Search Results - Computing methodologies Machine learning Machine learning approaches Markov decision processes

A Machine Learning Approach for offload Decision Making in Mobile Cloud Computing by Mathur, Robin Prakash, Sharma, Manmohan

“… A technique has been proposed by performing a stack ensemble approach on machine learning techniques like the Gaussian approach, multi-layer perceptron, k-nearest neighbors, and linear regression…”
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Decision Support Platform for Production of Chili using IoT, Cloud Computing, and Machine Learning Approach by Elijah, Olakunle, Rahim, Sharul K. A., Abioye, Emmanuel A., Musa, Mu'Azu Jibrin, Salihu, Yahaya Otuoze, Oremeyi, Abubakar Abisetu

“… It is a major source of income for both small- and large-scale farmers. Unfortunately, chili farmers have to contend with the challenge of pests and diseases and the need for timely decisions to have a bountiful production…”
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Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software by Chen, Zhenpeng, Li, Xinyue, Zhang, Jie M., Sarro, Federica, Liu, Yang

“…Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes…”
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PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning by Brauckmann, Alexander, Goens, Andres, Castrillon, Jeronimo

“… In this paper, we propose a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP…”
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Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata by Bojanczyk, Mikolaj, Klin, Bartek, Moerman, Joshua

“…We develop a theory of vector spaces spanned by orbit-finite sets. Using this theory, we give a decision procedure for equivalence of weighted register automata…”
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Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach by Deshwal, Aryan, Belakaria, Syrine, Bhat, Ganapati, Doppa, Janardhan Rao, Pande, Partha Pratim

“…Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of emerging…”
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Improving Robot Controller Transparency Through Autonomous Policy Explanation by Hayes, Bradley, Shah, Julie A.

“…Shared expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially…”
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Continuous One-Counter Automata by Blondin, Michael, Leys, Tim, Mazowiecki, Filip, Offtermatt, Philip, Perez, Guillermo A.

“…We study the reachability problem for continuous one-counter automata, COCA for short. In such automata, transitions are guarded by upper and lower bound tests…”
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Exploiting learning and scenario-based specification languages for the verification and validation of highly automated driving by Damm, Werner, Galbas, Roland

“…We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue…”
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DiffSAT: Differential MaxSAT Layer for SAT Solving by Zhang, Yu, Zhen, Hui-Ling, Yuan, Mingxuan, Yu, Bei

“… To address this issue, numerous learning-driven methods have been proposed. However, these methods primarily focus on utilizing data-driven approaches to enhance…”
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Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation by Daniele, Andrea F., Bansal, Mohit, Walter, Matthew R.

“…Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural…”
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LICS: Logic in Computer Security -- Some Attacker's Models and Related Decision Problems by Comon-Lundh, Hubert

“…Logic plays an important role in formal aspects of computer security, for instance in access control, security of communications or even intrusion detection…”
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Distributed regression: an efficient framework for modeling sensor network data by Guestrin, Carlos, Bodik, Peter, Thibaux, Romain, Paskin, Mark, Madden, Samuel

“…We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor…”
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Specification Test Compaction for Analog Circuits and MEMS by Biswas, Sounil, Li, Peng, Blanton, R. D. (Shawn), Pileggi, Larry T.

“… required.We propose to eliminate redundant tests by employing ε-SVM based statistical learning.Application of the proposed methodology to an operational amplifier…”
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Pueblo: A Modern Pseudo-Boolean SAT Solver by Sheini, Hossein M., Sakallah, Karem A.

“… Its novel features include an efficient PB literal watching strategy and several PB learning methods that take advantage of the pruning power of PB constraints…”
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DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification by Wang, Junyao, Huang, Sitao, Imani, Mohsen

“… Many applications exploit machine learning methodology to dissect collected data, and edge computing was introduced to enhance the efficiency and scalability in resource-constrained computing environments…”
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Can Wear-Aware Memory Allocation be Intelligent? by Hakert, Christian, Chen, Kuan-Hsun, Chen, Jian-Jia

“… Therefore, we investigate the employment of a reinforcement learning agent as a substitute for such a strategy in this paper…”
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DARL: Distributed Reconfigurable Accelerator for Hyperdimensional Reinforcement Learning by Chen, Hanning, Issa, Mariam, Ni, Yang, Imani, Mohsen

“…Reinforcement Learning (RL) is a powerful technology to solve decision-making problems such as robotics control…”
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Considering Circuit Observability Don't Cares in CNF Satisfiability by Fu, Zhaohui, Yu, Yinlei, Malik, Sharad

“… However, this process loses some information regarding the structure of the circuit. One example of such structural information is the Circuit…”
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A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning by Fu, Yonggan, Zhang, Yongan, Li, Chaojian, Yu, Zhongzhi, Lin, Yingyan

“…Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications…”
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