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Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Towards Improving Calibration In Object Detection Under Domain Shift, Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali
Towards Improving Calibration In Object Detection Under Domain Shift, Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali
Computer Vision Faculty Publications
With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in safety-critical environments, be highly accurate and well-calibrated. Although some techniques addressing DNN calibration have been proposed, they are only limited to visual classification applications and in-domain predictions. Unfortunately, very little to no attention is paid towards addressing calibration of DNN-based visual object detectors, that occupy similar space and importance in many decision making systems as their visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift. …
The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska
The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska
Karbala International Journal of Modern Science
Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.
Essays In Multidimensional Mechanism Design., Kolagani Paramahamsa Dr.
Essays In Multidimensional Mechanism Design., Kolagani Paramahamsa Dr.
Doctoral Theses
This thesis analyzes three problems where a monopolistic seller is selling to an agent with multidimensional private information. While our understanding of such problems is comprehensive if the agent's private information is one-dimensional, problems with multidimensional private information are known to be ubiquitous but analytically notorious. The three chapters in this thesis make progress in understanding optimal mechanism design in such multidimensional screening problems. In the first problem, the seller is selling an object to an agent who exhibits behavioral preferences, in a departure from the standard rational models. Behavioral preferences arise because the agent is budget constrained and needs …
Death-Related Anxiety Associated With Riskier Decision-Making Irrespective Of Framing: A Bayesian Model Comparison, Blaine Tomkins
Death-Related Anxiety Associated With Riskier Decision-Making Irrespective Of Framing: A Bayesian Model Comparison, Blaine Tomkins
Psychology Faculty Publications
A commonly reported finding is that anxious individuals are less likely to make risky decisions. However, no studies have examined whether this association extends to death-related anxiety. The present study examined how groups low, moderate, and high in death-related anxiety make decisions with varying levels of risk. Participants completed a series of hypothetical bets in which the probability of a win was systematically manipulated. High-anxiety individuals displayed the greatest risk-taking behavior, followed by the moderate-anxiety group, with the low-anxiety group being most risk-averse. Experiment 2 tested this association further by framing outcomes in terms of losses, rather than gains. A …
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Research Collection School Of Computing and Information Systems
Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …
Factors Influencing Adoption Of Rangeland Rehabilitation Technologies By Agro-Pastoralists In The Arabian Peninsula: Evidence From Analysis In Saudi Arabia And Qatar, Boubaker Dhehibi, Arash Nejatian, Abdul Aziz Niane, Azaiez Ouled Belgacem
Factors Influencing Adoption Of Rangeland Rehabilitation Technologies By Agro-Pastoralists In The Arabian Peninsula: Evidence From Analysis In Saudi Arabia And Qatar, Boubaker Dhehibi, Arash Nejatian, Abdul Aziz Niane, Azaiez Ouled Belgacem
IGC Proceedings (1997-2023)
Rangelands are the main land use in the Arabian Peninsula and cover about 50% of total area. They are under continuous heavy grazing pressure due to underlying social and economic causes as well as institutional effects. ICARDA in collaboration with the National Agricultural Research Systems (NARS) has developed and introduced different rehabilitation techniques including resting, planting native range species and water harvesting in different countries of the AP such as Kuwait, Qatar, Saudi Arabia (KSA) and Yemen. However, the adoption of these techniques by end users was not evaluated. In this context, a research has been conducted in Saudi Arabia …
Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub
Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub
Computer Vision Faculty Publications
For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) [13] have been extensively used in embedding image and text data into …
The Data Analytics And The Science Revolution, Leila Halawi, Amal Clarke, Kelly George
The Data Analytics And The Science Revolution, Leila Halawi, Amal Clarke, Kelly George
Publications
This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they …
Many-Objective Evolutionary Algorithms: Objective Reduction, Decomposition And Multi-Modality., Monalisa Pal Dr.
Many-Objective Evolutionary Algorithms: Objective Reduction, Decomposition And Multi-Modality., Monalisa Pal Dr.
Doctoral Theses
Evolutionary Algorithms (EAs) for Many-Objective Optimization (MaOO) problems are challenging in nature due to the requirement of large population size, difficulty in maintaining the selection pressure towards global optima and inability of accurate visualization of high-dimensional Pareto-optimal Set (in decision space) and Pareto-Front (in objective space). The quality of the estimated set of Pareto-optimal solutions, resulting from the EAs for MaOO problems, is assessed in terms of proximity to the true surface (convergence) and uniformity and coverage of the estimated set over the true surface (diversity). With more number of objectives, the challenges become more profound. Thus, better strategies have …
Functional Implications Of Dale's Law In Balanced Neuronal Network Dynamics And Decision Making, Victor J. Barranca, Asha Bhuiyan , '23, Max Sundgren , '22, Fangzhou Xing , '22
Functional Implications Of Dale's Law In Balanced Neuronal Network Dynamics And Decision Making, Victor J. Barranca, Asha Bhuiyan , '23, Max Sundgren , '22, Fangzhou Xing , '22
Mathematics & Statistics Faculty Works
The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual …
Business Intelligence Trends: A Review Of Mobile Business Intelligence, Shanika Edirisinghe
Business Intelligence Trends: A Review Of Mobile Business Intelligence, Shanika Edirisinghe
Articles
The early stages of Decision Support Systems evolved with the technological improvements and availability of massive amounts of data. The concept of Business Intelligence became apparent along with this evolution which incorporates a range of fields and supports decision making in business organizations. Mobile business intelligence is a popular trend in the domain of business intelligence at present. Business organizations employ mobile business intelligence as an extension to the existing business intelligence systems. This study intends to present a review of mobile business intelligence while addressing its benefits, challenges, and limitations. Moreover, this study provides details of several use cases …
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Engineering Management & Systems Engineering Faculty Publications
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …