Physical Sciences and Mathematics Commons^™

Benewind: An Adaptive Benefit Win–Win Platform With Distributed Virtual Emotion Foundation, Hyunbum Kim, Jalel Ben-Othman

All Works

In recent decades, online platforms that use Web 3.0 have tremendously expanded their goods, services, and values to numerous applications thanks to its inherent advantages of convenience, service speed, connectivity, etc. Although online commerce and other relevant platforms have clear merits, offline-based commerce and payments are indispensable and should be activated continuously, because offline systems have intrinsic value for people. With the theme of benefiting all humankind, we propose a new adaptive benefit platform, called BeneWinD, which is endowed with strengths of online and offline platforms. Furthermore, a new currency for integrated benefits, the win–win digital currency, is used in …

Ai-Enabled Adaptive Learning Using Automated Topic Alignment And Doubt Detection, Kar Way Tan, Siaw Ling Lo, Eng Lieh Ouh, Wei Leng Neo

Research Collection School Of Computing and Information Systems

Implementing adaptive learning is often a challenging task at higher learning institutions where the students come from diverse backgrounds and disciplines. In this work, we collected informal learning journals from learners. Using the journals, we trained two machine learning models, an automated topic alignment and a doubt detection model to identify areas of adjustment required for teaching and students who require additional attention. The models form the baseline for a quiz recommender tool to dynamically generate personalized quizzes for each learner as practices to reinforce learning. Our pilot deployment of our AI-enabled Adaptive Learning System showed that our approach delivers …

Triangular Mesh Boolean Operation Method For Finite Element Analysis, Yufei Guo, Kang Zhao, Yongqing Hai

Journal of System Simulation

Abstract: To shorten the cycle of finite element analysis (FEA), an adaptive triangular mesh Boolean operation method for finite element analysis is proposed. The ADT (alternating digital tree) data structure is applied to the intersection calculation of triangular meshes, which improves the efficiency of the intersection calculation of Boolean operations. A sphere packing algorithm and a node addition/deletion algorithm are used to remesh some remeshing regions, which ensures the efficiency of the method and the high-quality of remeshed meshes. An improved octree background grid is used to record and smooth the size field, which can generate size-adaptive meshes. The size …

Reflecting On, And Revising, International Best Practice Principles For Eia Follow-Up, Angus Morrison-Saunders, Jos Arts, Alan Bond, Jenny Pope, Francois Retief

Research outputs 2014 to 2021

Follow-up is a vital component of Environmental Impact Assessment (EIA), being essential for understanding assessment outcomes. Long-standing international best practice principles for EIA follow-up are reviewed, and revisions proposed, based on workshops with academics and practitioners, literature review and self-reflection. The proposed revision of EIA follow-up principles will feature an introduction with a simple definition and explanation of objectives for follow-up, and 15 principles. The revised principles address: objective; context; early establishment; project life-cycle; transparency; accessibility; accountability; performance criteria provision; enforcement; learning; adaptive environmental management; flexible or adaptive approach; tiering; cumulative effects and overall performance evaluation. Through publishing this proposal, …

Multi-Dimensional Numerical Integration On Parallel Architectures, Ioannis Sakiotis, Marc Paterno, Balsa Terzic, Mohammad Zubair, Desh Ranjan

College of Sciences Posters

Multi-dimensional numerical integration is a challenging computational problem that is encountered in many scientific computing applications. Despite extensive research and the development of efficient techniques such as adaptive and Monte Carlo methods, many complex high-dimensional integrands can be too computationally intense even for state-of-the-art numerical libraries such as CUBA, QUADPACK, NAG, and MSL. However, adaptive integration has few dependencies and is very well suited for parallel architectures where processors can operate on different partitions of the integration-space. While existing parallel methods exist, most are simple extensions of their sequential versions. This results in moderate speedup and in many cases failure …

An Evaluation Of Knot Placement Strategies For Spline Regression, William Klein

CMC Senior Theses

Regression splines have an established value for producing quality fit at a relatively low-degree polynomial. This paper explores the implications of adopting new methods for knot selection in tandem with established methodology from the current literature. Structural features of generated datasets, as well as residuals collected from sequential iterative models are used to augment the equidistant knot selection process. From analyzing a simulated dataset and an application onto the Racial Animus dataset, I find that a B-spline basis paired with equally-spaced knots remains the best choice when data are evenly distributed, even when structural features of a dataset are known …

Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Project Study Of Adaptive Error Concealment Based On Multi-Texture Direction For H.264 Video Stream, Xiaohong Zhang, Ting Hu

Journal of System Simulation

Abstract: The smoothness of restored video is poor to the complex-texture block for the H.264 standard error concealment algorithm. Aiming at the H.264 questions, the project of adaptive error concealment based on multi-texture direction was proposed. Two thresholding methods were used to set the thresholding of Sobel edge detection algorithm, and the proposed Sobel algorithm was judged to detection the edges of adjacent. Cost function of the boundary pixel difference was combined with to determine the interpolation direction for each pixel of error block, and then, the error block was recovered by the interpolation direction of each pixel. JM8.6 simulation …

Adaptive Quick Artificial Bee Colony Algorithm Based On Opposition Learning, Xiaojian Yang, Yiwei Dong

Journal of System Simulation

Abstract: On the basis of analyzing such shortcomings of the artificial bee colony algorithm (ABC) as slow convergence, low convergence precision and premature convergence, the opposition-learning adaptive quick artificial bee colony algorithm (OAQABC) was proposed. A new step size was proposed, which made the around food source parameter of quick artificial bee colony algorithm (QABC) adaptive, and combined the opposition-based learning to improve the employed bee phase. The experimental results show that OAQABC has better performance than basic ABC and QABC. Also the optimization performance of OAQABC is better than particle swarm optimization (PSO) algorithm and Cuckoo Search (CS) algorithm …

Facial Expression Recognition In The Wild Using Convolutional Neural Networks, Amir Hossein Farzaneh

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Facial Expression Recognition (FER) is the task of predicting a specific facial expression given a facial image. FER has demonstrated remarkable progress due to the advancement of deep learning. Generally, a FER system as a prediction model is built using two sub-modules: 1. Facial image representation model that learns a mapping from the input 2D facial image to a compact feature representation in the embedding space, and 2. A classifier module that maps the learned features to the label space comprising seven labels of neutral, happy, sad, surprise, anger, fear, or disgust. Ultimately, …

Study Of Modified Particle Swarm Optimization Algorithm Based On Adaptive Mutation Probability, Huang Song, Tian Na, Zhicheng Ji

Journal of System Simulation

Abstract: Mutation operator is an effective method to solve the premature of particle swarm optimization. According to the characteristic of population diversity, a modified particle swarm optimization based on adaptive mutation probability and hybrid mutation strategy was proposed. Aggregation degree was introduced to adjust the mutation probability of each generation, and a hybrid Gaussian and Cauchy mutation based on the global-best position and an adaptive wavelet mutation based on the worst personal-best position were carried out. The simulation of the comparisons with other particle swarm optimizations with mutation operator on matlab was proposed. The results demonstrate that the proposed algorithm …

Cuckoo Search Algorithm With Dynamic Step And Discovery Probability, Jingsen Liu, Xiaozhen Liu, Li Yu

Journal of System Simulation

Abstract: In order to further improve the low accuracy and slow convergence speed of algorithm search, a cuckoo search algorithm with dynamic step size and probability of discovery is proposed. The algorithm dynamically constrains the Levy's moving step of each generation by introducing the step adjustment factor, which makes the Levy's flight mechanism adaptive. In the probability of finding, the random inertia weight with uniform distribution and F distribution is used to change the fixed value of the probability of discovery, to strengthen the diversity of the population and to keep the balance between global search and local exploration. The …

An Enhanced Multi-Modal Function Optimization Fireworks Algorithm Base On Loser-Out Tournament, Xiaoning Shen, Wang Qian, Huang Yao, You Xuan

Journal of System Simulation

Abstract: An enhanced multi-modal fireworks algorithm based on the loser-out tournament is proposed. A new position-based mapping rule is used to map the explosion sparks beyond the upper boundary of the explosion space to the area near the upper boundary, and to map the one below the lower boundary to the area near the lower boundary. A strategy which adaptively adjusts the number of explosion sparks is introduced to better balance the global and local search abilities of the algorithm. The 28 functions in the CEC2013 standard test function set are selected to the test. Experimental results show that the …

Adpative Mode Matching Upgrade For Advanced Ligo, Fabian Magana-Sandoval

Dissertations - ALL

Advanced LIGO is commissioning new noise reducing technologies that are sensitive to optical losses generated by optical cavity mode mismatch. Optical losses due to mode mismatched Fabry-P´erot optical cavities can be reduced by the use of adaptive optics, wavefront sensors, and feedback control loops. Advanced LIGO currently uses adaptive hardware for alignment, but not for mode matching. Though LIGO instrumentalist can measure static mode mismatch, they can not yet adaptively correct it.

This thesis presents a possible upgrade that will allow for sensing optical cavity mode mismatch and alignment. The upgrade will require minimal hardware installation and will work in …

Structure Tensor Adaptive Total Variation For Image Restoration, Surya Prasath, Dang Nh Thanh

Turkish Journal of Electrical Engineering and Computer Sciences

Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.

Adaptive Meshfree Methods For Partial Differential Equations, Jaeyoun Oh

Dissertations

There are many types of adaptive methods that have been developed with different algorithm schemes and definitions for solving Partial Differential Equations (PDE). Adaptive methods have been developed in mesh-based methods, and in recent years, they have been extended by using meshfree methods, such as the Radial Basis Function (RBF) collocation method and the Method of Fundamental Solutions (MFS). The purpose of this dissertation is to introduce an adaptive algorithm with a residual type of error estimator which has not been found in the literature for the adaptive MFS. Some modifications have been made in developing the algorithm schemes depending …

Further Advances For The Sequential Multiple Assignment Randomized Trial (Smart), Tianjiao Dai

Dissertations & Theses (Open Access)

ABSTRACT

FURTHER ADVANCES FOR THE SEQUENTIAL MULTIPLE ASSIGNMENT RANDOMIZED TRIAL (SMART)

Tianjiao Dai, M.S.

Advisory Professor: Sanjay Shete, Ph.D.

Sequential multiple assignment randomized trial (SMART) designs have been developed these years for studying adaptive interventions. In my Ph.D. study, I mainly investigate how to further improve SMART designs and optimize the interventions for each individual in the trial. My dissertation has focused on two topics of SMART designs.

1) Developing a novel SMART design that can reduce the cost and side effects associated with the interventions and proposing the corresponding analytic methods. I have developed a time-varying SMART design in …

An Adaptive Agent-Based Approach To Traffic Simulation, Johan Barthelemy, Timoteo Carletti

SMART Infrastructure Facility - Papers

The aim of this work is to present the initial exploration of a behavioural Dynamic Traffic Assignment model, particularly suitable to be used and implemented in agent-based micro-simulations. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time spent. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents' …

Lifelong Machine Learning With Adaptive Multi-Agent Systems, Nicolas R. Verstaevel, Jeremy Boes, Julien Nigon, Dorian D'Amico, Marie-Pierre Gleizes

SMART Infrastructure Facility - Papers

Sensors and actuators are progressively invading our everyday life as well as industrial processes. They form complex and pervasive systems usually called "ambient systems" or "cyber-physical systems". These systems are supposed to efficiently perform various and dynamic tasks in an ever-changing environment. They need to be able to learn and to self-adapt throughout their life, because designers cannot specify a priori all the interactions and situations they will face. These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform …

Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key …

A Balance Between Inhibitor Binding And Substrate Processing Confers Influenza Drug Resistance, Li Jiang, Ping Liu, Claudia Bank, Nicholas Renzette, Kristina Prachanronarong, L. Yilmaz, Daniel Caffrey, Konstantin Zeldovich, Celia Schiffer, Timothy Kowalik, Jeffrey Jensen, Robert Finberg, Jennifer Wang, Daniel Bolon

Celia A. Schiffer

The therapeutic benefits of the neuraminidase (NA) inhibitor oseltamivir are dampened by the emergence of drug resistance mutations in influenza A virus (IAV). To investigate the mechanistic features that underlie resistance, we developed an approach to quantify the effects of all possible single-nucleotide substitutions introduced into important regions of NA. We determined the experimental fitness effects of 450 nucleotide mutations encoding positions both surrounding the active site and at more distant sites in an N1 strain of IAV in the presence and absence of oseltamivir. NA mutations previously known to confer oseltamivir resistance in N1 strains, including H275Y and N295S, …

Learn Piano With Bach: An Adaptive Learning Interface That Adjusts Task Difficulty Based On Brain State, Beste F. Yuksel, Kurt B. Oleson, Lane Harrison, Evan M. Peck, Daniel Afergan, Remco Chang, Robert Jk Jacob

Faculty Conference Papers and Presentations

We present Brain Automated Chorales (BACh), an adaptive brain-computer system that dynamically increases the levels of difficulty in a musical learning task based on pianists' cognitive workload measured by functional near-infrared spectroscopy. As users' cognitive workload fell below a certain threshold, suggesting that they had mastered the material and could handle more cognitive information, BACh automatically increased the difficulty of the learning task. We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition. Participant feedback indicated that they felt they learned better with BACh and they liked the timings …

Map: A Computational Model For Adaptive Persuasion, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which provides a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals’ personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse agent …

Designing For Better Building Adaptability: A Comparison Of Adaptstar And Arp Models, Sheila Conejos, Craig Langston, Jim Smith

Craig Langston

Can sustainability and adaptability be integrated in a single decision tool for designing future buildings? Indeed, it is not possible to know what lies ahead for future buildings but, using current research on sustainability and the impact on natural resources and climate, it is possible to forecast the connection between built environment activity and sustainability. This paper demonstrates that the assessment of future adaptation in newly designed building is achievable by using the adaptSTAR model. This new design-rating tool, based on detailed analysis of 12 award-winning adaptive reuse projects in Australia, will assist designers in making decisions to achieve optimum …

Adapt Or Die: Polynomial Lower Bounds For Non-Adaptive Dynamic Data Structures, Joshua Brody, K. G. Larsen

Computer Science Faculty Works

In this paper, we study the role non-adaptivity plays in maintaining dynamic data structures. Roughly speaking, a data structure is non-adaptive if the memory locations it reads and/or writes when processing a query or update depend only on the query or update and not on the contents of previously read cells. We study such non-adaptive data structures in the cell probe model. The cell probe model is one of the least restrictive lower bound models and in particular, cell probe lower bounds apply to data structures developed in the popular word-RAM model. Unfortunately, this generality comes at a high cost: …

Adaptive Step-Sizes For Reinforcement Learning, William C. Dabney

Doctoral Dissertations

The central theme motivating this dissertation is the desire to develop reinforcement learning algorithms that “just work” regardless of the domain in which they are applied. The largest impediment to this goal is the sensitivity of reinforcement learning algorithms to the step-size parameter used to rescale incremental updates. Adaptive step-size algorithms attempt to reduce this sensitivity or eliminate the step-size parameter entirely by automatically adjusting the step size throughout the learning process. Such algorithms provide an alternative to the standard “guess-and-check” methods used to find parameters known as parameter tuning. However, the problems with parameter tuning are currently masked by …

Adaptive Stochastic Systems: Estimation, Filtering, And Noise Attenuation, Araz Ryan Hashemi

Wayne State University Dissertations

This dissertation investigates problems arising in identification and control of stochastic systems. When the parameters determining the underlying systems are unknown and/or time varying, estimation and adaptive filter- ing are invoked to to identify parameters or to track time-varying systems. We begin by considering linear systems whose coefficients evolve as a slowly- varying Markov Chain. We propose three families of constant step-size (or gain size) algorithms for estimating and tracking the coefficient parameter: Least-Mean Squares (LMS), Sign-Regressor (SR), and Sign-Error (SE) algorithms.

The analysis is carried out in a multi-scale framework considering the relative size of the gain (rate of …

Soc Estimation For Lifepo4 Battery In Evs Using Recursive Least-Squares With Multiple Adaptive Forgetting Factors, Van Huan Duong, Hany A. Bastawrous, Kai Chin Lim, Khay Wai W. See, Peng Zhang, S X. Dou

Australian Institute for Innovative Materials - Papers

This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.

Hp-Daemon: HIgh PErformance DIstributed ADaptive ENergy-Efficient MAtrix-MultiplicatiOn, Li Tan, Longxiang Chen, Zizhong Chen, Ziliang Zong, Rong Ge, Dong Li

Mathematics, Statistics and Computer Science Faculty Research and Publications

The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than …

Adaptive Wavelet Discretization Of Tensor Products In H-Tucker Format, Mazen Ali

Masters Theses

"In previous work, the solution to a system of coupled parabolic PDEs, modeling the price of a CDO, was approximated numerically. Due to the nature of the problem, the system involved a large number of equations such that the parameters cannot be stored explicitly. The authors combined the data sparse H-Tucker storage format with the Galerkin method to approximate the solution, using wavelets for the space discretization together with time stepping (Method of Lines). The aforementioned approximation is of the linear kind, i.e., using a nonadaptive method. In this work, three methods for solving such systems adaptively are presented, together …