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Journal articles on the topic 'Context-aware'

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Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Kiani, Abdollah, Mohsen Amiri Fakhr, and Reza Rezaei. "Architectural Patterns for Context-Aware System." Journal of Software 10, no. 8 (August 2015): 1002–13. http://dx.doi.org/10.17706//jsw.10.8.1002-1013.

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2

Schilit, B. N., D. M. Hilbert, and J. Trevor. "Context-aware communication." IEEE Wireless Communications 9, no. 5 (October 2002): 46–54. http://dx.doi.org/10.1109/mwc.2002.1043853.

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3

Chang, Edward Y. "Context-aware computing." Proceedings of the VLDB Endowment 6, no. 11 (August 27, 2013): 1172–73. http://dx.doi.org/10.14778/2536222.2536245.

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4

Lu, Jianye, Athinodoros S. Georghiades, Andreas Glaser, Hongzhi Wu, Li-Yi Wei, Baining Guo, Julie Dorsey, and Holly Rushmeier. "Context-aware textures." ACM Transactions on Graphics 26, no. 1 (January 2007): 3. http://dx.doi.org/10.1145/1189762.1189765.

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5

Ranganathan, A., and Hui Lei. "Context-aware communication." Computer 36, no. 4 (April 2003): 90–92. http://dx.doi.org/10.1109/mc.2003.1193232.

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6

Liu, Xin, Jiyong Zhang, and Chenggang Yan. "Towards context-aware collaborative filtering by learning context-aware latent representations." Knowledge-Based Systems 199 (July 2020): 105988. http://dx.doi.org/10.1016/j.knosys.2020.105988.

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7

Lupiana, Dennis. "Context Modeling for Context-Aware Systems." International Journal of Intelligent Computing Research 8, no. 1 (March 30, 2017): 807–16. http://dx.doi.org/10.20533/ijicr.2042.4655.2017.0099.

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8

WU, Jin-liang, and Li-gang LIU. "Context-aware Focus+Context visualization technique." Journal of Computer Applications 31, no. 1 (March 18, 2011): 6–10. http://dx.doi.org/10.3724/sp.j.1087.2011.00006.

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9

Capilla, Rafael, Oscar Ortiz, and Mike Hinchey. "Context Variability for Context-Aware Systems." Computer 47, no. 2 (February 2014): 85–87. http://dx.doi.org/10.1109/mc.2014.33.

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10

Agagu, Tosin, and Thomas Tran. "Context-Aware Recommendation Methods." International Journal of Intelligent Systems and Applications 10, no. 9 (September 8, 2018): 1–12. http://dx.doi.org/10.5815/ijisa.2018.09.01.

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11

Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (October 31, 2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.

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Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

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12

Bhattacharyya, Shankari, Author RA.K.Saravanagru, and Arunkumar Thangavelu. "Context Aware Healthcare Application." International Journal of Computer Applications 22, no. 3 (May 31, 2011): 1–6. http://dx.doi.org/10.5120/2566-3527.

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13

Wang, Hongli, Bin Guo, Jiaqi Liu, Sicong Liu, Yungang Wu, and Zhiwen Yu. "Context-aware Adaptive Surgery." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–22. http://dx.doi.org/10.1145/3478073.

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Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

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14

WANG, Li-Cai, Xiang-Wu MENG, and Yu-Jie ZHANG. "Context-Aware Recommender Systems." Journal of Software 23, no. 1 (March 5, 2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.

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15

Agarwal, Er Vidhi. "Context Aware In Phr." IOSR Journal of Dental and Medical Sciences 1, no. 2 (2012): 25–28. http://dx.doi.org/10.9790/0853-0122528.

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16

Benerecetti, Massimo, Paolo Bouquet, and Matteo Bonifacio. "Distributed Context-Aware Systems." Human–Computer Interaction 16, no. 2-4 (December 2001): 213–28. http://dx.doi.org/10.1207/s15327051hci16234_06.

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17

Ming Yang, Ying Wu, and Gang Hua. "Context-Aware Visual Tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 7 (July 2009): 1195–209. http://dx.doi.org/10.1109/tpami.2008.146.

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18

Cai, Yang. "Minimalism Context-Aware Displays." CyberPsychology & Behavior 7, no. 6 (December 2004): 635–44. http://dx.doi.org/10.1089/cpb.2004.7.635.

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19

Goferman, Stas, Lihi Zelnik-Manor, and Ayellet Tal. "Context-Aware Saliency Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 10 (October 2012): 1915–26. http://dx.doi.org/10.1109/tpami.2011.272.

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20

Chan, Jacky C. K., Ata Mahjoubfar, Claire L. Chen, and Bahram Jalali. "Context-Aware Image Compression." PLOS ONE 11, no. 7 (July 1, 2016): e0158201. http://dx.doi.org/10.1371/journal.pone.0158201.

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21

Riboni, Daniele. "Context-Aware Pervasive Interfaces." IEEE Internet Computing 19, no. 4 (July 2015): 68–72. http://dx.doi.org/10.1109/mic.2015.78.

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22

Schmidt, Albrecht, and Hans W. Gellersen. "Context-aware mobile telephony." ACM SIGGROUP Bulletin 22, no. 1 (April 2001): 19–21. http://dx.doi.org/10.1145/500721.500726.

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23

Williamson, Carey, and Qian Wu. "Context-aware TCP/IP." ACM SIGMETRICS Performance Evaluation Review 30, no. 1 (June 2002): 262–63. http://dx.doi.org/10.1145/511399.511371.

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24

Coppola, Paolo, Vincenzo Della Mea, Luca Di Gaspero, Davide Menegon, Danny Mischis, Stefano Mizzaro, Ivan Scagnetto, and Luca Vassena. "The Context-Aware Browser." IEEE Intelligent Systems 25, no. 1 (January 2010): 38–47. http://dx.doi.org/10.1109/mis.2010.26.

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25

Cadenas, J. T., N. Marín, and M. A. Vila. "Context-Aware Fuzzy Databases." Applied Soft Computing 25 (December 2014): 215–33. http://dx.doi.org/10.1016/j.asoc.2014.09.020.

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26

Abeywickrama, Dhaminda B., and Sita Ramakrishnan. "Context-aware services engineering." ACM Transactions on Internet Technology 11, no. 3 (January 2012): 1–28. http://dx.doi.org/10.1145/2078316.2078318.

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27

Li, Haofeng, Guanbin Li, Liang Lin, Hongchuan Yu, and Yizhou Yu. "Context-Aware Semantic Inpainting." IEEE Transactions on Cybernetics 49, no. 12 (December 2019): 4398–411. http://dx.doi.org/10.1109/tcyb.2018.2865036.

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28

Basler, Gérard, Michele Mazzucchi, Thomas Wahl, and Daniel Kroening. "Context-aware counter abstraction." Formal Methods in System Design 36, no. 3 (June 11, 2010): 223–45. http://dx.doi.org/10.1007/s10703-010-0096-7.

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29

Zhou, Zhenji, Lifa Wu, and Zheng Hong. "Context-Aware Access Control Model for Cloud Computing." International Journal of Grid and Distributed Computing 6, no. 6 (December 31, 2013): 1–12. http://dx.doi.org/10.14257/ijgdc.2013.6.6.01.

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30

Opoku, Samuel King. "Activity Context Modeling in Context-Aware Environment." International Journal of Computer Applications Technology and Research 5, no. 11 (October 31, 2016): 687–92. http://dx.doi.org/10.7753/ijcatr0511.1002.

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31

Al-Shargabi, Asma A. Q., Francois Siewe, and Ammar T. Zahary. "Quality of Context in Context-Aware Systems." EAI Endorsed Transactions on Context-aware Systems and Applications 4, no. 12 (July 6, 2017): 152761. http://dx.doi.org/10.4108/eai.6-7-2017.152761.

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32

Khedr, M., and A. Karmouch. "Negotiating Context Information in Context-Aware Systems." IEEE Intelligent Systems 19, no. 06 (November 2004): 21–29. http://dx.doi.org/10.1109/mis.2004.70.

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33

Syukur, Evi, and Seng Wai Loke. "Implementing context-aware regulation of context-aware mobile services in pervasive computing environments." International Journal of Web and Grid Services 2, no. 3 (2006): 260. http://dx.doi.org/10.1504/ijwgs.2006.011357.

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34

Kwon, Ohbyung, and Namyeon Lee. "A relationship-aware methodology for context-aware service selection." Expert Systems 28, no. 4 (November 10, 2010): 375–90. http://dx.doi.org/10.1111/j.1468-0394.2010.00548.x.

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35

B., Siva Rama Krishna, and Sreenivasa Reddy E. "Improved Context Aware PSO Task Scheduling in Cloud Computing." Webology 19, no. 1 (January 20, 2022): 3709–21. http://dx.doi.org/10.14704/web/v19i1/web19244.

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One of the major advantages of switching to the clouds is the scalability ability of the applications. Contrary to the grids, the ability to scale the cloud resources allows their real-time provisioning so as to meet the application constraints Generation of optimal schedule for given set of tasks and machines. Different experiments show that although having an optimum solution is almost impossible but having a sub-optimal solution using heuristic algorithms seems possible. In this paper, we propose a Context Aware PSO Task Scheduling scheme to analyze various scenarios with different parameters in cloud computing system corresponding to APSO In each scenario, we change one parameter and keep other parameters constant.

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36

Botchkaryov, Alexey. "Context-Aware Task Sequence Planning for Autonomous Intelligent Systems." Advances in Cyber-Physical Systems 3, no. 2 (November 10, 2018): 60–66. http://dx.doi.org/10.23939/acps2018.02.060.

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37

Agarwal, Sohit, and Mukesh Kumar Gupta. "Context Aware Image Sentiment Classification using Deep Learning Techniques." Indian Journal Of Science And Technology 15, no. 47 (December 20, 2022): 2619–27. http://dx.doi.org/10.17485/ijst/v15i47.1907.

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38

Smirnov, A. V., N. G. Shilov, A. V. Ponomarev, A. M. Kashevnik, and V. G. Parfenov. "Group context-aware recommendation systems." Scientific and Technical Information Processing 41, no. 5 (December 2014): 325–34. http://dx.doi.org/10.3103/s0147688214050050.

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39

Lefford, M. Nyssim, Gary Bromham, György Fazekas, and David Moffat. "Context-Aware Intelligent Mixing Systems." Journal of the Audio Engineering Society 69, no. 3 (March 10, 2021): 128–41. http://dx.doi.org/10.17743/jaes.2020.0043.

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40

Han, Jialong, Aixin Sun, Haisong Zhang, Chenliang Li, and Shuming Shi. "CASE: Context-Aware Semantic Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7871–78. http://dx.doi.org/10.1609/aaai.v34i05.6293.

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In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On a dataset of 1.8 million sentences thus derived, we propose a network architecture that encodes the context and seed term separately before suggesting alternative terms. The context encoder in this architecture can be easily extended by incorporating seed-aware attention. Our experiments demonstrate that competitive results are achieved with appropriate choices of context encoder and attention scoring function.

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41

Luo, Ruotian, Ning Zhang, Bohyung Han, and Linjie Yang. "Context-Aware Zero-Shot Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11709–16. http://dx.doi.org/10.1609/aaai.v34i07.6841.

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We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transferring knowledge from the objects belonging to semantically similar seen categories, we aim to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior. Specifically, we leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories. We integrate our context-aware zero-shot learning framework into the traditional zero-shot learning techniques seamlessly using a Conditional Random Field (CRF). The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods.

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42

Yang, Baosong, Jian Li, Derek F. Wong, Lidia S. Chao, Xing Wang, and Zhaopeng Tu. "Context-Aware Self-Attention Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 387–94. http://dx.doi.org/10.1609/aaai.v33i01.3301387.

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Self-attention model has shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which has proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculate the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantify how the context vectors participate in the self-attention model.

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43

Augusto, Juan C., Mario J. Quinde, Chimezie L. Oguego, and José Giménez Manuel. "Context-Aware Systems Architecture (CaSA)." Cybernetics and Systems 53, no. 4 (October 16, 2021): 319–45. http://dx.doi.org/10.1080/01969722.2021.1985226.

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44

Ruan, Dongsheng, Yu Shi, Jun Wen, Nenggan Zheng, and Min Zheng. "Spatially-Aware Context Neural Networks." IEEE Transactions on Image Processing 30 (2021): 6906–16. http://dx.doi.org/10.1109/tip.2021.3097917.

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45

Jiang, Bo, Mohamed Seif, Ravi Tandon, and Ming Li. "Context-Aware Local Information Privacy." IEEE Transactions on Information Forensics and Security 16 (2021): 3694–708. http://dx.doi.org/10.1109/tifs.2021.3087350.

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46

McLamore, Eric S., Geisianny Moreira, Diana C. Vanegas, and Shoumen Palit Austin Datta. "Context-Aware Diagnostic Specificity (CADS)." Biosensors 12, no. 2 (February 7, 2022): 101. http://dx.doi.org/10.3390/bios12020101.

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47

Lehsten, Philipp, Sebastian Bader, and Djamshid Tavangarian. "CASA - Context-Aware Service Access." Journal of Integrated Design and Process Science 18, no. 1 (2014): 21–38. http://dx.doi.org/10.3233/jid-2014-0008.

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48

Park, Jeongkyu, and Keung Hae Lee. "User-driven Context-aware Service." Journal of Korean Society for Internet Information 14, no. 4 (August 31, 2013): 1–12. http://dx.doi.org/10.7472/jksii.2013.14.4.01.

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49

An, Tae-Ki, and Moon-Hyun Kim. "Context-aware Video Surveillance System." Journal of Electrical Engineering and Technology 7, no. 1 (January 1, 2012): 115–23. http://dx.doi.org/10.5370/jeet.2012.7.1.115.

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50

Abugabah, Ahed, Xiaochun Cheng, and Jianfeng Wang. "Learning Context-Aware Outfit Recommendation." Symmetry 12, no. 6 (May 26, 2020): 873. http://dx.doi.org/10.3390/sym12060873.

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With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching.

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