Artificial Intelligence and Robotics Commons^™

Joint Shift Scheduling Method For Call Center With Mechanism Of Delay Information, Miao Yu, Manru Li, Yu Zhao

Journal of System Simulation

Abstract: A joint shift scheduling method is studied for call center with delay information. According to the queue model of call center with delay information, the influence rule of the customer's patience and abandonment behavior is addressed, and a mechanism of delay information is proposed to estimate the waiting time of customers. Considering the influence of non-stationary arrival and other factors, the scheduling model of the call centers is established by the discrete Event-Scheduling approach. Based on the proposed evaluation method of delay information, the joint shift scheduling method by simulation optimization is designed to solve the scheduling problem …

Multi-Stage Multi-Agv Path Planning With Walk Under Shelves For Robotic Mobile Fulfillment Systems, Teng Li, Peipei Ding, Jinfang Liu

Journal of System Simulation

Abstract: Aiming at the problem of increasing travel time due to turning and obstacle avoidance in robotic mobile fulfillment systems(RMFS) with large-scale multi-AGV path planning, a path planning model with the shortest task completion time is established. A path planning model considering no-load AGV that can pass through the shelf is proposed, and the model is solved by an improving A* algorithm. The AGV operation stage is divided, an turning penalty value is introduced into the A* algorithm to reduce the turning times, and the obstacle avoidance priority with the obstacle avoidance waiting time is set. The simulation results show …

Design And Simulation Of Ts Fuzzy Based Cooperative Control Of Missile Formation, Yexin Zhang, Yu Cheng, Hongyan Yan, Xuwei Fan, Xu Zhang, Yi Tian

Journal of System Simulation

Abstract: Aiming at the requirement of cooperative operation of multi-missile formation, a cooperative control algorithm of multi-missile formation based on Takagi-Sugeno(TS) fuzzy control theory is proposed.The flight speed, trajectory angle and trajectory deflection angle of the missile are taken as parameters in the leader-follower mode missile formation flying system.The local asymptotically stable controller is designed by using the systemlocal linearization of multiple groups of equilibrium pointsduring the whole flight process.Through the expert experience method,the membership function and fuzzy rules for the system are designedwith TS fuzzy theory, and the whole multi-missile cooperative control system is completed and the stability …

Green, Quantized Federated Learning Over Wireless Networks: An Energy-Efficient Design, Minsu Kim, Walid Saad, Mohammad Mozaffari, Mérouane Debbah

Machine Learning Faculty Publications

The practical deployment of federated learning (FL) over wireless networks requires balancing energy efficiency and convergence time due to the limited available resources of devices. Prior art on FL often trains deep neural networks (DNNs) to achieve high accuracy and fast convergence using 32 bits of precision level. However, such scenarios will be impractical for resource-constrained devices since DNNs typically have high computational complexity and memory requirements. Thus, there is a need to reduce the precision level in DNNs to reduce the energy expenditure. In this paper, a green-quantized FL framework, which represents data with a finite precision level in …

Adversarial Pixel Restoration As A Pretext Task For Transferable Perturbations, Hashmat Shadab Malik, Shahina K. Kunhimon, Muzammal Nasser, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Transferable adversarial attacks optimize adversaries from a pretrained surrogate model and known label space to fool the unknown black-box models. Therefore, these attacks are restricted by the availability of an effective surrogate model. In this work, we relax this assumption and propose Adversarial Pixel Restoration as a self-supervised alternative to train an effective surrogate model from scratch under the condition of no labels and few data samples. Our training approach is based on a min-max objective which reduces overfitting via an adversarial objective and thus optimizes for a more generalizable surrogate model. Our proposed attack is complimentary to our adversarial …

Robustar: Interactive Toolbox Supporting Precise Data Annotation For Robust Vision Learning, Chonghan Chen, Haohan Wang, Leyang Hu, Yuhao Zhang, Shuguang Lyu, Jingcheng Wu, Xinnuo Li, Linjing Sun, Eric Xing

Machine Learning Faculty Publications

We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective. Building upon the recent understanding that the lack of machine learning model’s robustness is the tendency of the model’s learning of spurious features, we aim to solve this problem from its root at the data perspective by removing the spurious features from the data before training. In particular, we introduce a software that helps the users to better prepare the data for training image classification models by allowing the users to annotate the spurious features …

Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network, Stephan Mgaya, Hellen Maziku

Tanzania Journal of Engineering and Technology (TJET)

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. …

Big Data Analytics Framework For Effective Higher Education Institutions, George Matto

Tanzania Journal of Engineering and Technology (TJET)

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …

Toward A Standard Formal Semantic Representation Of The Model Card Report, Xiaoqian Jiang, Fang Li, Evan Yu, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of …

Control And Planning For Mobile Manipulators Used In Large Scale Manufacturing Processes, Joshua T. Nguyen

LSU Master's Theses

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for …

Bridging The Gap Between Object And Image-Level Representations For Open-Vocabulary Detection, Hanoona Rasheed, Muhammad Maaz, Muhammad Uzair Khattak, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at inference. Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision. We note that both these modes of supervision are not optimally aligned for the detection task: CLIP is trained with image-text pairs and lacks precise localization of objects while the image-level supervision has been used with heuristics that do not accurately specify local object regions. In this work, we propose to address this problem by performing object-centric alignment of …

Unpaired Style Transfer Conditional Generative Adversarial Network For Scanned Document Generation, David Jonathan Hawbaker

Dissertations and Theses

Neural networks are a powerful machine learning tool, especially when trained on a large dataset of relevant high-quality data. Generative adversarial networks, image super resolution and most other image manipulation neural networks require a dataset of images and matching target images for training. Collecting and compiling that data can be time consuming and expensive. This work explores an approach for building a dataset of paired document images with a matching scanned version of each document without physical printers or scanners. A dataset of these document image pairs could be used to train a generative adversarial network or image super resolution …

Fuzzy Reasoning Procedure For Ontologies Based On Rough Membership Approximation, Armand Florentin Donfack Kana, Babatunde Opeoluwa Akinkunmi

Future Computing and Informatics Journal

One of the major challenges in modeling a real-world domain is how to effectively represent uncertain and incomplete knowledge of that domain. Several techniques for representing uncertainty in ontologies have been proposed with some of the techniques lacking provision for vague inference. The classical tableaux-based algorithm does not provide the flexibility for reasoning over such vague ontologies. However, several extensions of the tableaux-based algorithm have been proposed to cope with fuzzy reasoning. Similarly, several alternative reasoning methods for incomplete, inconsistent, and uncertain ontologies have been proposed. One of the major limitations of most of those techniques is that they require …

Textual Emotion Detection Approaches: A Survey, Mahinda Mahmoud Samy Zidan, Ibrahim Elhenawy, Ahmed R. Abas, Mahmoud Othman

Future Computing and Informatics Journal

Over the past decades, social media attracted individuals to express their feelings on any topic or item, resulting in an incremental growth in the size of created data. These feelings and unstructured data paved the path for business organizations to gather information and build statistical analysis. Various machine learning and natural language processing-based approaches are used for sentiment and emotion analysis. Moreover, deep learning-based approaches recently gained popularity due to their remarkable performance in text analysis. This paper provides a comprehensive overview of the prominent machine learning models applied in emotion analysis. It explores various emotion analysis taxonomies, in addition …

A Comparative Study On Deep Learning Models For Text Classification Of Unstructured Medical Notes With Various Levels Of Class Imbalance, Hongxia Lu, Louis Ehwerhemuepha, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical …

A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela

Faculty Publications

Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …

Sdq: Stochastic Differentiable Quantization With Mixed Precision, Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric Xing, Kwang Ting Cheng

Machine Learning Faculty Publications

In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used. In addition, as new hardware that supports mixed bitwidth arithmetic operations, recent research on mixed precision quantization (MPQ) begins to fully leverage the capacity of representation by searching optimized bitwidths for different layers and modules in a network. However, previous studies mainly search the MPQ strategy in a costly scheme using reinforcement learning, neural architecture search, etc., or simply utilize partial prior knowledge for bitwidth assignment, which might be biased on locality of information and is sub-optimal. In this work, we present …

Action-Sufficient State Representation Learning For Control With Structural Constraints, Biwei Huang, Chaochao Lu, Liu Leqi, Josã© Miguel Hernã¡Ndez-Lobato, Clark Glymour, Bernhard Schã¶Lkopf, Kun Zhang

Machine Learning Faculty Publications

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks. In this paper, we focus on partially observable environments and propose to learn a minimal set of state representations that capture sufficient information for decision-making, termed Action-Sufficient state Representations (ASRs). We build a generative environment model for the structural relationships among variables in the system and present a principled way to characterize ASRs based on structural constraints and the goal of maximizing …

Gradient-Free Method For Heavily Constrained Nonconvex Optimization, Wanli Shi, Hongchang Gao, Bin Gu

Machine Learning Faculty Publications

Zeroth-order (ZO) method has been shown to be a powerful method for solving the optimization problem where explicit expression of the gradients is difficult or infeasible to obtain. Recently, due to the practical value of the constrained problems, a lot of ZO Frank-Wolfe or projected ZO methods have been proposed. However, in many applications, we may have a very large number of nonconvex white/black-box constraints, which makes the existing zeroth-order methods extremely inefficient (or even not working) since they need to inquire function value of all the constraints and project the solution to the complicated feasible set. In this paper, …

Identification Of Linear Non-Gaussian Latent Hierarchical Structure, Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang

Machine Learning Faculty Publications

Traditional causal discovery methods mainly focus on estimating causal relations among measured variables, but in many real-world problems, such as questionnaire-based psychometric studies, measured variables are generated by latent variables that are causally related. Accordingly, this paper investigates the problem of discovering the hidden causal variables and estimating the causal structure, including both the causal relations among latent variables and those between latent and measured variables. We relax the frequently-used measurement assumption and allow the children of latent variables to be latent as well, and hence deal with a specific type of latent hierarchical causal structure. In particular, we define …