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1
MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction
In: Information; Volume 13; Issue 2; Pages: 91 (2022)
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2
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals
In: Applied Sciences; Volume 12; Issue 5; Pages: 2298 (2022)
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3
Explainable Multimodal Fusion
Alvi, Jaweriah. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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4
Prediction and Visual Intelligence for Security Information: The PREVISION H2020 Project
In: CIRCLE 2020 ; https://hal.archives-ouvertes.fr/hal-02877780 ; CIRCLE 2020, Iván Cantador; Max Chevalier; Massimo Melucci; Josiane Mothe, Jul 2020, Samatan, France ; http://ceur-ws.org/Vol-2621/ (2020)
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5
Multimodal deep networks for text and image-based document classification ; Réseau de neurones multimodal pour la classification de documents image/texte
In: Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA) ; https://hal.archives-ouvertes.fr/hal-02163257 ; Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA), Jul 2019, Toulouse, France (2019)
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6
Data Fusion through Fuzzy-Bayesian Networks for Belief Generation in Cognitive Agents
In: Revista de Informática Teórica e Aplicada; v. 26, n. 2 (2019); 69-80 ; 21752745 ; 01034308 (2019)
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7
Methodology for automatic bioacoustic classification of anurans based on feature fusion
In: Expert Systems With Applications[ISSN 0957-4174],v. 50, p. 100-106 (2016)
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8
Disambiguation of Named Entities in Cultural Heritage Texts Using Linked Data Sets
In: New Trends in Databases and Information Systems ; First International Workshop on Semantic Web for Cultural Heritage, SW4CH 2015 ; https://hal.archives-ouvertes.fr/hal-01203784 ; First International Workshop on Semantic Web for Cultural Heritage, SW4CH 2015, Sep 2015, Poitiers, France. pp.505-514, ⟨10.1007/978-3-319-23201-0_51⟩ (2015)
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9
Mobile Active Authentication via Linguistic Modalities
In: DTIC (2015)
Abstract: Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed - world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: (1) text entered via soft keyboard, (2) applications used, (3) websites visited, and (4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance. We further characterize the contribution of two additional modalities developed in addition to the main four modalities: eye tracking and an alternate stylometry method. ; Sponsored in part by DARPA.
Keyword: *BIOMETRIC SECURITY; *COMPUTER NETWORK SECURITY; *INTRUSION DETECTION(COMPUTERS); *MOBILE COMPUTING; ACTIVE AUTHENTICATION; ANDROID OPERATING SYSTEMS; APPLICATION USAGE PATTERNS; BEHAVIOR; BEHAVIORAL BIOMETRICS; Biomedical Instrumentation and Bioengineering; Computer Programming and Software; Computer Systems Management and Standards; DFC(DATA FUSION CENTER); EER(EQUAL ERROR RATE); EYE TRACKING; FRR(FALSE REJECTION RATE); GLOBAL POSITIONING SYSTEM; INFORMATION ASSURANCE; INTERNET; MAN COMPUTER INTERFACE; MOBILE DEVICES; MONITORING; OPERATING SYSTEMS(COMPUTERS); PATTERN RECOGNITION; PE62722F; PERFORMANCE(ENGINEERING); SMARTPHONES; STYLOMETRY; TARGET DETECTION; TEST AND EVALUATION; TEXT PROCESSING; THREATS; TRACKING; USER NEEDS; VERIFICATION; WEB BROWSING BEHAVIOR; WIRELESS COMPUTER NETWORKS; WUAFRLAAP2DREX
URL: http://www.dtic.mil/docs/citations/ADA621879
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA621879
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10
A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules
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11
Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue
In: ISSN: 0928-4931 ; Materials Science and Engineering: C ; https://hal.archives-ouvertes.fr/hal-01073780 ; Materials Science and Engineering: C, Elsevier, 2014, 45, pp.348-358. ⟨10.1016/j.msec.2014.09.030⟩ (2014)
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12
Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment
Miranda, Luís Miguel Gonçalves. - : Faculdade de Ciências e Tecnologia, 2014
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13
Face Recognition and Event Detection in Video: An Overview of PROVE-IT Projects
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14
Towards a Simple and Efficient Web Search Framework
In: DTIC (2014)
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15
On the Design of an Intelligent Sensor Network for Flash Flood Monitoring, Diagnosis and Management in Urban Areas
Ancona, M.; Corradi, N.; Dellacasa, A.. - : Elsevier, 2014
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16
Applying query formulation and fusion techniques for cross language news story search
In: Arora, Piyush orcid:0000-0002-4261-2860 , Foster, Jennifer orcid:0000-0002-7789-4853 and Jones, Gareth J.F. orcid:0000-0003-2923-8365 (2013) Applying query formulation and fusion techniques for cross language news story search. In: 5th Forum on Information Retrieval Evaluation (FIRE'13), 4 -6 Dec 2013, New Delhi, India. ISBN 978-1-4503- 2830-2 (2013)
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17
Combining multiple types of intelligence to generate probability maps of moving targets
Zlatsin, Philip. - : Monterey, California: Naval Postgraduate School, 2013
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18
Employing the Intelligence Cycle Process Model Within the Homeland Security Enterprise
In: DTIC (2013)
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19
Better Equipping Reserve Military Intelligence Analyst to Meet the Needs of the Commander by Championing a Process-Driven Training Model
In: DTIC (2013)
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20
Combining Multiple Types of Intelligence to Generate Probability Maps of Moving Targets
In: DTIC (2013)
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