Resource discovery using mobile agents
Conference paper
Singh, M., Cheng, X. and Belavkin, R. 2010. Resource discovery using mobile agents. Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference. Changchun, Jilin Province 18 - 22 Aug 2010 IEEE. pp. 72 -77 https://doi.org/10.1109/FCST.2010.93
| Type | Conference paper |
|---|---|
| Title | Resource discovery using mobile agents |
| Authors | Singh, M., Cheng, X. and Belavkin, R. |
| Abstract | The peer-to-peer (P2P) system has a number of nodes that are connected to each other in an unstructured or a structured overlay network. One of the most important problems in a P2P system is locating of resources that are shared by various nodes. Techniques such as Flooding and Distributed Hash-Table (DHT) has been proposed to locate resources shared by various nodes. Flooding suffers from saturation as number of nodes increase, while DHT cannot handle multiple keys to define and search a resource. We present the solution that is more efficient and effective for discovering shared resources on a network that is influenced by content shared by nodes. Our solution presents use of multiple agents that manage the shared information on a node and a mobile agent called reconnaissance agent (RA) that is responsible for querying various nodes. To reduce the search load on nodes that have unrelated content, an efficient migration route is proposed for RA that is based on cosine similarity of content shared by nodes and user query. Results show reduction in search load and traffic due to communication, and increase in locating of resources defined by multiple keys using RA that are logically similar to user query. Furthermore, the results indicate that by use of our technique the relevance of search results is higher that is obtained by minimal traffic generation/communication and hops made by RA. |
| Keywords | cosine similarity;distributed hash-table;flooding;mobile agents;peer-to-peer system;reconnaissance agent;resource discovery;shared resources;structured overlay network;mobile agents;peer-to-peer computing; |
| Research Group | Artificial Intelligence group |
| Conference | Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference |
| Page range | 72 -77 |
| ISBN | |
| Hardcover | 9781424477791 |
| Publisher | IEEE |
| Publication dates | |
| 18 Aug 2010 | |
| Publication process dates | |
| Deposited | 03 Feb 2012 |
| Output status | Published |
| Copyright Statement | Access via IEEE. |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/FCST.2010.93 |
| Scopus EID | 2-s2.0-77957919346 |
| Language | English |
| Book title | Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference. |
Permalink -
https://repository.mdx.ac.uk/item/8373v
63
total views0
total downloads1
views this month0
downloads this month
Export as
Related outputs
An evolutionary approach to automated class-specific data augmentation for image classification
Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12Social and moral psychology of COVID-19 across 69 countries
Azevedo, F., Pavlović, T., Rêgo, G., Ay, F., Gjoneska, B., Etienne, T., Ross, R., Schönegger, P., Riaño-Moreno, J., Cichocka, A., Capraro, V., Cian, L., Longoni, C., Chan, H., Van Bavel, J., Sjåstad, H., Nezlek, J., Alfano, M., Gelfand, M., Birtel, M., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbres, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jorgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomöki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., White, K., Habib, R., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P. and Sampaio, W. 2023. Social and moral psychology of COVID-19 across 69 countries. Scientific Data. 10 (1), p. 272. https://doi.org/10.1038/s41597-023-02080-8
IHWC: intelligent hidden web crawler for harvesting data in urban domains
Kaur, S., Singh, A., Geetha, G. and Cheng, X. 2021. IHWC: intelligent hidden web crawler for harvesting data in urban domains. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-022-00839-xA tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles
Sun, J., Tao, J., Zhang, H., Zhao, Y., Nie, L., Cheng, X. and Zhang, T. 2023. A tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 25 (3), pp. 2837-2846. https://doi.org/10.1109/TITS.2023.3265403Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices
Bao, Y., Qiu, W. and Cheng, X. 2023. Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices. Expert Systems. 40 (6). https://doi.org/10.1111/exsy.13220A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations
Bukhari, M., Yasmin, S., Habib, A., Cheng, X., Ullah, F., Yoo, J. and Lee, D. 2023. A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations. Journal of Healthcare Engineering. 2023 (1). https://doi.org/10.1155/2023/1847115Fake news stance detection using selective features and FakeNET
Aljrees, T., Cheng, X., Ahmed, M., Umer, M., Majeed, R., Alnowaiser, K., Abuzinadah, N. and Ashraf, I. 2023. Fake news stance detection using selective features and FakeNET. PLoS ONE. 18 (7). https://doi.org/10.1371/journal.pone.0287298PIGNUS: a deep learning model for IDS in industrial internet-of-things
Jayalaxmi, P., Saha, R., Kumar, G., Alazab, M., Conti, M. and Cheng, X. 2023. PIGNUS: a deep learning model for IDS in industrial internet-of-things. Computers and Security. 132. https://doi.org/10.1016/j.cose.2023.103315Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution
Liu, Y., Wan, B., Shi, D. and Cheng, X. 2023. Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution. Remote Sensing. 15 (2). https://doi.org/10.3390/rs15020364Value of information in the mean-square case and its application to the analysis of financial time-series forecast
Belavkin, R., Pardalos, P. and Principe, J. 2023. Value of information in the mean-square case and its application to the analysis of financial time-series forecast. Simos, D., Rasskazova, V., Archetti, F., Kotsireas, I. and Pardalos, P. (ed.) 16th International Conference on Learning and Intelligent Optimization. Milos Island, Greece 05 - 10 Jun 2022 Cham Springer. https://doi.org/10.1007/978-3-031-24866-5_39Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles
Li, S., Cheng, X., Huang, X., Otaibi, S. and Wang, H. 2023. Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles. IEEE Transactions on Intelligent Transportation Systems. 24 (2), pp. 2183-2198. https://doi.org/10.1109/TITS.2021.3137458An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches
Mittal, M., Kobielnik, M., Gupta, S., Cheng, X. and Wozniak, M. 2022. An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches. Journal of Cloud Computing. 11 (1), pp. 1-21. https://doi.org/10.1186/s13677-022-00344-zA deep convolutional neural network stacked ensemble for malware threat classification in internet of things
Naeem, H., Cheng, X., Ullah, F., Jabbar, S. and Dong, S. 2022. A deep convolutional neural network stacked ensemble for malware threat classification in internet of things. Journal of Circuits, Systems and Computers. 31 (17). https://doi.org/10.1142/s0218126622503029Efficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system
Bao, Y., Qiu, W., Tang, P. and Cheng, X. 2022. Efficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system. IEEE Journal of Biomedical and Health Informatics. 26 (5), pp. 2041-2051. https://doi.org/10.1109/JBHI.2021.3100871Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network
Yin, X., Liu, J., Cheng, X. and Xiong, X. 2022. Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network. IEEE Transactions on Intelligent Transportation Systems. 25 (7), pp. 9840-9852. https://doi.org/10.1109/TITS.2021.3118701A differentiated learning environment in domain model for learning disabled learners
Thapliyal, M., Ahuja, N., Shankar, A., Cheng, X. and Kumar, M. 2022. A differentiated learning environment in domain model for learning disabled learners. Journal of Computing in Higher Education. 34, pp. 60-82. https://doi.org/10.1007/s12528-021-09278-yOffline signature verification using deep neural network with application to computer vision
Sharma, N., Gupta, S., Mehta, P., Cheng, X., Shankar, A., Singh, P. and Nayak, S. 2022. Offline signature verification using deep neural network with application to computer vision. Journal of Electronic Imaging (JEI). 31 (4). https://doi.org/10.1117/1.JEI.31.4.041210Intrusion detection and prevention system for an IoT environment
Kumar, A., Abhishek, K., Ghalib, M., Shankar, A. and Cheng, X. 2022. Intrusion detection and prevention system for an IoT environment. Digital Communications and Networks. 8 (4), pp. 540-551. https://doi.org/10.1016/j.dcan.2022.05.027An empirical study on Retinex methods for low-light image enhancement
Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. 2022. An empirical study on Retinex methods for low-light image enhancement. Remote Sensing. 14 (18). https://doi.org/10.3390/rs14184608Value of information in the binary case and confusion matrix
Belavkin, R., Pardalos, P. and Principe, J. 2022. Value of information in the binary case and confusion matrix. 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Paris, France 18 - 22 Jul 2022 MDPI AG. pp. 1-9 https://doi.org/10.3390/psf2022005008A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality
Wan, Q., Cheng, X., Zhang, Y., Lu, G., Wang, S. and He, S. 2022. A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality. Current Bioinformatics. 17 (9), pp. 825-834. https://doi.org/10.2174/1574893617666220324155813Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation
Ullah, F., Ullah, S., Naeem, M., Mostarda, L., Rho, S. and Cheng, X. 2022. Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation. Sensors. 22 (15), pp. 1-26. https://doi.org/10.3390/s22155883RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon
Zhao, Y., Wang, Y., Cheng, X., Chen, H., Yu, H. and Ren, Y. 2022. RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon. IEEE Transactions on Intelligent Transportation Systems. 23 (7), pp. 9668-9679. https://doi.org/10.1109/TITS.2021.3105458Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Pavlović, T., Azevedo, F., De, K., Riaño-Moreno, J., Maglić, M., Gkinopoulos, T., Donnelly-Kehoe, P., Payán-Gómez, C., Huang, G., Kantorowicz, J., Birtel, M., Schönegger, P., Capraro, V., Santamaría-García, H., Yucel, M., Ibanez, A., Rathje, S., Wetter, E., Stanojević, D., van Prooijen, J., Hesse, E., Elbaek, C., Franc, R., Pavlović, Z., Mitkidis, P., Cichocka, A., Gelfand, M., Alfano, M., Ross, R., Sjåstad, H., Nezlek, J., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Amodio, D., Apps, M., Aruta, J., Besharati, S., Bor, A., Choma, B., Cunningham, W., Ejaz, W., Farmer, H., Findor, A., Gjoneska, B., Gualda, E., Huynh, T., Imran, M., Israelashvili, J., Kantorowicz-Reznichenko, E., Krouwel, A., Kutiyski, Y., Laakasuo, M., Lamm, C., Levy, J., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Olsson, A., Otterbring, T., Packer, D., Palomäki, J., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Schmid, P., Stadelmann, D., Stoica, A., Stoyanov, D., Stoyanova, K., Tewari, S., Todosijević, B., Torgler, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Zhang, Y., Abad, M., Adler, E., Mdarhri, H., Antazo, B., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., de Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Davis, V., Minda, J., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Dulleck, U., Ekmanis, J., Etienne, T., Farhana, H., Farkhari, F., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., García-Navarro, E., Garladinne, P., Gray, K., Griffin, S., Gronfeldt, B., Gruber, J., Halperin, E., Herzon, V., Hruška, M., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Keudel, O., Koppel, L., Koverola, M., Kunnari, A., Leota, J., Lermer, E., Li, C., Longoni, C., McCashin, D., Mikloušić, I., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nitschke, J., Nurse, M., de Mello, V., Palacios-Galvez, M., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Perander, S., Pitman, M., Raza, A., Rêgo, G., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Schultner, D., Schutte, E., Scott, A., Skali, A., Stefaniak, A., Sternisko, A., Strickland, B., Thomas, J., Tinghög, G., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Rooy, D., Västfjäll, D., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P., Whillans, A., Van Lange, P., Prasad, R., Onderco, M., O'Madagain, C., Nesh-Nash, T., Laguna, O., Kubin, E., Gümren, M., Fenwick, A., Ertan, A., Bernstein, M., Amara, H. and Van Bavel, J. 2022. Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus. 1 (3), pp. 1-15. https://doi.org/10.1093/pnasnexus/pgac093Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling
Wang, T., Wu, K., Du, T. and Cheng, X. 2022. Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling. Computational Intelligence. 38 (3), pp. 714-730. https://doi.org/10.1111/coin.12389National identity predicts public health support during a global pandemic
Van Bavel, J., Cichocka, A., Capraro, V., Sjåstad, H., Nezlek, J., Pavlović, T., Alfano, M., Gelfand, M., Azevedo, F., Birtel, M., Cislak, A., Lockwood, P., Ross, R., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gjoneska, B., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Riaño-Moreno, J., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvee, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Etienne, T., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Longoni, C., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., Oldemburgo de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomäki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rêgo, G., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schönegger, P., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R. and Boggio, P. 2022. National identity predicts public health support during a global pandemic. Nature Communications. 13 (1), pp. 1-14. https://doi.org/10.1038/s41467-021-27668-9CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model
Ullah, F., Naeem, M., Naeem, H., Cheng, X. and Alazab, M. 2022. CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model. International Journal of Intelligent Systems. 37 (9), pp. 5768-5795. https://doi.org/10.1002/int.22813MobiScan: an enhanced invisible screen‐camera communication system for IoT applications
Zhang, X., Liu, J., Ba, Z., Tao, Y. and Cheng, X. 2022. MobiScan: an enhanced invisible screen‐camera communication system for IoT applications. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4151Secure smart contracts for cloud-based manufacturing using Ethereum blockchain
Kumar, A., Abhishek, K., Nerurkar, P., Ghalib, M., Shankar, A. and Cheng, X. 2022. Secure smart contracts for cloud-based manufacturing using Ethereum blockchain. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4129Editorial: Security of cloud service for the manufacturing industry
Cheng, X., Liu, Z. and Ning, Y. 2022. Editorial: Security of cloud service for the manufacturing industry. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4369Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors
Li, S., Chen, Y., Wu, X., Cheng, X. and Tian, Z. 2021. Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors. Journal of Sensors. 2021, pp. 1-12. https://doi.org/10.1155/2021/8820413Secure and energy-efficient smart building architecture with emerging technology IoT
Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X. and Singh, P. 2021. Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications. 176, pp. 207-217. https://doi.org/10.1016/j.comcom.2021.06.003Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment
Narayanan, S., Baby, C., Perumal, B., Bhatt, R., Cheng, X., Ghalib, M. and Shankar, A. 2021. Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment. International Journal of Intelligent Systems. 36 (8), pp. 4280-4267. https://doi.org/10.1002/int.22459PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation
Wang, S., Zhang, Y., Cheng, X., Zhang, X. and Zhang, Y. 2021. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation. Computational and Mathematical Methods in Medicine. 2021, pp. 1-18. https://doi.org/10.1155/2021/6633755Task bundling in worker-centric mobile crowdsensing
Zhao, T., Yang, Y., Wang, E., Mumtaz, S. and Cheng, X. 2021. Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems. 36 (9), pp. 4936-4961. https://doi.org/10.1002/int.22497ShadowFPE: new encrypted web application solution based on shadow DOM
Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm
Liu, W., Huang, Y., Ye, Z., Cai, W., Yang, S., Cheng, X. and Frank, I. 2020. Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Applied Sciences. 10 (9). https://doi.org/10.3390/app10093225Reliability analysis of an air traffic network: from network structure to transport function
Li, S., Zhang, Z. and Cheng, X. 2020. Reliability analysis of an air traffic network: from network structure to transport function. Applied Sciences. 10 (9). https://doi.org/10.3390/app10093168XOR multiplexing technique for nanocomputers
Yu, L., Diao, M., Chen, X. and Cheng, X. 2020. XOR multiplexing technique for nanocomputers. Applied Sciences. 10 (8). https://doi.org/10.3390/app10082825Fine-grained action recognition by motion saliency and mid-level patches
Liu, F., Zhao, L., Cheng, X., Dai, Q., Shi, X. and Qiao, J. 2020. Fine-grained action recognition by motion saliency and mid-level patches. Applied Sciences. 10 (8). https://doi.org/10.3390/app10082811Adaptive dynamic disturbance strategy for differential evolution algorithm
Wang, T., Wu, K., Du, T. and Cheng, X. 2020. Adaptive dynamic disturbance strategy for differential evolution algorithm. Applied Sciences. 10 (6). https://doi.org/10.3390/app10061972A game theoretic analysis of resource mining in blockchain
Singh, R., Dwivedi, A., Srivastava, G., Wisznieska-Mayszkiel, A. and Cheng, X. 2020. A game theoretic analysis of resource mining in blockchain. Cluster Computing. 23 (3), pp. 2035-2046. https://doi.org/10.1007/s10586-020-03046-wHybridization of cognitive computing for food services
Zhang, X., Yang, S., Srivastava, G., Chen, M. and Cheng, X. 2020. Hybridization of cognitive computing for food services. Applied Soft Computing. 89. https://doi.org/10.1016/j.asoc.2019.106051Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network
Xie, X., Zhang, Z., Wang, J. and Cheng, X. 2019. Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network. Journal on Communications. 40 (8), pp. 143-150. https://doi.org/10.11959/j.issn.1000-436x.2019172Incremental association rule mining based on matrix compression for edge computing
Zhou, D., Ouyang, M., Kuang, Z., Li, Z., Zhou, J. and Cheng, X. 2019. Incremental association rule mining based on matrix compression for edge computing. IEEE Access. 7, pp. 1730444-173053. https://doi.org/10.1109/ACCESS.2019.2956823Facial landmark detection via attention-adaptive deep network
Sadiq, M., Shi, D., Guo, M. and Cheng, X. 2019. Facial landmark detection via attention-adaptive deep network. IEEE Access. 7, pp. 181041-181050. https://doi.org/10.1109/ACCESS.2019.2955156Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha
Yhang, X., Mohanty, S., Parida, A., Pani, S., Dong, B. and Cheng, X. 2020. Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access. 8, pp. 30223-30233. https://doi.org/10.1109/ACCESS.2020.2972435A sparse Bayesian learning method for structural equation model-based gene regulatory network inference
Li, Y., Liu, D., Chu, J., Zhu, Y., Liu, J. and Cheng, X. 2020. A sparse Bayesian learning method for structural equation model-based gene regulatory network inference. IEEE Access. 8, pp. 40067-40080. https://doi.org/10.1109/ACCESS.2020.2976743Micro-distortion detection of lidar scanning signals based on geometric analysis
Liu, S., Chen, X., Li, Y. and Cheng, X. 2019. Micro-distortion detection of lidar scanning signals based on geometric analysis. Symmetry. 11 (12), pp. 2-13. https://doi.org/10.3390/sym11121471Verifying cryptographic protocols
Ma, X. and Cheng, X. 2005. Verifying cryptographic protocols. IEEE Journal of Intelligent Cybernetic Systems.Verifying security protocols by knowledge analysis
Ma, X. and Cheng, X. 2008. Verifying security protocols by knowledge analysis. International Journal of Security and Networks. 3 (3), pp. 183-192. https://doi.org/10.1504/IJSN.2008.020092A face recognition algorithm using a fusion method based on Adaboost Bidirectional 2DLDA
Wang, S., Li, W., Cheng, X., Wang, Y. and Jiang, Y. 2012. A face recognition algorithm using a fusion method based on Adaboost Bidirectional 2DLDA. Advances in Information Sciences and Service Sciences. 4 (23), pp. 181-188. https://doi.org/10.4156/AISS.vol4.issue23.23A security design for cloud computing: an implementation of an on premises authentication with Kerberos and IPSec within a network
Umar, M. and Cheng, X. 2012. A security design for cloud computing: an implementation of an on premises authentication with Kerberos and IPSec within a network. International Journal of Advanced Research in Computer Science. 3 (1), pp. 10-16. https://doi.org/10.26483/ijarcs.v3i1.6040Comparative experiments on resource discovery in P2P networks
Gautam, S. and Cheng, X. 2014. Comparative experiments on resource discovery in P2P networks. Journal of Next Generation Information Technology. 5 (1), pp. 89-98.Unbalanced private set intersection cardinality protocol with low communication cost
Lv, S., Ye, J., Yin, S. and Cheng, X. 2020. Unbalanced private set intersection cardinality protocol with low communication cost. Future Generation Computer Systems. 102, pp. 1054-1061. https://doi.org/10.1016/j.future.2019.09.022Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface
Men, J., Xu, G., Han, Z., Sun, Z., Zhou, X., Lian, W. and Cheng, X. 2019. Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface. IEEE Access. 7, pp. 103751-103759. https://doi.org/10.1109/ACCESS.2019.2931061Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs
Shi, F., Chen, Z. and Cheng, X. 2020. Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs. IEEE Access. 8, pp. 2447-2454. https://doi.org/10.1109/ACCESS.2019.2923059Introduction of key problems in long-distance learning and training
Liu, S., Li, Z., Zhang, Y. and Cheng, X. 2019. Introduction of key problems in long-distance learning and training. Mobile Networks and Applications. 24 (1), pp. 1-4. https://doi.org/10.1007/s11036-018-1136-6Vulnerabilities and limitations of MQTT protocol used between IoT devices
Dinculeană, D. and Cheng, X. 2019. Vulnerabilities and limitations of MQTT protocol used between IoT devices. Applied Sciences. 9 (5). https://doi.org/10.3390/app9050848Imbalanced big data classification based on virtual reality in cloud computing
Xie, W. and Cheng, X. 2020. Imbalanced big data classification based on virtual reality in cloud computing. Multimedia Tools and Applications. 79 (23-24), pp. 16403-16420. https://doi.org/10.1007/s11042-019-7317-xPlatform of quality evaluation system for multimedia video communication based NS2
Yu, G., Xu, J. and Cheng, X. 2018. Platform of quality evaluation system for multimedia video communication based NS2. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1164-xRelation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence
Belavkin, R. 2018. Relation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence. IGAIA IV 2016: Information Geometry and Its Applications. Liblice, Czech Republic 12 - 17 Jun 2016 Springer International Publishing. https://doi.org/10.1007/978-3-319-97798-0_15An authentication scheme to defend against UDP DrDoS attacks in 5G networks
Huang, H., Hu, L., Chu, J. and Cheng, X. 2019. An authentication scheme to defend against UDP DrDoS attacks in 5G networks. IEEE Access. 7, pp. 175970-175979. https://doi.org/10.1109/ACCESS.2019.2957565Data provenance with retention of reference relations
Wang, C., Yang, L., Wu, Y., Wu, Y., Cheng, X., Li, Z. and Liu, Z. 2018. Data provenance with retention of reference relations. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2876879Editorial: Recent advances of content understanding in image and multimedia
Liu, S., Cheng, X. and Min, G. 2017. Editorial: Recent advances of content understanding in image and multimedia. Recent Patents on Computer Science. 10 (1), pp. 2-5. https://doi.org/10.2174/221327591001170808093310Channel state information-based detection of Sybil attacks in wireless networks
Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. 2018. Channel state information-based detection of Sybil attacks in wireless networks. Journal of Internet Services and Information Security. 8 (1), pp. 2-17. https://doi.org/10.22667/JISIS.2018.02.28.002Environmental pleiotropy and demographic history direct adaptation under antibiotic selection
Gifford, D., Krašovec, R., Aston, E., Belavkin, R., Channon, A. and Knight, C. 2018. Environmental pleiotropy and demographic history direct adaptation under antibiotic selection. Heredity. 121 (5), pp. 438-448. https://doi.org/10.1038/s41437-018-0137-3Research on trust model in container-based cloud service
Xie, X., Yuan, T., Zhou, X. and Cheng, X. 2018. Research on trust model in container-based cloud service. Computers, Materials and Continua. 56 (2), pp. 273-283. https://doi.org/10.3970/cmc.2018.03587Opposing effects of final population density and stress on Escherichia coli mutation rate
Krašovec, R., Richards, H., Gifford, D., Belavkin, R., Channon, A., Aston, E., McBain, A. and Knight, C. 2018. Opposing effects of final population density and stress on Escherichia coli mutation rate. The ISME journal. 12 (12), pp. 2981-2987. https://doi.org/10.1038/s41396-018-0237-3Introduction of recent advanced hybrid information processing
Liu, S., Li, Z., Cheng, X. and Lin, Y. 2018. Introduction of recent advanced hybrid information processing. Mobile Networks and Applications. 23 (4), pp. 673-676. https://doi.org/10.1007/s11036-018-1013-3Accurate Sybil attack detection based on fine-grained physical channel information
Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. 2018. Accurate Sybil attack detection based on fine-grained physical channel information. Sensors. 18 (3). https://doi.org/10.3390/s18030878DivORAM: Towards a practical oblivious RAM with variable block size
Liu, Z., Huang, Y., Li, J., Cheng, X. and Shen, C. 2018. DivORAM: Towards a practical oblivious RAM with variable block size. Information Sciences. 447, pp. 1-11. https://doi.org/10.1016/j.ins.2018.02.071M-SSE: an effective searchable symmetric encryption with enhanced security for mobile devices
Gao, C., Lv, S., Wei, Y., Wang, Z., Liu, Z. and Cheng, X. 2018. M-SSE: an effective searchable symmetric encryption with enhanced security for mobile devices. IEEE Access. 6, pp. 38860-38869. https://doi.org/10.1109/ACCESS.2018.2852329A distributed anomaly detection system for in-vehicle network using HTM
Wang, C., Zhao, Z., Gong, L., Zhu, L., Liu, Z. and Cheng, X. 2018. A distributed anomaly detection system for in-vehicle network using HTM. IEEE Access. 6, pp. 9091-9098. https://doi.org/10.1109/ACCESS.2018.2799210Crime pattern recognition based on high-performance computing
Eissa, A., Cheng, X. and Petridis, M. 2018. Crime pattern recognition based on high-performance computing. 2017 International Conference Next Generation Community Policing. Heraklion, Crete, Greece 25 - 27 Oct 2017A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface
Liu, S., Pan, Z. and Cheng, X. 2017. A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals. 25 (04), pp. 1740004-1-11. https://doi.org/10.1142/s0218348x17400047Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover
Aston, E., Channon, A., Belavkin, R., Gifford, D., Krašovec, R. and Knight, C. 2017. Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover. Scientific Reports. 7 (1). https://doi.org/10.1038/s41598-017-14628-xSpontaneous mutation rate is a plastic trait associated with population density across domains of life
Krašovec, R., Richards, H., Gifford, D., Hatcher, C., Faulkner, K., Belavkin, R., Channon, A., Aston, E., McBain, A. and Knight, C. 2017. Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLOS Biology. 15 (8). https://doi.org/10.1371/journal.pbio.2002731Degradation and encryption for outsourced PNG images in cloud storage
Wang, Y., Du, J., Cheng, X., Liu, Z. and Lin, K. 2016. Degradation and encryption for outsourced PNG images in cloud storage. International Journal of Grid and Utility Computing. 7 (1), pp. 22-28. https://doi.org/10.1504/IJGUC.2016.073773Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud
Xie, X., Liu, R., Cheng, X., Hu, X. and Ni, J. 2016. Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud. Intelligent Automation & Soft Computing: An International Journal . 22 (4), pp. 561-566.Monotonicity of fitness landscapes and mutation rate control
Belavkin, R., Channon, A., Aston, E., Aston, J., Krašovec, R. and Knight, C. 2016. Monotonicity of fitness landscapes and mutation rate control. Journal of Mathematical Biology. 73 (6-7), pp. 1491-1524. https://doi.org/10.1007/s00285-016-0995-3Asymmetric topologies on statistical manifolds
Belavkin, R. 2015. Asymmetric topologies on statistical manifolds. 2nd International Conference on Geometric Science of Information. Palaiseau, France 28 - 30 Oct 2015 Springer. https://doi.org/10.1007/978-3-319-25040-3_23Numeric characteristics of generalized M-set with its asymptote
Liu, S., Cheng, X., Fu, W., Zhou, Y. and Li, Q. 2014. Numeric characteristics of generalized M-set with its asymptote. Applied Mathematics and Computation. 243, pp. 767-774. https://doi.org/10.1016/j.amc.2014.06.016Local semantic indexing for resource discovery on overlay network using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2014. Local semantic indexing for resource discovery on overlay network using mobile agents. International Journal of Computational Intelligence Systems. 7 (3), pp. 432-455. https://doi.org/10.1080/18756891.2013.856257Where antibiotic resistance mutations meet quorum-sensing
Krašovec, R., Belavkin, R., Aston, J., Channon, A., Aston, E., Rash, B., Kadirvel, M., Forbes, S. and Knight, C. 2014. Where antibiotic resistance mutations meet quorum-sensing. Microbial Cell. 1 (7), pp. 250-252. https://doi.org/10.15698/mic2014.07.158On variational definition of quantum entropy
Belavkin, R. 2014. On variational definition of quantum entropy. 34th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2014). Clos Lucé, Amboise, France 21 - 26 Sep 2014 American Institute of Physics. https://doi.org/10.1063/1.4905979Asymmetry of risk and value of information
Belavkin, R. 2014. Asymmetry of risk and value of information. in: Vogiatzis, C., Walteros, J. and Pardalos, P. (ed.) Dynamics of Information Systems: Computational and Mathematical Challenges Springer.Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions
Krašovec, R., Belavkin, R., Aston, J., Channon, A., Aston, E., Rash, B., Kadirvel, M., Forbes, S. and Knight, C. 2014. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nature Communications. 5, pp. 1-8. https://doi.org/10.1038/ncomms4742Fractal property of generalized M-set with rational number exponent
Liu, S., Cheng, X., Lan, C., Fu, W., Zhou, J., Li, Q. and Gao, G. 2013. Fractal property of generalized M-set with rational number exponent. Applied Mathematics and Computation. 220, pp. 668-675. https://doi.org/10.1016/j.amc.2013.06.096Mechanical verification of cryptographic protocols
Cheng, X., Ma, X., Huang, S. and Cheng, M. 2010. Mechanical verification of cryptographic protocols. Network Security. https://doi.org/10.1007/978-0-387-73821-5_5DNSsec in Isabelle – replay attack and origin authentication
Kammueller, F., Kirsal-Ever, Y. and Cheng, X. 2013. DNSsec in Isabelle – replay attack and origin authentication. SMC 2013: IEEE International Conference on Systems, Man, and Cybernetics. Manchester, UK 13 - 16 Oct 2013 IEEE. pp. 4772-4777 https://doi.org/10.1109/SMC.2013.812Minimum of information distance criterion for optimal control of mutation rate in evolutionary systems
Belavkin, R. 2013. Minimum of information distance criterion for optimal control of mutation rate in evolutionary systems. in: Accardi, L., Freudenberg, W. and Ohya, M. (ed.) Quantum Bio-Informatics V : Proceedings of the Quantum Bio-Informatics 2011. Tokyo University of Science, Japan, 7 – 12 March 2011 World Scientific.Law of cosines and Shannon-Pythagorean theorem for quantum information
Belavkin, R. 2013. Law of cosines and Shannon-Pythagorean theorem for quantum information. in: Nielsen, F. and Barbaresco, F. (ed.) Geometric Science of Information : First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings Berlin Springer.Optimal measures and Markov transition kernels
Belavkin, R. 2013. Optimal measures and Markov transition kernels. Journal of Global Optimization. 55 (2), pp. 387-416. https://doi.org/10.1007/s10898-012-9851-1Critical mutation rate has an exponential dependence on population size
Channon, A., Aston, E., Day, C., Belavkin, R. and Knight, C. 2011. Critical mutation rate has an exponential dependence on population size. Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M. and Doursat, R. (ed.) ECAL 2011: The 11th European Conference on Artificial Life. Paris, France 08 - 12 Aug 2011 The MIT Press. pp. 117-124 https://doi.org/10.7551/978-0-262-29714-1-ch021Dynamics of information and optimal control of mutation in evolutionary systems
Belavkin, R. 2012. Dynamics of information and optimal control of mutation in evolutionary systems. in: Sorokin, A., Murphey, R., Thai, M. and Pardalos, P. (ed.) Dynamics of Information Systems: Mathematical Foundations New York Springer.A cooperative particle swarm optimizer with statistical variable interdependence learning
Sun, L., Yoshida, S., Cheng, X. and Liang, Y. 2012. A cooperative particle swarm optimizer with statistical variable interdependence learning. Information Sciences. 186 (1), pp. 20-39. https://doi.org/10.1016/j.ins.2011.09.033Maximal connectivity and constraints in the human brain
Belavkin, R. 2012. Maximal connectivity and constraints in the human brain. in: Pardalos, P., Coleman, T. and Xanthopoulos, P. (ed.) Optimization and data analysis in biomedical informatics New York Springer.On evolution of an information dynamic system and its generating operator
Belavkin, R. 2012. On evolution of an information dynamic system and its generating operator. Optimization Letters. 6 (5), pp. 827-840. https://doi.org/10.1007/s11590-011-0325-zSurvey of grid resource monitoring and prediction strategies.
Hu, L., Cheng, X. and Che, X. 2010. Survey of grid resource monitoring and prediction strategies. International Journal of Intelligent Information Processing. 1 (2).Efficient identity-based broadcast encryption without random oracles.
Hu, L., Liu, Z. and Cheng, X. 2010. Efficient identity-based broadcast encryption without random oracles. Journal of Computers. 5 (3), pp. 331-336.Solving job shop scheduling problem using genetic algorithm with penalty function
Sun, L., Cheng, X. and Liang, Y. 2010. Solving job shop scheduling problem using genetic algorithm with penalty function. International Journal of Intelligent Information Processing. 1 (2), pp. 65-77.Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences
Belavkin, R., Channon, A., Aston, E., Aston, J. and Knight, C. 2011. Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences. Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M. and Doursat, R. (ed.) ECAL 2011: The 11th European Conference on Artificial Life. Paris, France 08 - 12 Aug 2011 The MIT Press. pp. 85-92 https://doi.org/10.7551/978-0-262-29714-1-ch017Mutation and optimal search of sequences in nested Hamming spaces
Belavkin, R. 2011. Mutation and optimal search of sequences in nested Hamming spaces. IEEE Information Theory Workshop (ITW). Paraty, Brazil 16 - 20 Oct 2011 IEEE. pp. 90-94 https://doi.org/10.1109/ITW.2011.6089592Conflict resolution and learning probability matching in a neural cell-assembly architecture
Belavkin, R. and Huyck, C. 2011. Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research. 12 (2), pp. 93-101. https://doi.org/10.1016/j.cogsys.2010.08.003Towards a theory of decision-making without paradoxes.
Belavkin, R. 2006. Towards a theory of decision-making without paradoxes. Fum, D., Missier, F. and Stocco, A. (ed.) Proceedings of the Seventh International Conference on Cognitive Modeling. Trieste, Italy 05 - 08 Apr 2006 Trieste, Italy Edizioni Goliardiche. pp. 38-43Emergence of rules in cell assemblies of fLIF neurons.
Belavkin, R. and Huyck, C. 2008. Emergence of rules in cell assemblies of fLIF neurons. The 18th European Conference on Artificial Intelligence. University of Patras, Greece 21 - 25 Jul 2008A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.
Belavkin, R. and Huyck, C. 2009. A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies. 9th International conference on cognitive modelling {ICCM 2009]. University of Manchester 24 - 26 Jul 2009Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization
Cheng, X., Che, X. and Hu, L. 2010. Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization. International Journal of Computational Intelligence Systems. 3 (1), pp. 70-83. https://doi.org/10.2991/ijcis.2010.3.1.7Utility and value of information in cognitive science, biology and quantum theory.
Belavkin, R. 2010. Utility and value of information in cognitive science, biology and quantum theory. Accardi, L., Freudenberg, W. and Ohya, M. (ed.) Quantum Bio-Informatics III. Tokyo, Japan 11 - 14 Mar 2009 London World Scientific. pp. 33-47 https://doi.org/10.1142/9789814304061_0004Information trajectory of optimal learning
Belavkin, R. 2010. Information trajectory of optimal learning. in: Hirsch, M., Pardalos, P. and Murphey, R. (ed.) Dynamics of Information Systems: Theory and Applications Springer.Learning behaviour patterns of classroom and distance students using flexible learning resources.
Dimitrova, M., Belavkin, R., Milankovic-Atkinson, M., Sadler, C. and Murphy, A. 2003. Learning behaviour patterns of classroom and distance students using flexible learning resources. in: Lee, K. and Mitchell, K. (ed.) International conference on computers in education 2003: a conference of the Asia-Pacific chapter of the association for the advancement of computing in education (AACE). Hong Kong ICCE.Resource discovery using mobile agents
Singh, M., Cheng, X. and He, X. 2009. Resource discovery using mobile agents. in: Tao, D., Xu, D. and Li, X. (ed.) Semantic Mining Technologies for Multimedia Databases. New York, USA Information Science Reference. pp. 419-448New e-Learning system architecture based on knowledge engineering technology
Li, Y., Chen, Z., Huang, R. and Cheng, X. 2009. New e-Learning system architecture based on knowledge engineering technology. 2009 IEEE International Conference on Systems, Man and Cybernetics. San Antonio, TX, USA 11 - 14 Oct 2009 IEEE. pp. 5140-5144 https://doi.org/10.1109/ICSMC.2009.5346013Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals
Li, Y., Guo, H., Gao, G., Huang, R. and Cheng, X. 2009. Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals. in: Kim, J., Delen, D., Jinsoo, P., Ko, F., Rui, C., Hyung, J., Lee, W. and Kou, G. (ed.) NCM 2009: Fifth International Joint Conference on INC, IMS, and IDC; [proceedings]. IEEE. pp. 1081-1086Bounds of optimal learning
Belavkin, R. 2009. Bounds of optimal learning. 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Nashville, TN, USA 30 Mar - 02 Apr 2009 IEEE. pp. 199-204 https://doi.org/10.1109/ADPRL.2009.4927545Formal verification of the merchant registration phase of the SET protocol.
Cheng, X. and Ma, X. 2005. Formal verification of the merchant registration phase of the SET protocol. International Journal of Automation and Computing. 2 (2), pp. 155-162. https://doi.org/10.1007/s11633-005-0155-5Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons.
Huyck, C. and Belavkin, R. 2006. Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons. 7th International Conference on Cognitive Modelling. Trieste, Italy pp. 142-147The use of entropy for analysis and control of cognitive models
Belavkin, R. and Ritter, F. 2003. The use of entropy for analysis and control of cognitive models. The Fifth International Conference on Cognitive Modelling. Bamberg, Germany 2003 pp. 21-26Conflict resolution by random estimated costs
Belavkin, R. 2003. Conflict resolution by random estimated costs. 17th European Simulation Multiconference. Nottingham UK pp. 105-110On relation between emotion and entropy
Belavkin, R. 2004. On relation between emotion and entropy. AISB'04 Symposium on Emotion, Cognition and Affective Computing. Leeds UK pp. 1-8OPTIMIST: A new conflict resolution algorithm for ACT-R.
Belavkin, R. and Ritter, F. 2004. OPTIMIST: A new conflict resolution algorithm for ACT-R. Sixth International Conference on Cognitive Modelling. Mahwah, NJ pp. 40-45