All Outputs (110)

Exploring an Approach for Grouping through Predicting Group Performance from Analysis of Learner Characteristics (2018)
Conference Proceeding
Wang, J., & Kojima, K. (2018). Exploring an Approach for Grouping through Predicting Group Performance from Analysis of Learner Characteristics. . https://doi.org/10.1109/iiai-aai.2018.00062

In this paper, we present a mathematical model for forming heterogeneous groups of learners under different teaching strategies. This model requires a formulation which can effectively predict the learning performance of cooperative learning groups.... Read More about Exploring an Approach for Grouping through Predicting Group Performance from Analysis of Learner Characteristics.

Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification (2018)
Conference Proceeding
Long, Y., Tan, Y., Organisciak, D., Yang, L., & Shao, L. (2018). Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification.

Remote Heart Rate Extraction Using Microsoft KinectTM v2.0 (2018)
Conference Proceeding
Malasinghe, L., Katsigiannis, S., Ramzan, N., & Dahal, K. (2018). Remote Heart Rate Extraction Using Microsoft KinectTM v2.0. . https://doi.org/10.1145/3232059.3232060

Φ Clust: Pheromone-Based Aggregation for Robotic Swarms (2018)
Conference Proceeding
Arvin, F., Turgut, A. E., Krajnik, T., Rahimi, S., Okay, I. E., Yue, S., …Lennox, B. (2018). Φ Clust: Pheromone-Based Aggregation for Robotic Swarms. . https://doi.org/10.1109/iros.2018.8593961

In this paper, we proposed a pheromone-based aggregation method based on the state-of-the-art BEECLUST algorithm. We investigated the impact of pheromone-based communication on the efficiency of robotic swarms to locate and aggregate at areas with a... Read More about Φ Clust: Pheromone-Based Aggregation for Robotic Swarms.

Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction (2018)
Conference Proceeding
Holder, C., & Breckon, T. (2018). Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for Off-Road Path Prediction. In Proc. Intelligent Vehicles Symposium (2104-2110). https://doi.org/10.1109/IVS.2018.8500526

Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement (2018)
Conference Proceeding
Alzahrani, I. R., Ramzan, N., Katsigiannis, S., & Amira, A. (2018). Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement. . https://doi.org/10.1007/978-3-319-78753-4

RoVAN: A Rough Set-based Scheme for Cluster Head Selection in Vehicular Ad-hoc Networks (2018)
Conference Proceeding
Dua, A., Sharma, P., Ganju, S., Jindal, A., Aujla, G. S., Kumar, N., & Rodrigues, J. J. (2018). RoVAN: A Rough Set-based Scheme for Cluster Head Selection in Vehicular Ad-hoc Networks. . https://doi.org/10.1109/glocom.2018.8647576

Adaptive Skip Graph Framework for Peer-to-Peer Networks: Search Time Complexity Analysis (2018)
Conference Proceeding
Goyal, A., Batra, S., Kumar, N., Aujla, G. S., & Obaidat, M. S. (2018). Adaptive Skip Graph Framework for Peer-to-Peer Networks: Search Time Complexity Analysis. . https://doi.org/10.1109/glocom.2018.8648018

Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues (2018)
Conference Proceeding
Zarachoff, M., Sheikh-Akbari, A., & Monekosso, D. (2018). Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues.

An Intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System (2018)
Conference Proceeding
Alamri, A., Rusby, H., Cristea, A. I., Kayama, M., Khan, J., Shi, L., & Stewart, C. (2018). An Intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System.

Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering (2018)
Conference Proceeding
Khadka, A. R., Remagnino, P., & Argyriou, V. (2018). Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering. In G. DiBaja, L. Gallo, K. Yetongnon, A. Dipanda, M. CastrillonSantana, & R. Chbeir (Eds.), . https://doi.org/10.1109/sitis.2018.00039

Deterministic Dispersion of Mobile Robots in Dynamic Rings (2018)
Conference Proceeding
Agarwalla, A., Augustine, J., Moses Jr., W. K., Sankar K., M., & Sridhar, A. K. (2018). Deterministic Dispersion of Mobile Robots in Dynamic Rings. . https://doi.org/10.1145/3154273.3154294

Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy (2018)
Conference Proceeding
Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. In Proc. Towards Autonomous Robotic Systems Conference (147-158). https://doi.org/10.1007/978-3-319-96728-8_13

On Occupancy Based Randomized Routing Schemes in Large Systems of Shared Servers (2018)
Conference Proceeding
Vasantam, T., & Mazumdar, R. R. (2018). On Occupancy Based Randomized Routing Schemes in Large Systems of Shared Servers. . https://doi.org/10.1109/itc30.2018.00013

DEEP RESIDUAL NETWORK WITH SUBCLASS DISCRIMINANT ANALYSIS FOR CROWD BEHAVIOR RECOGNITION (2018)
Conference Proceeding
Mandal, B., Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). DEEP RESIDUAL NETWORK WITH SUBCLASS DISCRIMINANT ANALYSIS FOR CROWD BEHAVIOR RECOGNITION.

Influence of A-Posteriori Subcell Limiting on Fault Frequency in Higher-Order DG Schemes (2018)
Conference Proceeding
Reinarz, A., Gallard, J., & Bader, M. (2018). Influence of A-Posteriori Subcell Limiting on Fault Frequency in Higher-Order DG Schemes. . https://doi.org/10.1109/ftxs.2018.00012

A deep convolutional auto-encoder with embedded clustering (2018)
Conference Proceeding
Alqahtani, A., Xie, X., Deng, J., & Jones, M. W. (2018). A deep convolutional auto-encoder with embedded clustering. . https://doi.org/10.1109/icip.2018.8451506

DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR (2018)
Conference Proceeding
Jindal, A., Aujla, G. S., Kumar, N., Prodan, R., & Obaidat, M. S. (2018). DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR. . https://doi.org/10.1109/glocom.2018.8647926

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments (2018)
Conference Proceeding
McGough, S., Forshaw, M., Brennan, J., Al Moubayed, N., & Bonner, S. (2018). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. In 2018 Ninth International Green and Sustainable Computing Conference (IGSC) (1-8). https://doi.org/10.1109/igcc.2018.8752115

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time - volunteer computing. This has great advantages... Read More about Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments.

Behavior Analysis of Learning Methods to Overcome Stress or Pressure on Learning Situation (2018)
Conference Proceeding
Kaneko, K., & Wang, J. (2018). Behavior Analysis of Learning Methods to Overcome Stress or Pressure on Learning Situation. . https://doi.org/10.1109/iiai-aai.2018.00056

This paper discusses about learning methods to overcome stress or pressure. We had an experiment in which learners were given a quiz game for each learner to need responsibility for their team. Each learner was also given learning time for the game s... Read More about Behavior Analysis of Learning Methods to Overcome Stress or Pressure on Learning Situation.