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486 results on '"Albaba A"'

1. RILe: Reinforced Imitation Learning

Author

Albaba, Mert, Christen, Sammy, Langarek, Thomas, Gebhardt, Christoph, Hilliges, Otmar, and Black, Michael J.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Reinforcement Learning has achieved significant success in generating complex behavior but often requires extensive reward function engineering. Adversarial variants of Imitation Learning and Inverse Reinforcement Learning offer an alternative by learning policies from expert demonstrations via a discriminator. However, these methods struggle in complex tasks where randomly sampling expert-like behaviors is challenging. This limitation stems from their reliance on policy-agnostic discriminators, which provide insufficient guidance for agent improvement, especially as task complexity increases and expert behavior becomes more distinct. We introduce RILe (Reinforced Imitation Learning environment), a novel trainer-student system that learns a dynamic reward function based on the student's performance and alignment with expert demonstrations. In RILe, the student learns an action policy while the trainer, using reinforcement learning, continuously updates itself via the discriminator's feedback to optimize the alignment between the student and the expert. The trainer optimizes for long-term cumulative rewards from the discriminator, enabling it to provide nuanced feedback that accounts for the complexity of the task and the student's current capabilities. This approach allows for greater exploration of agent actions by providing graduated feedback rather than binary expert/non-expert classifications. By reducing dependence on policy-agnostic discriminators, RILe enables better performance in complex settings where traditional methods falter, outperforming existing methods by 2x in complex simulated robot-locomotion tasks.

Published
2024

2. Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention

Author

Ozdel, Suleyman, Rong, Yao, Albaba, Berat Mert, Kuo, Yen-Ling, Wang, Xi, and Kasneci, Enkelejda

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Humans utilize their gaze to concentrate on essential information while perceiving and interpreting intentions in videos. Incorporating human gaze into computational algorithms can significantly enhance model performance in video understanding tasks. In this work, we address a challenging and innovative task in video understanding: predicting the actions of an agent in a video based on a partial video. We introduce the Gaze-guided Action Anticipation algorithm, which establishes a visual-semantic graph from the video input. Our method utilizes a Graph Neural Network to recognize the agent's intention and predict the action sequence to fulfill this intention. To assess the efficiency of our approach, we collect a dataset containing household activities generated in the VirtualHome environment, accompanied by human gaze data of viewing videos. Our method outperforms state-of-the-art techniques, achieving a 7\% improvement in accuracy for 18-class intention recognition. This highlights the efficiency of our method in learning important features from human gaze data., Comment: 2024 Symposium on Eye Tracking Research and Applications (ETRA24), Glasgow, United Kingdom

Published
2024
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3. A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos

Author

Ozdel, Suleyman, Rong, Yao, Albaba, Berat Mert, Kuo, Yen-Ling, Wang, Xi, and Kasneci, Enkelejda

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important. To effectively automate the process of video analysis based on eye-tracking data, it is important to accurately replicate human gaze behavior. However, this task presents significant challenges due to the inherent complexity and ambiguity of human gaze patterns. In this work, we introduce a novel method for simulating human gaze behavior. Our approach uses a transformer-based reinforcement learning algorithm to train an agent that acts as a human observer, with the primary role of watching videos and simulating human gaze behavior. We employed an eye-tracking dataset gathered from videos generated by the VirtualHome simulator, with a primary focus on activity recognition. Our experimental results demonstrate the effectiveness of our gaze prediction method by highlighting its capability to replicate human gaze behavior and its applicability for downstream tasks where real human-gaze is used as input., Comment: 2024 Symposium on Eye Tracking Research and Applications (ETRA24), Glasgow, United Kingdom

Published
2024
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4. Impact of an Inter-Professional Clinic on Pancreatic Cancer Outcomes: A Retrospective Cohort Study

Author

Gordon Taylor Moffat, Zachary Coyne, Hamzeh Albaba, Kyaw Lwin Aung, Anna Dodd, Osvaldo Espin-Garcia, Shari Moura, Steven Gallinger, John Kim, Adriana Fraser, Shawn Hutchinson, Carol-Anne Moulton, Alice Wei, Ian McGilvray, Neesha Dhani, Raymond Jang, Elena Elimova, Malcolm Moore, Rebecca Prince, and Jennifer Knox

Subjects
pancreatic cancer, inter-professional clinic, improved care, time to biopsy, overall survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges in diagnosis, staging, and appropriate treatment. Furthermore, patients with PDAC often experience complex symptomatology and psychosocial implications that require multi-disciplinary and inter-professional supportive care management from health professionals. Despite these hurdles, the implementation of inter-professional clinic approaches showed promise in enhancing clinical outcomes. To assess the effectiveness of such an approach, we examined the impact of the Wallace McCain Centre for Pancreatic Cancer (WMCPC), an inter-professional clinic for patients with PDAC at the Princess Margaret Cancer Centre (PM). Methods: This retrospective cohort study included all patients diagnosed with PDAC who were seen at the PM before (July 2012–June 2014) and after (July 2014–June 2016) the establishment of the WMCPC. Standard therapies such as surgery, chemotherapy, and radiation therapy remained consistent across both time periods. The cohorts were compared in terms of survival rates, disease stage, referral patterns, time to treatment, symptoms, and the proportion of patients assessed and supported by nursing and allied health professionals. Results: A total of 993 patients were included in the review, comprising 482 patients pre-WMCPC and 511 patients post-WMCPC. In the multivariate analysis, adjusting for ECOG (Eastern Cooperative Oncology Group) and stage, it was found that post-WMCPC patients experienced longer median overall survival (mOS, HR 0.84, 95% CI 0.72–0.98, p = 0.023). Furthermore, the time from referral to initial consultation date decreased significantly from 13.4 to 8.8 days in the post-WMCPC cohort (p < 0.001), along with a reduction in the time from the first clinic appointment to biopsy (14 vs. 8 days, p = 0.022). Additionally, patient-reported well-being scores showed improvement in the post-WMCPC cohort (p = 0.02), and these patients were more frequently attended to by nursing and allied health professionals (p < 0.001). Conclusions: The implementation of an inter-professional clinic for patients diagnosed with PDAC led to improvements in overall survival, patient-reported well-being, time to initial assessment visit and pathological diagnosis, and symptom management. These findings advocate for the adoption of an inter-professional clinic model in the treatment of patients with PDAC.

Published
2024
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5. MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs

Author

Langerak, Thomas, Christen, Sammy, Albaba, Mert, Gebhardt, Christoph, and Hilliges, Otmar

Subjects
Computer Science - Human-Computer Interaction
Abstract

Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines.

Published
2022

7. Classification of variants of reduced penetrance in high-penetrance cancer susceptibility genes: Framework for genetics clinicians and clinical scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK)

Author

Turnbull, C., Garrett, A., Loong, L., Choi, S., Torr, B., Allen, S., Durkie, M., Callaway, A., Drummond, J., Burghel, G.J., Robinson, R., Berry, I.R., Wallace, A.J., Eccles, D.M., Tischkowitz, M., Ellard, S., Hanson, H., Baple, E., Evans, D.G., Woodward, E., Lalloo, F., Samant, S., Lucassen, A., Znaczko, A., Shaw, A., Ansari, A., Kumar, A., Donaldson, A., Murray, A., Ross, A., Taylor-Beadling, A., Taylor, A., Innes, A., Brady, A., Kulkarni, A., Hogg, A.C., Bowden, A. Ramsay, Hadonou, A., Coad, B., McIldowie, B., Speight, B., DeSouza, B., Mullaney, B., McKenna, C., Brewer, C., Olimpio, C., Clabby, C., Crosby, C., Jenkins, C., Armstrong, C., Bowles, C., Brooks, C., Byrne, C., Maurer, C., Baralle, D., Chubb, D., Stobo, D., Moore, D., O'Sullivan, D., Donnelly, D., Randhawa, D., Halliday, D., Atkinson, E., Rauter, E., Johnston, E., Maher, E., Sofianopoulou, E., Petrides, E., McRonald, F., Pelz, F., Frayling, I., Corbett, G., Rea, G., Clouston, H., Powell, H., Williamson, H., Carley, H., Thomas, H.J.W., Tomlinson, I., Cook, J., Hoyle, J., Tellez, J., Whitworth, J., Williams, J., Murray, J., Campbell, J., Tolmie, J., Field, J., Mason, J., Burn, J., Bruty, J., Callaway, J., Grant, J., Del Rey Jimenez, J., Pagan, J., VanCampen, J., Barwell, J., Monahan, K., Tatton-Brown, K., Ong, K.R., Murphy, K., Andrews, K., Mokretar, K., Cadoo, K., Smith, K., Baker, K., Brown, K., Reay, K., McKay Bounford, K., Bradshaw, K., Russell, K., Stone, K., Snape, K., Crookes, L., Reed, L., Taggart, L., Yarram, L., Cobbold, L., Walker, L., Hawkes, L., Busby, L., Izatt, L., Kiely, L., Hughes, L., Side, L., Sarkies, L., Greenhalgh, K.-L., Shanmugasundaram, M., Duff, M., Bartlett, M., Watson, M., Owens, M., Bradford, M., Huxley, M., Slean, M., Ryten, M., Smith, M., Ahmed, M., Roberts, N., O'Brien, C., Middleton, O., Tarpey, P., Logan, P., Dean, P., May, P., Brace, P., Tredwell, R., Harrison, R., Hart, R., Kirk, R., Martin, R., Nyanhete, R., Wright, R., Davidson, R., Cleaver, R., Talukdar, S., Butler, S., Sampson, J., Ribeiro, S., Dell, S., Mackenzie, S., Hegarty, S., Albaba, S., McKee, S., Palmer-Smith, S., Heggarty, S., MacParland, S., Greville-Heygate, S., Daniels, S., Prapa, S., Abbs, S., Tennant, S., Hardy, S., MacMahon, S., McVeigh, T., Foo, T., Bedenham, T., Cranston, T., McDevitt, T., Clowes, V., Tripathi, V., McConnell, V., Woodwaer, N., Wallis, Y., Kemp, Z., Mullan, G., Pierson, L., Rainey, L., Joyce, C., Timbs, A., Reuther, A.-M., Frugtniet, B., Husher, C., Lawn, C., Corbett, C., Nocera-Jijon, D., Reay, D., Cross, E., Ryan, F., Lindsay, H., Oliver, J., Dring, J., Spiers, J., Harper, J., Ciucias, K., Connolly, L., Tsang, M., Brown, R., Shepherd, S., Begum, S., Tadiso, T., Linton-Willoughby, T., Heppell, H., Sahan, K., Worrillow, L., Allen, Z., Watt, C., Hegarty, M., Mitchell, R., Coles, R., Nickless, G., Cojocaru, E., Doal, I., Sava, F., McCarthy, C., Jeeneea, R., Goudie, D., McConachie, M., Botosneanu, S., Kavanaugh, G., Sherlaw, C., Tsoulaki, O., Forde, C., Petley, E., Jones, A.-B., Oprych, K., Pryde, S., Hyder, Z., Elkhateeb, N., Braham, R., Hanington, L., Huntley, C., Irving, R., Sadan, A., Ramos, M., Elliot, C., Wren, D., Lobo, D., McLean, J., May, D., Kearney, L., Campbell, T., Asakura, K., Alwadi, L., O’Shea, R., Gabriel, J., Chiecchio, L., Bowman, P., Sutton, L.A., Walsh, C., Cloke, V., Ucanok, D., Davies, J., Pleasance, B., Maguire, E., Whaite, A., Best, S., Westbury, S., Logan, A., Navarajasegaran, D., Bench, A., Wightman, P., Cartwright, A., Higgs, E., J.Bott, Whitehouse, H., Stevens, J., Martin, D., Dunlop, J., Thomas, S., Sau, C., Farndon, S., Coleman, N., Angelini, P., Massey, H., Rowlands, C., Garcia-Petit, C., Gillespie, K., Alder, A., Middleton, E., Cassidy, C., Orfali, N., Webb, A., Luharia, A., Walker, N., Charlton, J., Andreou, A., Peddie, J., Khan, M., Wilkinson, L., Bezuidenhout, H., Edis, M., Callard, A., Ostrowski, P., Moverley, P., Bean, K., Dunne, A., Moleirinho, A., Waller, S., Cox, K., Greensmith, L., Brittle, A., Gossan, N., Freestone, L., Shak, C., Langford, T., Clinch, Y., Livesey, H., Borland, S., Joshi, A., Wall, K., Whitworth, A., Wilsdon, A., Edgerley, K., Pugh, S., Chrysochoidi, N., Mutch, S., McMullan, C., Johnston, Y., Muraru, M., May, A., Begum, R., Smith, C., Patel, R., Bhatnagar, I., Brown, D., Willan, J., Taylor, S., Jones, K., Ramsden, C., Taiwo, O., Jaudzemaite, J., Sharmin, R., Young, L., O’Dubhshlaine, C., McSorley, L., Rodriguez, I. Abreu, Lillis, S., Alexopoulos, P., Mortensson, E., Kingham, L., Moore, R., Kosicka-Slawinska, M., Aslam, S., Wells, R., Carter, A., Warren, H., Rolf, E., Reed, H., Pearce, L., Lock, D., Ali, F., Kolozi, A., White, N., Wood, D., Hayden, C., Garrett, Alice, Allen, Sophie, Durkie, Miranda, Burghel, George J., Robinson, Rachel, Callaway, Alison, Field, Joanne, Frugtniet, Bethan, Palmer-Smith, Sheila, Grant, Jonathan, Pagan, Judith, McDevitt, Trudi, Rowlands, Charlie F., McVeigh, Terri, Hanson, Helen, and Turnbull, Clare

Published
2025
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9. Dry granular avalanche impact force on a rigid wall of semi-infinite height

Author

Albaba Adel, Lambert Stéphane, and Faug Thierry

Subjects
Physics, QC1-999
Abstract

The present paper tackles the problem of the impact of a dry granular avalanche-flow on a rigid wall of semi-infinite height. An analytic force model based on depth-averaged shock theory is proposed to describe the flow-wall interaction and the resulting impact force on the wall. Provided that the analytic force model is fed with the incoming flow conditions regarding thickness, velocity and density, all averaged over a certain distance downstream of the undisturbed incoming flow, it reproduces very well the time history of the impact force actually measured by detailed discrete element simulations, for a wide range of slope angles.

Published
2017
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11. Age-related upregulation of dense core vesicles in the central inferior colliculus

Author

Jeffrey G. Mellott, Syllissa Duncan, Justine Busby, Laila S. Almassri, Alexa Wawrzyniak, Milena C. Iafrate, Andrew P. Ohl, Elizabeth A. Slabinski, Abigail M. Beaver, Diana Albaba, Brenda Vega, Amir M. Mafi, Morgan Buerke, Nick J. Tokar, and Jesse W. Young

Subjects
inferior colliculus, GABA, synapse, aging, dense core vesicles, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Presbycusis is one of the most prevalent disabilities in aged populations of industrialized countries. As we age less excitation reaches the central auditory system from the periphery. To compensate, the central auditory system [e.g., the inferior colliculus (IC)], downregulates GABAergic inhibition to maintain homeostatic balance. However, the continued downregulation of GABA in the IC causes a disruption in temporal precision related to presbycusis. Many studies of age-related changes to neurotransmission in the IC have therefore focused on GABAergic systems. However, we have discovered that dense core vesicles (DCVs) are significantly upregulated with age in the IC. DCVs can carry neuropeptides, co-transmitters, neurotrophic factors, and proteins destined for the presynaptic zone to participate in synaptogenesis. We used immuno transmission electron microscopy across four age groups (3-month; 19-month; 24-month; and 28-month) of Fisher Brown Norway rats to examine the ultrastructure of DCVs in the IC. Tissue was stained post-embedding for GABA immunoreactivity. DCVs were characterized by diameter and by the neurochemical profile (GABAergic/non-GABAergic) of their location (bouton, axon, soma, and dendrite). Our data was collected across the dorsolateral to ventromedial axis of the central IC. After quantification, we had three primary findings. First, the age-related increase of DCVs occurred most robustly in non-GABAergic dendrites in the middle and low frequency regions of the central IC during middle age. Second, the likelihood of a bouton having more than one DCV increased with age. Lastly, although there was an age-related loss of terminals throughout the IC, the proportion of terminals that contained at least one DCV did not decline. We interpret this finding to mean that terminals carrying proteins packaged in DCVs are spared with age. Several recent studies have demonstrated a role for neuropeptides in the IC in defining cell types and regulating inhibitory and excitatory neurotransmission. Given the age-related increase of DCVs in the IC, it will be critical that future studies determine whether (1) specific neuropeptides are altered with age in the IC and (2) if these neuropeptides contribute to the loss of inhibition and/or increase of excitability that occurs during presbycusis and tinnitus.

Published
2024
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12. Biallelic CRELD1 variants cause a multisystem syndrome, including neurodevelopmental phenotypes, cardiac dysrhythmias, and frequent infections

Author

Borras, Silvia, Clark, Caroline, Dean, John, Miedzybrodzka, Zosia, Ross, Alison, Tennant, Stephen, Dabir, Tabib, Donnelly, Deirdre, Humphreys, Mervyn, Magee, Alex, McConnell, Vivienne, McKee, Shane, McNerlan, Susan, Morrison, Patrick J., Rea, Gillian, Stewart, Fiona, Cole, Trevor, Cooper, Nicola, Cooper-Charles, Lisa, Cox, Helen, Islam, Lily, Jarvis, Joanna, Keelagher, Rebecca, Lim, Derek, McMullan, Dominic, Morton, Jenny, Naik, Swati, O’Driscoll, Mary, Ong, Kai-Ren, Osio, Deborah, Ragge, Nicola, Turton, Sarah, Vogt, Julie, Williams, Denise, Bodek, Simon, Donaldson, Alan, Hills, Alison, Low, Karen, Newbury-Ecob, Ruth, Norman, Andrew M., Roberts, Eileen, Scurr, Ingrid, Smithson, Sarah, Tooley, Madeleine, Abbs, Steve, Armstrong, Ruth, Dunn, Carolyn, Holden, Simon, Park, Soo-Mi, Paterson, Joan, Raymond, Lucy, Reid, Evan, Sandford, Richard, Simonic, Ingrid, Tischkowitz, Marc, Woods, Geoff, Bradley, Lisa, Comerford, Joanne, Green, Andrew, Lynch, Sally, McQuaid, Shirley, Mullaney, Brendan, Berg, Jonathan, Goudie, David, Mavrak, Eleni, McLean, Joanne, McWilliam, Catherine, Reavey, Eleanor, Azam, Tara, Cleary, Elaine, Jackson, Andrew, Lam, Wayne, Lampe, Anne, Moore, David, Porteous, Mary, Baple, Emma, Baptista, Júlia, Brewer, Carole, Castle, Bruce, Kivuva, Emma, Owens, Martina, Rankin, Julia, Shaw-Smith, Charles, Turner, Claire, Turnpenny, Peter, Tysoe, Carolyn, Bradley, Therese, Davidson, Rosemarie, Gardiner, Carol, Joss, Shelagh, Kinning, Esther, Longman, Cheryl, McGowan, Ruth, Murday, Victoria, Pilz, Daniela, Tobias, Edward, Whiteford, Margo, Williams, Nicola, Barnicoat, Angela, Clement, Emma, Faravelli, Francesca, Hurst, Jane, Jenkins, Lucy, Jones, Wendy, Ajith Kumar, V.K., Lees, Melissa, Loughlin, Sam, Male, Alison, Morrogh, Deborah, Rosser, Elisabeth, Scott, Richard, Wilson, Louise, Beleza, Ana, Deshpande, Charu, Flinter, Frances, Holder, Muriel, Irving, Melita, Izatt, Louise, Josifova, Dragana, Mohammed, Shehla, Molenda, Aneta, Robert, Leema, Roworth, Wendy, Ruddy, Deborah, Ryten, Mina, Yau, Shu, Bennett, Christopher, Blyth, Moira, Campbell, Jennifer, Coates, Andrea, Dobbie, Angus, Hewitt, Sarah, Hobson, Emma, Jackson, Eilidh, Jewell, Rosalyn, Kraus, Alison, Prescott, Katrina, Sheridan, Eamonn, Thomson, Jenny, Bradshaw, Kirsty, Dixit, Abhijit, Eason, Jacqueline, Haines, Rebecca, Harrison, Rachel, Mutch, Stacey, Sarkar, Ajoy, Searle, Claire, Shannon, Nora, Sharif, Abid, Suri, Mohnish, Vasudevan, Pradeep, Canham, Natalie, Ellis, Ian, Greenhalgh, Lynn, Howard, Emma, Stinton, Victoria, Swale, Andrew, Weber, Astrid, Banka, Siddharth, Breen, Catherine, Briggs, Tracy, Burkitt-Wright, Emma, Chandler, Kate, Clayton-Smith, Jill, Donnai, Dian, Douzgou, Sofia, Gaunt, Lorraine, Jones, Elizabeth, Kerr, Bronwyn, Langley, Claire, Metcalfe, Kay, Smith, Audrey, Wright, Ronnie, Bourn, David, Burn, John, Fisher, Richard, Hellens, Steve, Henderson, Alex, Montgomery, Tara, Splitt, Miranda, Straub, Volker, Wright, Michael, Zwolinski, Simon, Allen, Zoe, Bernhard, Birgitta, Brady, Angela, Brooks, Claire, Busby, Louise, Clowes, Virginia, Ghali, Neeti, Holder, Susan, Ibitoye, Rita, Wakeling, Emma, Blair, Edward, Carmichael, Jenny, Cilliers, Deirdre, Clasper, Susan, Gibbons, Richard, Kini, Usha, Lester, Tracy, Nemeth, Andrea, Poulton, Joanna, Price, Sue, Shears, Debbie, Stewart, Helen, Wilkie, Andrew, Albaba, Shadi, Baker, Duncan, Balasubramanian, Meena, Johnson, Diana, Parker, Michael, Quarrell, Oliver, Stewart, Alison, Willoughby, Josh, Crosby, Charlene, Elmslie, Frances, Homfray, Tessa, Jin, Huilin, Lahiri, Nayana, Mansour, Sahar, Marks, Karen, McEntagart, Meriel, Saggar, Anand, Tatton-Brown, Kate, Butler, Rachel, Clarke, Angus, Corrin, Sian, Fry, Andrew, Kamath, Arveen, McCann, Emma, Mugalaasi, Hood, Pottinger, Caroline, Procter, Annie, Sampson, Julian, Sansbury, Francis, Varghese, Vinod, Baralle, Diana, Callaway, Alison, Cassidy, Emma J., Daniels, Stacey, Douglas, Andrew, Foulds, Nicola, Hunt, David, Kharbanda, Mira, Lachlan, Katherine, Mercer, Catherine, Side, Lucy, Temple, I. Karen, Wellesley, Diana, Ambrose, J.C., Arumugam, P., Baple, E.L., Bleda, M., Boardman-Pretty, F., Boissiere, J.M., Boustred, C.R., Caulfield, M.J., Chan, G.C., Craig, C.E.H., Daugherty, L.C., de Burca, A., Devereau, A., Elgar, G., Foulger, R.E., Fowler, T., FurióTarí, P., Hackett, J.M., Halai, D., Hamblin, A., Henderson, S., Holman, J.E., Hubbard, T.J.P., Ibáñez, K., Jackson, R., Jones, L.J., Kasperaviciute, D., Kayikci, M., Lahnstein, L., Lawson, K., Leigh, S.E.A., Leong, I.U.S., Lopez, F.J., MaleadyCrowe, F., Mason, J., McDonagh, E.M., Moutsianas, L., Mueller, M., Murugaesu, N., Need, A.C., Odhams, C.A., Patch, C., Perez-Gil, D., Polychronopoulos, D., Pullinger, J., Rahim, T., Rendon, A., Riesgo-Ferreiro, P., Rogers, T., Ryten, M., Savage, K., Sawant, K., Scott, R.H., Siddiq, A., Sieghart, A., Smedley, D., Smith, K.R., Sosinsky, A., Spooner, W., Stevens, H.E., Stuckey, A., Sultana, R., Thomas, E.R.A., Thompson, S.R., Tucci, A., Walsh, E., Watters, S.A., Welland, M.J., Williams, E., Witkowska, K., Acosta, Maria T., Adam, Margaret, Adams, David R., Agrawal, Pankaj B., Alejandro, Mercedes E., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Azamian, Mahshid S., Bacino, Carlos A., Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Peter Chang, Ta Chen, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cooper, Cynthia M., Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davids, Mariska, Dayal, Jyoti G., Deardorff, Matthew, Dell'Angelica, Esteban C., Dhar, Shweta U., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douine, Emilie D., Draper, David D., Duncan, Laura, Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Esteves, Cecilia, Estwick, Tyra, Falk, Marni, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L., Findley, Laurie C., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Fresard, Laure, Gahl, William A., Glass, Ian, Godfrey, Rena A., Golden-Grant, Katie, Goldman, Alica M., Goldstein, David B., Grajewski, Alana, Groden, Catherine A., Gropman, Andrea L., Gutierrez, Irma, Hahn, Sihoun, Hamid, Rizwan, Hanchard, Neil A., Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yong, Isasi, Rosario, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Johnston, Jean M., Karaviti, Lefkothea, Kelley, Emily G., Kennedy, Jennifer, Kiley, Dana, Kohane, Isaac S., Kohler, Jennefer N., Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Korrick, Susan, Koziura, Mary, Krier, Joel B., Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., Lau, C. Christopher, LeBlanc, Kimberly, Lee, Brendan H., Lee, Hane, Levitt, Roy, Lewis, Richard A., Lincoln, Sharyn A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Majcherska, Marta M., Mak, Bryan, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Markello, Thomas C., Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McCormack, Colleen E., McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo M., Morimoto, Marie, Mulvihill, John J., Murdock, David R., Nakano-Okuno, Mariko, Nath, Avi, Nelson, Stan F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Papp, Jeanette C., Parker, Neil H., Phillips, John A., Posey, Jennifer E., Potocki, Lorraine, Pusey, Barbara N., Quinlan, Aaron, Raskind, Wendy, Raja, Archana N., Rao, Deepak A., Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Samson, Susan L., Saporta, Mario, Scott, C. Ron, Schaechter, Judy, Schedl, Timothy, Scott, Daryl A., Sharma, Prashant, Shin, Jimann, Signer, Rebecca, Sillari, Catherine H., Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solem, Emily, Solnica-Krezel, Lilianna, Stoler, Joan M., Stong, Nicholas, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tamburro, Cecelia P., Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Tucker, Brianna M., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Wallace, Stephanie, Walley, Nicole M., Walsh, Chris A., Walker, Melissa, Wambach, Jennifer, Wan, Jijun, Wang, Lee-Kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Wener, Mark, Wenger, Tara, Perry, Katherine Wesseling, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Woods, Jeremy D., Yamamoto, Shinya, Yang, John, Yu, Guoyun, Zastrow, Diane B., Zhao, Chunli, Zuchner, Stephan, Jeffries, Lauren, Mis, Emily K., McWalter, Kirsty, Donkervoort, Sandra, Brodsky, Nina N., Carpier, Jean-Marie, Ji, Weizhen, Ionita, Cristian, Roy, Bhaskar, Morrow, Jon S., Darbinyan, Armine, Iyer, Krishna, Aul, Ritu B., Chao, Katherine R., Cobbold, Laura, Cohen, Stacey, Custodio, Helena M., Drummond-Borg, Margaret, Finanger, Erika, Hainline, Bryan E., Helbig, Ingo, Hewson, Stacy, Hu, Ying, Jackson, Adam, Konstantino, Monica, Leach, Meganne E., McCormick, David, Nelson, Stanley, Nguyen, Joanne, Nugent, Kimberly, Ortega, Lucy, Goodkin, Howard P., Roeder, Elizabeth, Roy, Sani, Sapp, Katie, Saade, Dimah, Sisodiya, Sanjay M., Stals, Karen, Towner, Shelley, Wilson, William, Khokha, Mustafa K., Bönnemann, Carsten G., Lucas, Carrie L., and Lakhani, Saquib A.

Published
2024
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14. SyNet: An Ensemble Network for Object Detection in UAV Images

Author

Albaba, Berat Mert and Ozer, Sedat

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on UAVs (or on drones) is relatively a new area, it remains as a more challenging problem to detect objects in aerial images. There are several reasons for that including: (i) the lack of large drone datasets including large object variance, (ii) the large orientation and scale variance in drone images when compared to the ground images, and (iii) the difference in texture and shape features between the ground and the aerial images. Deep learning based object detection algorithms can be classified under two main categories: (a) single-stage detectors and (b) multi-stage detectors. Both single-stage and multi-stage solutions have their advantages and disadvantages over each other. However, a technique to combine the good sides of each of those solutions could yield even a stronger solution than each of those solutions individually. In this paper, we propose an ensemble network, SyNet, that combines a multi-stage method with a single-stage one with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling strategy. We report the state of the art results obtained by our proposed solution on two different datasets: namely MS-COCO and visDrone with \%52.1 $mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2 $mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$., Comment: accepted for publication at ICPR 2020

Published
2020

15. Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory

Author

Albaba, Berat Mert and Yildiz, Yildiray

Subjects
Computer Science - Multiagent Systems, Computer Science - Machine Learning
Abstract

In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data., Comment: 22 pages, 19 figures

Published
2020

20. Testosterone treatment and change of categories of the International prostate symptom score (IPSS) in hypogonadal patients: 12 years prospective controlled registry study

Author

Aksam Yassin, Daniel Kelly, Joanne Nettleship, Raidh Talib, Raed M. Al-Zoubi, Omar M. Aboumarzouk, and Bassam Albaba

Subjects
Hypogonadism, LUTS, IPSS, testosterone treatment, prostate cancer, Medicine (General), R5-920, Physiology, QP1-981
Abstract

AbstractFunctional hypogonadism is a condition characterized by low testosterone concentrations, occurring more commonly in men as they age. The International Prostate Symptom Score (IPSS) is used to categorize the severity of lower urinary tract symptoms (LUTS) and related symptoms in hypogonadal men. Testosterone therapy (TTh) has previously shown potential in improving total IPSS in men with hypogonadism. However, concerns regarding the effects of urinary function following TTh often prevent treatment in hypogonadal men. To explore this further, two population-based single-center, prospective, cumulative registry studies were combined to contribute to a total population of 1176 men with symptoms of hypogonadism. The total population was separated into a TTh group receiving testosterone undecanoate (TU) for up to 12 years and a control group that did not receive treatment. IPSS was recorded at both baseline and at final recorded visit for each patient. Long-term TTh with TU in hypogonadal men resulted in significant improvements in IPSS categories, even in patients with severe symptoms at baseline. In the control group, untreated hypogonadal men experienced a worsening of IPSS categories. These data indicate that TTh improves LUTS in men with hypogonadism and suggest that previous concerns regarding urinary function may have been overstated.

Published
2023
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21. Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory

Author

Albaba, Mert and Yildiz, Yildiray

Subjects
Computer Science - Multiagent Systems, Computer Science - Machine Learning
Abstract

Predicting the outcomes of cyber-physical systems with multiple human interactions is a challenging problem. This article reviews a game theoretical approach to address this issue, where reinforcement learning is employed to predict the time-extended interaction dynamics. We explain that the most attractive feature of the method is proposing a computationally feasible approach to simultaneously model multiple humans as decision makers, instead of determining the decision dynamics of the intelligent agent of interest and forcing the others to obey certain kinematic and dynamic constraints imposed by the environment. We present two recent exploitations of the method to model 1) unmanned aircraft integration into the National Airspace System and 2) highway traffic. We conclude the article by providing ongoing and future work about employing, improving and validating the method. We also provide related open problems and research opportunities.

Published
2019

26. Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Author

F. B. van Zadelhoff, A. Albaba, D. Cohen, C. Phillips, B. Schaefli, L. Dorren, and M. Schwarz

Subjects
Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity / transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.

Published
2022
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28. Teacher Learning during Transition from Pre-Service to Novice EFL Teacher: A Longitudinal Case Study

Author

Bulut Albaba, Melike

Abstract

In language teacher education literature, attention is predominantly centered on the content of teacher learning and cognition. The process of teacher learning and cognitive change over time remains relatively underexplored. For instance, despite its importance in career trajectory, there is limited empirical evidence on the transition process from English language teacher education programmes to the early years of teaching. Existing studies suggest that novice teachers experience a lack of preparedness to teach after completion of the teacher education programmes and encounter many challenges in implementing what they were taught at teacher education programmes. This article presents the preliminary findings on five case studies exploring teacher learning through participation in school communities in the context of teaching English as a foreign language. The student teachers were interviewed before and during the practicum which was a part of their pre-service teacher education programme, and they were followed up with further interviews in the initial years of teaching. The findings showed that in a context where the school practices conflicted with the teacher's educational ideals, student teacher participation in the school community fostered adaptation towards conventional language teaching practices at schools. The study has implications for research and practice in language teacher education.

Published
2017

30. Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants

Author

Samant, S., Lucassen, A., Znaczko, A., Shaw, A., Ansari, A., Kumar, A., Donaldson, A., Murray, A., Ross, A., Taylor-Beadling, A., Taylor, A., Innes, A., Brady, A., Kulkarni, A., Hogg, A.-C., Bowden, A. Ramsay, Hadonou, A., Coad, B., McIldowie, B., Speight, B., DeSouza, B., Mullaney, B., McKenna, C., Brewer, C., Olimpio, C., Clabby, C., Crosby, C., Jenkins, C., Armstrong, C., Bowles, C., Brooks, C., Byrne, C., Maurer, C., Baralle, D., Chubb, D., Stobo, D., Moore, D., O'Sullivan, D., Donnelly, D., Randhawa, D., Halliday, D., Atkinson, E., Baple, E., Rauter, E., Johnston, E., Woodward, E., Maher, E., Sofianopoulou, E., Petrides, E., Lalloo, F., McRonald, F., Pelz, F., Frayling, I., Evans, G., Corbett, G., Rea, G., Clouston, H., Powell, H., Williamson, H., Carley, H., Thomas, H.J.W., Tomlinson, I., Cook, J., Hoyle, J., Tellez, J., Whitworth, J., Williams, J., Murray, J., Campbell, J., Tolmie, J., Field, J., Mason, J., Burn, J., Bruty, J., Callaway, J., Grant, J., Del Rey Jimenez, J., Pagan, J., VanCampen, J., Barwell, J., Monahan, K., Tatton-Brown, K., Ong, K.-R., Murphy, K., Andrews, K., Mokretar, K., Cadoo, K., Smith, K., Baker, K., Brown, K., Reay, K., McKay Bounford, K., Bradshaw, K., Russell, K., Stone, K., Snape, K., Crookes, L., Reed, L., Taggart, L., Yarram, L., Cobbold, L., Walker, L., Hawkes, L., Busby, L., Izatt, L., Kiely, L., Hughes, L., Side, L., Sarkies, L., Greenhalgh, K.-L., Shanmugasundaram, M., Duff, M., Bartlett, M., Watson, M., Owens, M., Bradford, M., Huxley, M., Slean, M., Ryten, M., Smith, M., Ahmed, M., Roberts, N., O'Brien, C., Middleton, O., Tarpey, P., Logan, P., Dean, P., May, P., Brace, P., Tredwell, R., Harrison, R., Hart, R., Kirk, R., Martin, R., Nyanhete, R., Wright, R., Davidson, R., Cleaver, R., Talukdar, S., Butler, S., Sampson, J., Ribeiro, S., Dell, S., Mackenzie, S., Hegarty, S., Albaba, S., McKee, S., Palmer-Smith, S., Heggarty, S., MacParland, S., Greville-Heygate, S., Daniels, S., Prapa, S., Abbs, S., Tennant, S., Hardy, S., MacMahon, S., McVeigh, T., Foo, T., Bedenham, T., Cranston, T., McDevitt, T., Clowes, V., Tripathi, V., McConnell, V., Woodwaer, N., Wallis, Y., Kemp, Z., Mullan, G., Pierson, L., Rainey, L., Joyce, C., Timbs, A., Reuther, A.-M., Frugtniet, B., Husher, C., Lawn, C., Corbett, C., Nocera-Jijon, D., Reay, D., Cross, E., Ryan, F., Lindsay, H., Oliver, J., Dring, J., Spiers, J., Harper, J., Ciucias, K., Connolly, L., Tsang, M., Brown, R., Shepherd, S., Begum, S., Tadiso, T., Linton-Willoughby, T., Heppell, H., Sahan, K., Worrillow, L., Allen, Z., Barlett, M., Watt, C., Hegarty, M., Loong, Lucy, Cubuk, Cankut, Choi, Subin, Allen, Sophie, Torr, Beth, Garrett, Alice, Loveday, Chey, Durkie, Miranda, Callaway, Alison, Burghel, George J., Drummond, James, Robinson, Rachel, Berry, Ian R., Wallace, Andrew, Eccles, Diana M., Tischkowitz, Marc, Ellard, Sian, Ware, James S., Hanson, Helen, and Turnbull, Clare

Published
2022
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49. Comprehensive study of 28 individuals with SIN3A-related disorder underscoring the associated mild cognitive and distinctive facial phenotype

Author

Balasubramanian, Meena, Dingemans, Alexander J. M., Albaba, Shadi, Richardson, Ruth, Yates, Thabo M., Cox, Helen, Douzgou, Sofia, Armstrong, Ruth, Sansbury, Francis H., Burke, Katherine B., Fry, Andrew E., Ragge, Nicola, Sharif, Saba, Foster, Alison, De Sandre-Giovannoli, Annachiara, Elouej, Sahar, Vasudevan, Pradeep, Mansour, Sahar, Wilson, Kate, Stewart, Helen, Heide, Solveig, Nava, Caroline, Keren, Boris, Demirdas, Serwet, Brooks, Alice S., Vincent, Marie, Isidor, Bertrand, Küry, Sebastien, Schouten, Meyke, Leenders, Erika, Chung, Wendy K., Haeringen, Arie van, Scheffner, Thomas, Debray, Francois-Guillaume, White, Susan M., Palafoll, Maria Irene Valenzuela, Pfundt, Rolph, Newbury-Ecob, Ruth, and Kleefstra, Tjitske

Published
2021
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50. Intelligent routing algorithm for mobile Internet Protocol Television

Author

Abubakar, Babangida Albaba

Subjects
004.6
Abstract

Network bandwidth and server capacity are gradually becoming overloaded due to the high demand and rapid evolution of high quality multimedia services over the Internet. Internet Protocol Television (IPTV) is among the multimedia services that demand more of network and server resources, especially with the emergence of Mobile IPTV. It is imperative for the service providers to maintain good quality management services in order to satisfy their clients. To guarantee the required quality of service (QoS) and quality of experience (QoE) in IPTV, the server must have the required capacity and resources to serve all the clients’ requests. The flexibility of IPTV services which provide users with the ability to stream multimedia content at anytime and anywhere they want, makes the demand for video-on-demand (VoD) services higher. However, the server bandwidth capacity is limited, and as such the numerous requests from the clients may exhaust all the available bandwidth depending on the number of requests at a given time, which may lead to the poor QoS and QoE. In this research, a new algorithm called Intelligent Routing Algorithm for Mobile IPTV (IRA-MIPTV) is proposed. The algorithm combined features and advantages of Internet Protocol (IP), Mobile Ad hoc Network (MANET) characteristics and Content Delivery Network (CDN) based network architecture to improve on the QoS and QoE in mobile IPTV. The proposed algorithm is aimed at reducing total dependency on the server by the mobile nodes. The algorithm intelligently learns the best server or client to serve an incoming service request depending on the available server capacity and the number of requests received at a point in time. The routing decision is made by the Designated Server (DS) that selects and reroutes a request to the most appropriate server or client. The novelty of this research work can simply be identified as the designing, developing and evaluating an Intelligent Routing Algorithm for mobile ITPV (IRA-MIPTV) that intelligently learns and select a reflective server or client to serve a particular service request on behalf of the server during high service demand. The selection depends on the server available bandwidth, load and proximity. The proposed algorithm also dynamically adjusts to server failure by assigning the role of designated server to the backup server and re-elect another backup server to guarantee service delivery at all times. To validate the effectiveness of the proposed algorithm, different simulation tests were conducted using OPNET/Riverbed Modeler 18.0. A typical IPTV network, where packets are delivered over IP, and the proposed algorithm were modelled and incorporated into the Modeler. For the study to reflect more on real situations, live video programme was streamed using VLC media player. The packet’s size and packet inter-arrival time data were collected and used in the simulation’s environment. After conducting a series of simulation tests, the results showed that the proposed algorithm outperforms the normal IPTV system in server load reduction, high throughput and low amount of end-to-end delay, as well as adaptability and robustness. The results also showed that the efficiency of the proposed algorithm increases as the number of clients increase. It also confirmed that the algorithm reduces the server overload during high service request periods by using clients to serve some of the incoming service requests on behalf of the server. The algorithm produced low server and network load, low end-to-end delay, high throughput, adaptability and robustness.

Published
2016

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