

Evaluation in Multimedia Recommender Systems: A Practical Guide.

pg. 294-297

DOI: 10.1109/ICSC.2018.00050

(2018)

Beitrag in Sammelwerk/Tagungsband

F. Persia
Mouzhi Ge
D. DAuria

How to Exploit Recommender Systems in Social Media.

pg. 537-541

DOI: 10.1109/IRI.2018.00085

(2018)

Beitrag in Sammelwerk/Tagungsband

T. Chondrogiannis
Mouzhi Ge

Inferring ratings for custom trips from rich GPS traces.

In:
Yaron K.
M. Renz
Tamraparni Dasu
P. Bouros
D. Sacharidis

pg. 1-4

DOI: 10.1145/3356994.3365502

(2019)

Beitrag in Sammelwerk/Tagungsband

Q. Yang
Mouzhi Ge
M. Helfert

Analysis of Data Warehouse Architectures: Modeling and Classification.

In:
Institute of Electrical and Electronics Engineers Inc.

pg. 604-611

DOI: 10.5220/0007728006040611

(2019)

Beitrag in Sammelwerk/Tagungsband

D. DAuria
Mouzhi Ge
F. Persia

Exploiting Recommender Systems in Collaborative Healthcare.

In:
K.-K. Choo
J. Hong
C. Esposito

Springer International Publishing pg. 71-82

(2019)

Beitrag in Sammelwerk/Tagungsband

H. Bangui
Mouzhi Ge
B. Buhnova

Quality Management for Big 3D Data Analytics: A Case Study of Protein Data Bank.

pg. 286-293

DOI: 10.5220/0007717402860293

(2019)

Beitrag in Sammelwerk/Tagungsband

H. Bangui
Mouzhi Ge
B. Buhnova

Quality Management for Big 3D Data Analytics: A Case Study of Protein Data Bank.

pg. 286-293

DOI: 10.5220/0007717402860293

(2019)

Beitrag in Sammelwerk/Tagungsband

L. Trang
H. Bangui
Mouzhi Ge
B. Buhnova

Scaling Big Data Applications in Smart City with Coresets.

pg. 357-363

DOI: 10.5220/0007958803570363

(2019)

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova

A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things.

In: International Journal of Organizational and Collective Intelligence (vol. 9) , pg. 16-30

(2019)

DOI: 10.4018/IJOCI.2019040102

Abstract anzeigen

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
S. Chren
B. Rossi
T. Pitner

Data Quality Management Framework for Smart Grid Systems.

In:
Corchuelo R.
Abramowicz W.

Cham, Switzerland: Springer pg. 299-310

DOI: 10.1007/978-3-030-20482-2_24

(2019)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
F. Persia

Factoring Personalization in Social Media Recommendations.

Piscataway, NJ: IEEE pg. 344-347

(2019)

Beitrag in Sammelwerk/Tagungsband

S. Chren
B. Rossi
B. Buhnova
Mouzhi Ge
T. Pitner

Industrial Involvement in Information System Education: Lessons Learned from a Software Quality Course.

In:
C. Barry
A. Siarheyeva
C. Schneider
H. Linger
M. Lang

Toulon, France: ISEN Yncréa Méditerranée

(2019)

Beitrag in Sammelwerk/Tagungsband

B. Mbarek
Mouzhi Ge
T. Pitner

Self-adaptive RFID Authentication for Internet of Things.

In:
M. Takizawa
F. Xhafa
L. Barolli
T. Enokido

pg. 1094-1105

(2019)

Beitrag in Sammelwerk/Tagungsband

T. Chondrogiannis
Mouzhi Ge

Inferring ratings for custom trips from rich GPS traces.

In:
Y. Kanza
M. Renz
D. Tamraparni
B. Panagiotis
D. Sacharidis

pg. 4:1-4:4

DOI: 10.1145/3356994.3365502

(2019)

Beitrag in Sammelwerk/Tagungsband

L. Walletzky
L. Carubbo
Mouzhi Ge

Modelling Service Design and Complexity for Multi-contextual Applications in Smart Cities.

pg. 101-106

DOI: 10.1109/ICSTCC.2019.8885800

(2019)

Beitrag in Sammelwerk/Tagungsband

B. Mbarek
Mouzhi Ge
T. Pitner

Blockchain-Based Access Control for IoT in Smart Home Systems.

In:
I. Khalil
S. Hartmann
G. Kotsis
A. Tjoa
J. Küng

Springer International Publishing pg. 17-32

(2020)

Beitrag in Sammelwerk/Tagungsband

M. Elahi
N. El Ioini
A. Alexander Lambrix
Mouzhi Ge

Exploring Personalized University Ranking and Recommendation.

In:
C. Gena
R. Burke
I. Torre
T. Kuflik

pg. 6-10

DOI: 10.1145/3386392.3397590

(2020)

Beitrag in Sammelwerk/Tagungsband

L. Walletzký
F. Romanovská
A. Toli
Mouzhi Ge

Research Challenges of Open Data as a Service for Smart Cities.

SCITEPRESS – Science and Technology Publications, Lda pg. 468-472

(2020)

Zeitschriftenartikel

Mouzhi Ge
W. Lewoniewski

Developing the Quality Model for Collaborative Open Data.

In: Procedia Computer Science (vol. 176) , pg. 1883-1892

(2020)

DOI: 10.1016/j.procs.2020.09.228

Abstract anzeigen

Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

Beitrag in Sammelwerk/Tagungsband

H. Bangui
Mouzhi Ge
B. Buhnova

Improving Big Data Clustering for Jamming Detection in Smart Mobility.

Cham, Switzerland: Springer Nature

(2020)

Zeitschriftenartikel

M. Macak
Mouzhi Ge
B. Buhnova

A Cross-Domain Comparative Study of Big Data Architectures.

In: International Journal of Cooperative Information Systems (vol. 29) , pg. 2030001

(2020)

DOI: 10.1142/S0218843020300016

Abstract anzeigen

Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

Beitrag in Sammelwerk/Tagungsband

Q. Yang
Mouzhi Ge
M. Helfert

Developing Reliable Taxonomic Features for Data Warehouse Architectures.

pg. 241-249

DOI: 10.1109/CBI49978.2020.00033

(2020)

Beitrag in Sammelwerk/Tagungsband

F. Persia
D. DAuria
Mouzhi Ge

Improving Learning System Performance with Multimedia Semantics.

pg. 238-241

DOI: 10.1109/ICSC.2020.00050

(2020)

Zeitschriftenartikel

B. Mbarek
Mouzhi Ge
T. Pitner

An Efficient Mutual Authentication Scheme for Internet of Things.

In: Internet of Things (vol. 9) , pg. 100160

(2020)

DOI: 10.1016/j.iot.2020.100160

Abstract anzeigen

The Internet of Things (IoT) is developed to facilitate the connections and data sharing among people, devices, and systems. Among the infrastructural IoT techniques, Radio Frequency IDentification (RFID) has been used to enable the proliferation and communication in IoT networks. However, the RFID techniques usually suffer from security issues due to the inherent weaknesses of underlying wireless radio communications. One of the main security issues is the authentication vulnerability from the jamming attack. In order to tackle the vulnerabilities of key updating algorithms, this paper therefore proposes an efficient authentication scheme based on the self-adaptive and mutual key updating. Furthermore, we evaluate the performance and applicability of our solution with a thorough simulation by taking into account the energy consumption, authentication failure rate and authentication delay. The feasibility and applicability are demonstrated by implementing the proposed authentication scheme in smart home IoT systems.

Beitrag in Sammelwerk/Tagungsband

B. Mbarek
Mouzhi Ge
T. Pitner

Enhanced network intrusion detection system protocol for internet of things.

In:
Association for Computing Machinery

pg. 1156-1163

(2020)

Zeitschriftenartikel

F. Persia
G. Pilato
Mouzhi Ge
P. Bolzoni
D. D’Auria
S. Helmer

Improving orienteering-based tourist trip planning with social sensing.

In: Future Generation Computer Systems (vol. 110) , pg. 931-945

(2020)

DOI: 10.1016/j.future.2019.10.028

Abstract anzeigen

We enhance a tourist trip planning framework based on orienteering with category constraints by adding social sensing. This allows us to customize a user’s experience without putting the burden of preference elicitation on the user. We identify the interests of a user by analyzing their Tweets and then match these interests to descriptions of points of interests. For this analysis we adapt different schemes for social sensing to the needs of our orienteering context and compare them to find the most suitable approach. We show that our technique is fast enough for use in real-time dynamic settings and also has a higher accuracy compared to previous approaches. Additionally, we integrate a more efficient algorithm for solving the orienteering problem, boosting the overall performance and utility of our framework further, as demonstrated by the positive user satisfaction received by real users.

Zeitschriftenartikel

M. Drăgoicea
L. Walletzký
L. Carrubbo
Nabil Badr
T., Angeliki, M.
F. Romanovská
Mouzhi Ge

Service Design for Resilience: A Multi-Contextual Modeling Perspective.

In: IEEE Access (vol. 8) , pg. 185526-185543

(2020)

DOI: 10.1109/ACCESS.2020.3029320

Abstract anzeigen

This paper introduces a conceptual framework aiming to broaden the discussion on resilience for the design of public services. From a theoretical point of view, the paper explores service design with a Systems Thinking lens. A multi-contextual perspective aiming to analyze, decompose, and design smart cities services where resilience is an input at the service design level is described and the four diamondsof-context model for service design (4DocMod) is introduced. This service model accommodates various actors' contexts in public service design and consists of four design artefacts, the diamonds (See, Recognize, Organize, Do). From a practical point of view, guidelines for the application of the 4DocMod service model extension for resilience are described along with two case studies addressing the recent COVID-19 pandemic that illustrates a clear situation of resilience with insights in multiple contexts. According to the findings of this paper, it is obvious that resilience is not “just”a request. Instead, it plays a higher role within the service system. It is not “just”another Context, either. Instead, it goes through many contexts with different circumstances. In this manner, it is possible to address the qualities through which actors can become resilient, at the service design stage, to ensure continuity of the public services in times of emergency. As our approach using the 4DocMod is proposing, resilience may be is achieved when specific properties are provisioned at information service design level.

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova

A hybrid machine learning model for intrusion detection in VANET.

In: Computing , pg. 1-29

(2021)

DOI: 10.1007/s00607-021-01001-0

Abstract anzeigen

While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still an critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.

Zeitschriftenartikel

B. Mbarek
Mouzhi Ge
T. Pitner

Proactive trust classification for detection of replication attacks in 6LoWPAN-based IoT.

In: Internet of Things (vol. 16) , pg. 100442

(2021)

DOI: 10.1016/j.iot.2021.100442

Abstract anzeigen

The 6LoWPAN standard has been widely applied in different Internet of Things (IoT) application domains. However, since the nodes in the IoT are mostly resource constrained, 6LoWPAN is vulnerable to a variety of security attacks. Among others, replication attack is one of the severe security threads to IoT networks. This paper therefore proposes a trust-based detection strategy against replication attacks in IoT, where a number of replica nodes are intentionally inserted into the network to test the reliability and response of witness nodes. We further assess the feasibility of the proposed detection strategy and compare with two other strategies such as brute-force and first visited strategy via a thorough simulation. The evaluation takes into account the detection probability for compromised attacks, the execution time of transactions and rate of communication failure. The simulation results show that while maintaining detection runtime on average 60 s for up to 1000 nodes, the proposed trust-based strategy can significantly increase the detection probability to 90% on average against replication attacks and in turn significantly reduce the communication failure.

Zeitschriftenartikel

J. Blanco
Mouzhi Ge
T. Pitner

Modeling Inconsistent Data for Reasoners in Web of Things.

In: Procedia Computer Science , pg. 1265-1273

(2021)

DOI: 10.1016/j.procs.2021.08.130

Abstract anzeigen

With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap’s four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist.

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova
L. Trang

Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network.

In: Transactions on Emerging Telecommunications Technologies (vol. 32) , pg. 1-22

(2021)

DOI: 10.1002/ett.4280

Abstract anzeigen

Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i., reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.

Beitrag in Sammelwerk/Tagungsband

J. Blanco
Mouzhi Ge
T. Pitner

Recommendation Recovery with Adaptive Filter for Recommender Systems.

SCITEPRESS - Science and Technology Publications pg. 283-290

DOI: 10.5220/0010653600003058

(2021)

Beitrag in Sammelwerk/Tagungsband

A. Tóth
Mouzhi Ge

A Deployable Data as a Service Architecture for Enterprises.

pg. 278-285

DOI: 10.5220/0010470702780285

(2021)

Beitrag in Sammelwerk/Tagungsband

B. Mbarek
Mouzhi Ge
T. Pitner

Trust-based Detection Strategy against Replication Attacks in IoT.

vol. 2 pg. 1–12

DOI: 10.1007/978-3-030-75075-6_53

(2021)

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova

A Hybrid Data-driven Model for Intrusion Detection in VANET.

In: Procedia Computer Science (vol. 184) , pg. 516-523

(2021)

DOI: 10.1016/j.procs.2021.03.065

Abstract anzeigen

Nowadays, VANET (Vehicular Ad-hoc NETwork) has gained increasing attention from many researchers with its various applications, such as enhancing traffic safety by collecting and disseminating traffic event information. This increased interest in VANET has necessitated greater scrutiny of machine learning (ML) methods used for improving the security capabilities of intrusion detection systems (IDSs), such as the need to solve computationally intensive ML problems due to the increased vehicular data. Therefore, in this paper, we propose a hybrid ML model to enhance the performance of IDSs by dealing with the explosive growth in computing power and the need for detecting malicious incidents timely. The proposed approach mainly uses the advantages of Random Forest to detect known network intrusions. Besides, there is a post-detection phase to detect possible novel intruders by using the advantages of coresets and clustering algorithms. Our approach is evaluated over a very recent IDS dataset named CICIDS2017. The preliminary results show that the proposed hybrid model can increase the utility of IDSs.

Zeitschriftenartikel

B. Mbarek
Mouzhi Ge
T. Pitner

Trust-Based Authentication for Smart Home Systems.

In: Wireless Personal Communications (vol. 117) , pg. 2157-2172

(2021)

DOI: 10.1007/s11277-020-07965-0

Abstract anzeigen

Smart home systems are developed to interconnect and automate household appliances and create ubiquitous home services. Such a system is mainly driven by the communications among Internet-of-Things (IoT) objects along with Radio Frequency IDentification (RFID) technologies, where the RFID techniques in the IoT network are commonly prone to malicious attacks due to the inherent weaknesses of underlying wireless radio communications. Thus, it causes the smart home systems vulnerable to some active attacks such as the jamming and cloning attacks, which in turn threaten to home breach and personal information disclosure. This paper therefore proposes a new trust-based authentication scheme to effectively address two typical attacks, jamming and cloning attacks, in smart home environment. The evaluation shows that our solution can significantly reduce the authentication failure in jamming attacks, increase the detection probability of cloning attacks, and improve the authentication efficiency to manage the authentication delay in a reasonable time.

Beitrag in Sammelwerk/Tagungsband

V. Carusotto
G. Pilato
F. Persia
Mouzhi Ge

User Profiling for Tourist Trip Recommendations using Social Sensing.

In:
Institute of Electrical and Electronics Engineers Inc.

pg. 182-185

DOI: 10.1109/ISM52913.2021.00036

(2021)

Zeitschriftenartikel

B. Mbarek
Mouzhi Ge
T. Pitner

An adaptive anti-jamming system in HyperLedger-based wireless sensor networks.

In: Wireless Networks

(2022)

DOI: 10.1007/s11276-022-02886-1

Abstract anzeigen

Using new methodologies such as Blockchain in data communications in wireless sensor networks (WSN) has emerged owing to the proliferation of collaborative technologies. However, the WSN is still vulnerable to denial of service cyber attacks, in which jamming attack becomes prevalent in blocking data communications in WSN. The jamming attack launches malicious sensor nodes to block legitimate data communications by intentional interference. This can in turn cause monitoring disruptions, data loss and other safety-critical issues. In order to address the malicious attacks, this paper proposes an adaptive anti-jamming solution based on Hyperledger Fabric-based Blockchain, named as ABAS, to ensure the reliability and adaptivity of data communication in case of jamming attacks. In order to validate the ABAS solution, we applied the algorithm in healthcare WSN and showed that ABAS has significantly reduce the jamming coverage and energy consumption while maintaining high computational performance.

Zeitschriftenartikel

Mouzhi Ge
F. Persia
G. Pilato

Guest Editors’ Introduction.

In: International Journal of Semantic Computing (vol. 16) , pg. 161-162

(2022)

DOI: 10.1142/S1793351X22020020

Zeitschriftenartikel

G. Pilato
F. Persia
Mouzhi Ge
D. DAuria

Social Sensing for Personalized Orienteering Mediating the Need for Sociality and the Risk of COVID-19.

In: IEEE Transactions on Technology and Society (vol. 3) , pg. 323-332

(2022)

DOI: 10.1109/TTS.2022.3210882

Abstract anzeigen

Orienteering or itinerary planning applications aim to optimize travel routes exploiting user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-Of-Interest (POI) or touristic routes. However, user preference has been significantly affected by the COVID-19, since health concern plays a key trade-off role now. For example, people may try to avoid crowdedness, even if there is a strong social desire. However, most orienteering applications just focus on user preferences, thus paying less attention to the variety of the data inputs, which has become an essential factor for the utility of the application in the COVID-19 era. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, fear of being infected, knowledge of the COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with Doc2Vec and FastText based on the Yelp dataset. Furthermore, the proposed system is modular and can be efficiently adapted to different applications for COVID-aware itinerary planning.

Beitrag in Sammelwerk/Tagungsband

P. Kostka
B. Rossi
Mouzhi Ge

Monte Carlo Methods for Industry 4.0 Applications.

IEEE pg. 242-247

DOI: 10.1109/SMC53654.2022.9945553

(2022)

Zeitschriftenartikel

J. Blanco
Mouzhi Ge
T. Pitner

Human-Generated Web Data Disentanglement for Complex Event Processing.

In: Procedia Computer Science (vol. 207) , pg. 1341-1349

(2022)

DOI: 10.1016/j.procs.2022.09.190

Abstract anzeigen

In social media, human-generated web data from real-world events have become exponentially complex due to the chaotic and spontaneous features of natural language. This may create an information overload for the information consumers, and in turn not easily digest a large amount of information in a limited time. To tackle this issue, we propose to use Complex Event Processing (CEP) and semantic web reasoners to disentangle the human-generated data and present users with only relevant and important data. However, one of the key obstacles is that the human-generated data can have no structured meaning sometimes even for the speaker, hindering the output of the CEP. Therefore, in order to adapt to the CEP inputs, we present two different techniques that allow for the discrimination and digestion of value of human-generated data. The first one relies on the Variable Sharing Property that was developed for relevance logics, while the second one is based on semantic equivalence and natural language processing. The results can be given to CEP for further semantic reasoning and generate digested information for users.

Beitrag in Sammelwerk/Tagungsband

B. Buhnova
T. Kazičková
Mouzhi Ge
L. Walletzký
F. Caputo
L. Carrubbo

A Cross-domain Landscape of ICT Services in Smart Cities.

In:
S. Rassia
A. Tsokas
P. Pardalos

Springer pg. 63-95

DOI: 10.1007/978-3-030-84459-2_5

(2022)

Beitrag in Sammelwerk/Tagungsband

D. Kusnirakova
Mouzhi Ge
L. Walletzky
B. Buhnova

Interoperability-oriented Quality Assessment for Czech Open Data.

SCITEPRESS - Science and Technology Publications pg. 446-453

DOI: 10.5220/0011291900003269

(2022)

Beitrag in Sammelwerk/Tagungsband

B. Mbarek
Mouzhi Ge
T. Pitner

Precisional Detection Strategy for 6LoWPAN Networks in IoT.

IEEE pg. 1006 - 1011

DOI: 10.1109/SMC53654.2022.9945346

(2022)

Beitrag in Sammelwerk/Tagungsband

L. Walletzký
O. Bayarsaikhan
Mouzhi Ge
Z. Schwarzová

Evaluation of Smart City Models: A Conceptual and Structural View.

SCITEPRESS - Science and Technology Publications pg. 56-65

DOI: 10.5220/0011074900003203

(2022)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
B. Buhnova

DISDA: Digital Service Design Architecture for Smart City Ecosystems.

SCITEPRESS - Science and Technology Publications pg. 207-214

DOI: 10.5220/0011056100003200

(2022)

Beitrag in Sammelwerk/Tagungsband

J. Bauer
R. Wichert
C. Konrad
M. Hechtel
S. Dengler
Simon Uhrmann
Mouzhi Ge
P. Poller
D. Kahl
B. Ristok
J. Franke

ForeSight – User-Centered and Personalized Privacy and Security Approach for Smart Living.

In:
S. Konomi
N. Streitz

Cham: Springer International Publishing vol. 13326 pg. 18-36

DOI: 10.1007/978-3-031-05431-0_2

(2022)

Beitrag in Sammelwerk/Tagungsband

Q. Yang
Mouzhi Ge
M. Helfert

Classification Methodology for Architectures in Information Systems: A Statistical Converging Technique.

In:
Barry, C., Lang, M., Linger, H.
M. Da Silva
C. Schneider
A. Da Silva
J. Estima

DOI: 10.62036/ISD.2023.11

(2023)

Beitrag in Sammelwerk/Tagungsband

T. Chondrogiannis
Mouzhi Ge

Rating Inference for Custom Trips from Enriched GPS Traces using Random Forests.

New York, NY, USA: ACM pg. 50-57

DOI: 10.1145/3615896.3628344

(2023)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
G. Pilato
F. Persia
D. DAuria

Recommender System for Social Media: Research Challenges and Future Applications.

IEEE pg. 253-257

DOI: 10.1109/TransAI60598.2023.00033

(2023)

Zeitschriftenartikel

V. Stehlík
Mouzhi Ge

TOPSIS-based Recommender System for Big Data Visualizations.

In: Journal of Applied Interdisciplinary Research (Focus Issue: Artificial Intelligence) (vol. 1) , pg. 50-74

(2023)

DOI: 10.25929/jair.v1i1.114

Abstract anzeigen

p. 50-74.

Zeitschriftenartikel

G. Pilato
F. Persia
Mouzhi Ge
T. Chondrogiannis
D. DAuria

A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era.

In: ACM Transactions on Management Information Systems (vol. 14) , pg. 1-26

(2023)

DOI: 10.1145/3615359

Abstract anzeigen

Orienteering or itinerary planning algorithms in tourism are used to optimize travel routes by considering user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-of-Interest (POIs) or tourist routes. However, nowadays, user preference has been significantly affected by COVID-19 since health concern plays a key trade-off role. For example, people may try to avoid crowdedness, even if there is a strong desire for social interaction. Thus, the orienteering or itinerary planning algorithms should optimize routes beyond user preference. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, personality, knowledge of COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with a properly trained fastText neural network and a set of specialized Naïve Bayesian Classifiers based on the “Yelp!” data set. Also, we demonstrate how to approach and integrate COVID-related factors via conversational agents. Furthermore, the proposed system is in a modular design and evaluated in a user study; thus, it can be efficiently adapted to different algorithms for COVID-19-aware itinerary planning.

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
G. Pilato
F. Persia
D. DAuria

New Perspectives on Recommender Systems for Industries.

DOI: 10.1109/AI4I54798.2022.00009

(2023)

Zeitschriftenartikel

J. Blanco
Mouzhi Ge
J. Del Alamo
J. Dueñas
F. Cuadrado

A formal model for reliable digital transformation of water distribution networks.

In: Procedia Computer Science (vol. 225) , pg. 2076-2085

(2023)

DOI: 10.1016/j.procs.2023.10.198

Abstract anzeigen

The concept of modernizing outdated systems in critical infrastructure through digital transformation has been a widely discussed topic nowadays. Following the transition of energy systems, the attention has now shifted towards digitalizing the water distribution systems. These systems are large-scale but outdated systems that frequently encounter various issues and upgrading them would enable easier to identify issues and provide smoother, more efficient service. However, this process requires cautious planning and guidance to ensure that the generated data is reliable, and the system remains operational during the transition. Hence, the primary objective of this paper is to propose a formal model based on ternary relational semantics that can guide the digital transformation of water distribution networks. The proposed model provides a flexible transformation process while making the system generate reliable data. Additionally, this paper demonstrates the application of the proposed model by developing a proof of concept based on a real-world scenario.

Beitrag in Sammelwerk/Tagungsband

Md Moin Uddin
Mouzhi Ge

Data Analytics Framework for Identifying Relevant Adverse Events in Medical Software.

SCITEPRESS - Science and Technology Publications pg. 81-90

DOI: 10.5220/0012038900003476

(2023)

Beitrag in Sammelwerk/Tagungsband

J. Blanco
Mouzhi Ge
T. Pitner

An Adaptive Filter for Preference Fine-Tuning in Recommender Systems.

In:
F. Domínguez Mayo
M. Marchiori
J. Filipe

Cham: Springer International Publishing vol. 469 pg. 107-121

DOI: 10.1007/978-3-031-24197-0_7

(2023)

Beitrag in Sammelwerk/Tagungsband

M. Macak
T. Rebok
M. Stovcik
Mouzhi Ge
B. Rossi
B. Buhnova

CopAS: A Big Data Forensic Analytics System.

SCITEPRESS - Science and Technology Publications pg. 150-161

DOI: 10.5220/0011929000003482

(2023)

Beitrag in Sammelwerk/Tagungsband

D. DAuria
Mouzhi Ge
M. Sert
V. Swaminathan
T. Yamasaki

Message from the Program Co-Chairs.

DOI: 10.1109/ISM55400.2022.00006

(2023)

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova

Deep-Learning based Reputation Model for Indirect Trust Management.

In: Procedia Computer Science (vol. 220) , pg. 405-412

(2023)

DOI: 10.1016/j.procs.2023.03.052

Abstract anzeigen

In the digital era, human and thing behavioral patterns have been merged, which leads to the need for trust management to secure the relationship among people and things (e.g., driverless cars). Due to the dynamism and complexity of digital environments, trust management depends largely on indirect trust to support its reasoning by building the reputation of trustees based on recommendations reflected in the feedback of sentiment and non-sentiment objects. However, different biases are still affecting the accuracy of indirect trust that reflects a collective trustworthiness belief or societal stereotypes. This work focuses on enabling indirect trust management by leveraging deep learning in combination with synthetic data for bias management. Specifically, this paper proposes a reputation model to support decision-making in trust management by minimizing bias in indirect trust information and fostering fairly the relationship among sentiment and non-sentiment objects. Our experimental results show that the synthetic data can significantly improve the classification accuracy in trust management.

Beitrag in Sammelwerk/Tagungsband

H. Bangui
E. Cioroaica
Mouzhi Ge
B. Buhnova

Deep-Learning based Trust Management with Self-Adaptation in the Internet of Behavior.

In:
T. Cerny
J. Hong
H. Shahriar
M. Lanperne
J. Park

New York, NY, USA: ACM pg. 874-881

DOI: 10.1145/3555776.3577694

(2023)

Zeitschriftenartikel

L. Walletzký
L. Carrubbo
Mouzhi Ge
Z. Schwarzová
O. Bayarsaikhan

Multi-Contextual Smart City Model for Service Interconnections. L. Carrubbo and J. Ralyté (Eds.).

In: ITM Web of Conferences (International Conference on Exploring Service Science (IESS 2.3); Geneva, Switzerland, February 16-17, 2023) (vol. 51) , pg. 1-11

(2023)

DOI: 10.1051/itmconf/20235101001

Beitrag in Sammelwerk/Tagungsband

Sebastian Markov
Georg Steckenbauer
Marcus Herntrei
Mouzhi Ge

Zur sozialen Konstruktion von Wald und seinem Bedeutungswandel im Kontext von Gesundheit.

In:
T. Sedelmeier
O. Kühne
C. Jenal
T. Freytag

Wiesbaden: Springer Fachmedien Wiesbaden pg. 403-425

DOI: 10.1007/978-3-658-39085-3_21

(2023)

Beitrag in Sammelwerk/Tagungsband

L. Walletzký
O. Bayarsaikhan
Mouzhi Ge
Z. Schwarzová

An Evaluation of Smart City Models Towards a New Service Design Model.

In:
J. Ploeg
C. Klein
M. Jarke
M. Helfert
K. Berns
O. Gusikhin

Cham: Springer Nature Switzerland vol. 1843 pg. 47-67

DOI: 10.1007/978-3-031-37470-8_3

(2023)

Zeitschriftenartikel

H. Bangui
B. Buhnova
Mouzhi Ge

Social Internet of Things: Ethical AI Principles in Trust Management.

In: Procedia Computer Science (vol. 220) , pg. 553-560

(2023)

DOI: 10.1016/j.procs.2023.03.070

Abstract anzeigen

Trust management has become a fundamental requirement for Social Internet of Things (SIoT) to enable a trustworthy social network of smart objects necessary for enhancing the security and reliability of cyber-physical systems. To increase the credibility scores in trust management, AI (Artificial Intelligence) has been adopted. However, the current need for digital acceleration has brought ethical concerns related to the smartness and social consciousness of autonomous objects, which leads to a question whether AI-based trust management is ready to deal with these concerns. In this paper, we consider 11 ethical dimensions within the context of trust management in SIoT. Then, we examine the existing AI-based trust models in the context of SIoT and its related application domains to assess their maturity in terms of the 11 ethical dimensions. The evaluation results show how trust management can be improved by AI ethical principles in vehicular networks and underwater acoustic sensor networks.

Vortrag

Mouzhi Ge

Trust Management with Deep Learning in the Internet of Behavior. Invited Keynote.

CERIT Science Park II.

Brno, Czech Republic 12.-13.09.2023.

(2023)

Zeitschriftenartikel

H. Bangui
Mouzhi Ge
B. Buhnova

When Trustless meets Trust: Blockchain Consensus Review and Reconsideration.

In: Procedia Computer Science (vol. 246) , pg. 3351-3360

(2024)

DOI: 10.1016/j.procs.2024.09.222

Abstract anzeigen

Blockchain, as a trustless network, has provided diverse benefits for a wide range of application domains, such as enhancing data management in terms of data security, traceability, accountability, transparency, and decentralization. However, the detection of cybersecurity vulnerabilities in blockchain has initiated a debate on whether this inherently trustless technology needs further trust support or not. In this work, we explore mutual trust and trustless cooperation. First, we examine blockchain-assisted trust management to highlight the specific trustless trait of blockchain. Then, as consensus is an important component of blockchain technology, we examine the role of trust in evaluating the trustworthiness of peer participants in the blockchain consensus process and enhancing the growth of a consistent chain. Finally, we derive research findings in the promising cooperation between trustless and trust.

Zeitschriftenartikel

Fara Fernandes
Mouzhi Ge
Georgi Chaltikyan
Martin Gerdes
Christian Omlin

Preparing for downstream tasks in AI for dental radiology: a baseline performance comparison of deep learning models.

In: Dentomaxillofacial Radiology

(2024)

DOI: 10.1093/dmfr/twae056

Abstract anzeigen

OBJECTIVES To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT) and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures. METHODS Retrospectively collected 2-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT and gMLP architectures as classifiers for 4 different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, presence or absence of the mental foramen and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy and f1-score) and area under curve (AUC) - receiver operating characteristic and precision-recall curves were calculated. RESULTS The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77-1.00 (CNN), 0.80-1.00 (ViT) and 0.73-1.00 (gMLP) for all of the 4 cases. CONCLUSIONS The difference in performance of the ViT, gMLP and the CNN (the current state-of-the-art) was significant in certain tasks. This difference in model performance for various tasks proves that capabilities of different architectures may be leveraged. ADVANCES IN KNOWLEDGE The vision transformer, followed by the gated multilayer perceptron are deep learning models that exhibit comparable performance with the convolutional neural network in the classification of dental radiographic images.

Zeitschriftenartikel

F. Persia
D. DAuria
Mouzhi Ge
G. Pilato

Improving the learning performance by exploiting multimedia in eXtreme apprenticeship.

In: Multimedia Tools and Applications

(2024)

DOI: 10.1007/s11042-024-20006-3

Abstract anzeigen

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures.

Beitrag in Sammelwerk/Tagungsband

A. Pio
F. Persia
G. Pilato
Mouzhi Ge
D. DAuria

A Framework for Intelligent Trip Planning leveraging LLMs, OpenStreetMap, and Neo4j.

IEEE pg. 76-79

DOI: 10.1109/AIxB62249.2024.00021

(2024)

Zeitschriftenartikel

Mike Guttmann
Mouzhi Ge

Research Agenda of Ethical Recommender Systems based on Explainable AI.

In: Procedia Computer Science (vol. 238) , pg. 328-335

(2024)

DOI: 10.1016/j.procs.2024.06.032

Abstract anzeigen

In the digital era, recommender systems (RS) have become an integral part of our daily interactions, exerting a significant impact on users and society. However, this also raises ethical challenges related to RS that should be considered. Addressing these challenges requires the application of explainable artificial intelligence (XAI) models to make RS more understandable. Based on the current state-of-the-art literature, this paper aims to provide a comprehensive overview of XAI for RS and its ethical implications, with the aim of proposing a research agenda for ethical RS based on XAI. The findings of the literature review show that neural network-based RS have received much attention in terms of offering explanations, while there is a research gap in explaining context-based RS and in evaluating explanations. In addition, a set of ethical challenges for RS are discussed by exploring how explanations for recommendations can contribute to the ethical use of RS.

Beitrag in Sammelwerk/Tagungsband

F. Persia
Mouzhi Ge
G. Pilato
D. DAuria
A. Rafanelli
S. Costantini
G. Gasperis

Leveraging DALI to Refine Route Planning by Dynamically Avoiding Risky POIs.

IEEE pg. 351-354

DOI: 10.1109/ICSC59802.2024.00061

(2024)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
B. Rossi
S. Chren
J. Blanco

Petri Nets for Smart Grids: The Story So Far.

In:
J. Hong
J. Park

New York, NY, USA: ACM pg. 661-670

DOI: 10.1145/3605098.3635989

(2024)

Zeitschriftenartikel

Jessica Ohnesorg
N. Fakhoury
N. Eltahawi
Mouzhi Ge

A review of AI-based trust management in smart cities.

In: ITM Web of Conferences (vol. 62 (International Conference on Exploring Service Science (IESS 2.4)) , pg. 01003

(2024)

DOI: 10.1051/itmconf/20246201003

Abstract anzeigen

Given the complexity of trust management in smart cities, this work unfolds the important role of trust management across various domains. Be- yond its traditional roots in human relationships, trust management emerges as a cornerstone in technological, business, and societal contexts. This scoping review first organizes the literature by five most commonly cited indicators, and then derives essential insights from existing literature. This paper not only nav- igates through the challenges presented by technological advancements in trust management, but also offers a comprehensive understanding of the mechanisms and frameworks shaping trust in smart cities.

Zeitschriftenartikel

Mouzhi Ge
Fabio Persia
Giovanni Pilato

Guest Editorial: Special Issue on Multimedia Computing.

In: International Journal of Semantic Computing , pg. 1-3

(2024)

DOI: 10.1142/S1793351X24020057

Zeitschriftenartikel

Z. Schwarzová
L. Walletzký
Mouzhi Ge
P. Procházka

Application of context-driven methodology for implementing the smart city concept in Czech republic.

In: ITM Web of Conferences (vol. 62) , pg. 01002

(2024)

DOI: 10.1051/itmconf/20246201002

Abstract anzeigen

This paper investigates the significance of context comprehension within the smart city domain by emphasizing the various perspectives of stakeholders such as ministries, municipalities, and citizens. Since the value of service provision depends on the interactions across different service systems within distinct domains and contexts, through an exploration of the value chain and formulation of value propositions, the paper aims to achieve the synergy among diverse service contexts. Using the smart city context of the Czech Republic as a case study, the paper systematically examines the implementation of context-driven methodology and provides insights for the broader development of smart cities. Also, this study highlights the importance of understanding service overlaps and identifies key issues for future service research, with potential applicability in smart city initiatives, particularly in middle and eastern EU countries.

Beitrag in Sammelwerk/Tagungsband

Md Moin Uddin
Mouzhi Ge

Streamlining Clinical Evaluation with Explanatory Data Analytics for Adverse Events in Medical Devices.

In:
M. Lozano
M. Mulvenna
M. Ziefle

Cham: Springer Nature Switzerland vol. 2087 pg. 150-169

DOI: 10.1007/978-3-031-62753-8_9

(2024)

Beitrag in Sammelwerk/Tagungsband

Jessica Ohnesorg
Nazek Fakhoury
Noura Eltahawi
Mouzhi Ge

Customizing Trust Systems: Personalized Communication to Address AI Adoption in Smart Cities.

SCITEPRESS - Science and Technology Publications pg. 73-79

DOI: 10.5220/0012731400003714

(2024)

Beitrag in Sammelwerk/Tagungsband

Mouzhi Ge
T. Oshima
M. Sert
V. Swaminathan

Message from the Program Chairs.

IEEE pg. 14-15

DOI: 10.1109/ISM63611.2024.00066

(2024)

Beitrag in Sammelwerk/Tagungsband

H. Bangui
B. Buhnova
Mouzhi Ge
S. Kriglstein

Leveraging the Internet of Behaviors for Mutual Trust in Digital Ecosystems.

In:
L. Leiva
T. Li
K. Väänänen
D. Spano
F. Paternò
K. Verbert

New York, NY, USA: ACM pg. 82-86

DOI: 10.1145/3708557.3716344

(2025)

Vita

Dr. Mouzhi Ge ist Professor für Data Analytics an der Technischen Hochschule Deggendorf. Zuvor war er Associate Professor (Tenured) an der Masaryk Universität in Tschechischen, wo er seine Habilitation erhielt. Nach seiner Promotion an der Dublin City University in Irland hat er anschließend in Großbritannien, den USA und Italien Forschung und Praxis im Bereich Data Engineering und Intelligente Systeme betrieben. Seine Forschung konzentriert sich hauptsächlich auf Big Data Analytics, intelligente Gesundheitssysteme, Internet of Things sowie gesundheitsbewusste Empfehlungssysteme. In solchen Forschungsbereichen hat er mehr als 100 Publikationen im internationalen Umfeld veröffentlicht. Seine Forschungsergebnisse wurden in verschiedenen Fachzeitschriften veröffentlicht, darunter im Future Generation Computer Systems, International Journal of Human-Computer Studies, International Journal of Cooperative Information Systems, IEEE Access, Wireless Personal Communications, International Journal of Semantic Computing, Internet of Things Journal, Journal of Sensor and Actuator Networks, Transactions on Emerging Telecommunications Technologies, Computing Journal, Wireless Networks, Journal of Computer Information Systems, IEEE Transactions on Technology and Society, ACM Transactions on Management Information Systems, Multimedia Tools and Applications, usw.

Sonstiges

Akademische Aktivitäten