Artificial Intelligence and Robotics Commons^™

Signature Identification And Verification Systems: A Comparative Study On The Online And Offline Techniques, Nehal Hamdy Al-Banhawy, Heba Mohsen, Neveen I. Ghali Prof.

Future Computing and Informatics Journal

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for preprocessing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and …

Data: The Good, The Bad And The Ethical, John D. Kelleher, Filipe Cabral Pinto, Luis M. Cortesao

Articles

It is often the case with new technologies that it is very hard to predict their long-term impacts and as a result, although new technology may be beneficial in the short term, it can still cause problems in the longer term. This is what happened with oil by-products in different areas: the use of plastic as a disposable material did not take into account the hundreds of years necessary for its decomposition and its related long-term environmental damage. Data is said to be the new oil. The message to be conveyed is associated with its intrinsic value. But as in …

End-To-End Learning Utilizing Temporal Information For Vision- Based Autonomous Driving, Dapeng Guo

Master's Projects

End-to-End learning models trained with conditional imitation learning (CIL) have demonstrated their capabilities in driving autonomously in dynamic environments. The performance of such models however is limited as most of them fail to utilize the temporal information, which resides in a sequence of observations. In this work, we explore the use of temporal information with a recurrent network to improve driving performance. We propose a model that combines a pre-trained, deeper convolutional neural network to better capture image features with a long short-term memory network to better explore temporal information. Experimental results indicate that the proposed model achieves performance gain …

Detecting Deepfakes With Deep Learning, Eric C. Tjon

Master's Projects

Advances in generative models and manipulation techniques have given rise to digitally altered videos known as deepfakes. These videos are difficult to identify for both humans and machines. Typical detection methods exploit various imperfections in deepfake videos, such as inconsistent posing and visual artifacts. In this paper, we propose a pipeline with two distinct pathways for examining individual frames and video clips. The image pathway contains a novel architecture called Eff-YNet capable of both segmenting and detecting frames from deepfake videos. It consists of a U-Net with a classification branch and an EfficientNet B4 encoder. The video pathway implements a …

Multi-Agent Deep Reinforcement Learning For Walkers, Inhee Park

Master's Projects

This project was motivated by seeking an AI method towards Artificial General Intelligence (AGI), that is, more similar to learning behavior of human-beings. As of today, Deep Reinforcement Learning (DRL) is the most closer to the AGI compared to other machine learning methods. To better understand the DRL, we compares and contrasts to other related methods: Deep Learning, Dynamic Programming and Game Theory.

We apply one of state-of-art DRL algorithms, called Proximal Policy Op- timization (PPO) to the robot walkers locomotion, as a simple yet challenging environment, inherently continuous and high-dimensional state/action space.

The end goal of this project is …

Lidar Object Detection Utilizing Existing Cnns For Smart Cities, Vinay Ponnaganti

Master's Projects

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with …

Malware Classification With Gaussian Mixture Model-Hidden Markov Models, Jing Zhao

Master's Projects

Discrete hidden Markov models (HMM) are often applied to the malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this study, we apply GMM-HMMs to the malware classification problem and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve on the classification results that we can attain with …

The Use Of Evidential Reasoning Model With Biomarkers In Pancreatic Cancer Prediction, Qianhui Fan

Master's Projects

In this project, an evidential reasoning model is built to amalgamate factors that could be used in early detection of pancreatic cancer. Our machine learning model outputs a probability of a given patient having prostate cancer based on various input variables. These variables include health history factors, such as smoking and medical history, technical artifacts, such as biopsy sequencing technology, and genomic biomarkers such as mutational, transcriptional and methylomic profiles, cfDNA, and copy number variation. The dataset used in this project is a part of The Cancer Genome Atlas (TCGA) project and was collected from the National Cancer Institute (NIH) …

Machine Learning Model Selection For Predicting Global Bathymetry, Nicholas P. Moran

LSU New Orleans Theses and Dissertations

This work is concerned with the viability of Machine Learning (ML) in training models for predicting global bathymetry, and whether there is a best fit model for predicting that bathymetry. The desired result is an investigation of the ability for ML to be used in future prediction models and to experiment with multiple trained models to determine an optimum selection. Ocean features were aggregated from a set of external studies and placed into two minute spatial grids representing the earth's oceans. A set of regression models, classification models, and a novel classification model were then fit to this data and …

Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom

Master's Projects

Next generation sequencing (NGS) has revolutionized the biological sciences. Today, entire genomes can be rapidly sequenced, enabling advancements in personalized medicine, genetic diseases, and more. The National Center for Biotechnology Information (NCBI) hosts the Sequence Read Archive (SRA) containing vast amounts of valuable NGS data. Recently, research has shown that sequencing errors in conventional NGS workflows are key confounding factors for detecting mutations. Various steps such as sample handling and library preparation can introduce artifacts that affect the accuracy of calling rare mutations. Thus, there is a need for more insight into the exact relationship between various steps of the …

Malware Classification Using Lstms, Dennis Dang

Master's Projects

Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models. In this project, we create four different long-short term memory (LSTM) models and train each model to classify malware by family type. Our data consists of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP) such as word embedding and bidirection LSTMs (biLSTM). We also use convolutional neural networks (CNN). We found that our model consisting of …

Quantifying Deepfake Detection Accuracy For A Variety Of Natural Settings, Pratikkumar Prajapati

Master's Projects

Deep fakes are videos generated from a starting video of a person where that person's face has been swapped for someone else's. In this report, we describe our work to develop general, deep learning-based models to classify Deep Fake content. Our first experiments involved simple Convolution Neural Network (CNN)-based models where we varied how individual frames from the source video were passed to the CNN. These simple models tended to give low accuracy scores for discriminating fake versus non-fake videos of less than 60%. We then developed three more sophisticated models: one based on choosing test frames, one based on …

Analyzing Dispatching Wave Policies For E-Commerce Logistics Based On The Multi-Agent-Based Simulation, Zhiqiang Niu, Chaoyang Li, Hongyu Dong, Zhang Feng, Lingyun Meng, Tong Lu, Shengnan Wu

Journal of System Simulation

Abstract: To provide superior on-line shopping experiences and maintain sustained profitability,e-commerce logistics companies need design an effective dispatching wave policy strategy to handle the tradeoff between the advantage of economies of scale and fast-pace delivery services.A multi-agent-based simulation framework is proposed,where business processes in logistics are built as different nodes in the simulation network.Case studies are conducted to test the order delivery process with various wave strategies in the Beijing metropolitan area.Experimental results show that situation-dependent wave strategies are sensitive to different patterns of online shopping demands.

Optimization Algorithm For Planar Led Distribution And Connection, Fei Yue, Zhiqiang Gui, Yuyou Yao, Benzhu Xu, Liping Zheng

Journal of System Simulation

Abstract: The distribution and grouping of planar LED can be modeled as a multi constraint optimization problem.A novel algorithm based on the centroidal capacity-constrained power diagram for LED distribution is proposed,to achieve the goal of uniform illumination of planar LED.An optimization algorithm of LED combination and connection based on the greedy strategy is proposed to save materials.The experiment results show that the proposed algorithms are simple,effective in layout and grouping with rapid convergence,and can be used in practical applications.

An Online Evaluation Framework Of Complex Simulation System Based On Acceptability Criteria, Zhenglin Sun, Weiqiang Yuan, Weiqing Li

Journal of System Simulation

Abstract: An online simulation evaluation method based on Acceptability Criteria (AC) for the lag of current complex simulation systems is proposed.A qualitative and quantitative AC to index mapping model is used to establish an evaluation index system.Based on index sets and evaluation functions,a seven-tuple model of a simulation process finite automaton is proposed,and a mapping of the simulation process to automata and a data-driven state transfer mechanism are given.Based on the above results,an online evaluation tool is designed and the effectiveness is verified through a case.The result that the method can effectively solve lag in the evaluation …

Amorphous Sio2/Si Interface Defects And Mechanism Of Passivation/Depassivation Reaction, Zhuocheng Hong, Zuo Xu

Journal of System Simulation

Abstract: The amorphous silicon dioxide/silicon (a-SiO₂/Si) interface is an important part of semiconductor devices.The passivation and depassivation process of silicon dangling bond defects (P_b-type defects) at the SiO₂/Si interface has a significant impact on semiconductor devices.Based on molecular dynamics and first-principles calculation methods,a-SiO₂/Si(111) interface model is constructed based on a-SiO₂ and crystalline Si.The CI-NEB (Climbing Image-Nudged Elastic Band) method is used to study the passivation and depassivation reactions of H₂ and H atoms of P_b defects at the a-SiO₂/Si(111) interface. The curves,barriers,and transition state structures of …

Non-Cooperative Target Feature Point Cloud Registration Optimization Based On Icp Algorithm, Wei Liang, Muyao Xue, Huo Ju, Jinjie Zhang

Journal of System Simulation

Abstract: Aiming at the pose measurement caused by non-cooperative targets in visual measurement that cannot provide cooperation information,the ICP(Iterative Closest Point) algorithm is used to register the point cloud down-sampling data acquired at different times to complete the relative pose measurement of the target.The point cloud data of the target at the current moment is obtained using the structure from motion algorithm and the feature point matching algorithms are compared based on threshold matching and optical flow matching method.The extracted feature points are reconstructed by triangulation.The relative pose changes of the object at different times are calculated by using …

Human-Computer Interaction Speech Emotion Recognition Based On Random Forest And Convolution Feature Learning, Wang Jing, Hongyan Liu, Fangfang Liu, Qingqing Wang

Journal of System Simulation

Abstract: Focus on the different speech features of different types of people in the automatic speech emotion recognition of emotional robots,a random forest for speech emotion recognition is proposed,and a preliminary simulation experiment of emotional social robot system based on convolution feature learning is carried out.The results show that the emotional robot can track in real time,the seven basic emotions of excitement,anger,sadness,happiness,surprise,fear and neutrality.By using non personalized speech emotion features,the original personalized speech emotion features are supplemented,and the general emotion and special emotion are extracted.For emotional robot,using these indicators has a certain application prospect in the simulation experiment …

Research On Fuzzy Control And Optimization For Traffic Lights At Single Intersection, Jiajia Liu, Xingquan Zuo

Journal of System Simulation

Abstract: Aiming at the traffic signal control at urban single intersection,a fuzzy control method for traffic lights is presented.The method is based on a four-phase phasing sequence to control the traffic lights at a single intersection.Inputs of the fuzzy controller are the number of vehicles in line and the arrival rate of vehicles,and the output is the green light extension time of the current green light phase.A genetic algorithm (GA) is used to optimize fuzzy rules and membership functions of the fuzzy control system to improve the performance of the fuzzy controller.The fuzzy control method is realized by using …

Reliability Evaluation Method For Reuse Model Of Complex Simulation System, Ziheng Ye, Fuzhen Zhang, Yaoqin Zhu, Weiqing Li

Journal of System Simulation

Abstract: Model reuse can enhance the flexibility and scalability of simulation applications,and is necessary in the construction of complex simulation systems.Assessing credibility in multi-model combination simulation is the basic question of whether the model can be effectively reused.Aiming at the two simulation model reuse,in different application environments of physical model-oriented and numerical settlement-oriented,the credibility evaluation method for simulation reuse models based on bias propagation is proposed,and the respective modeling methods and evaluation methods are introduced in detail. Taking UAV as an example,the comparison result with the classic credibility evaluation method shows that the method can reduce the difficulty of …