Heterogeneous Univariate Outlier Ensembles In Multidimensional Data,
2020
Singapore Management University
Heterogeneous Univariate Outlier Ensembles In Multidimensional Data, Guansong Pang, Longbing Cao
Research Collection School Of Computing and Information Systems
In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers, in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector …
Deep Multi-Task Learning For Depression Detection And Prediction In Longitudinal Data,
2020
Singapore Management University
Deep Multi-Task Learning For Depression Detection And Prediction In Longitudinal Data, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression …
Nearest Centroid: A Bridge Between Statistics And Machine Learning,
2020
Singapore Management University
Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of …
Interventional Few-Shot Learning,
2020
Nanyang Technological University
Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua
Research Collection School Of Computing and Information Systems
We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view. It is worth noting that the contribution …
Causal Intervention For Weakly-Supervised Semantic Segmentation,
2020
Singapore Management University
Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun
Research Collection School Of Computing and Information Systems
We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels --- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of "horse'' and "person'' may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish between the boundaries. Inspired by this, we propose a structural causal model to analyze the causalities among images, contexts, and …
Artificial Intelligence For Social Impact: Learning And Planning In The Data-To-Deployment Pipeline,
2020
Harvard University
Artificial Intelligence For Social Impact: Learning And Planning In The Data-To-Deployment Pipeline, Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe
Research Collection School Of Computing and Information Systems
With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward addressing complex societal problems. In pursuit of this goal of AI for social impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention …
Goamlp: Network Intrusion Detection With Multilayer Perceptron And Grasshopper Optimization Algorithm,
2020
Case Western Reserve University
Goamlp: Network Intrusion Detection With Multilayer Perceptron And Grasshopper Optimization Algorithm, Farshid Bagheri Saravi
Student Scholarship
In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters …
Computational Cognition And Deep Learning,
2020
Arcadia University
Computational Cognition And Deep Learning, Andy Malinsky
The Compass
No abstract provided.
Energy-Based Neural Modelling For Large-Scale Multiple Domain Dialogue State Tracking,
2020
Technological University Dublin
Energy-Based Neural Modelling For Large-Scale Multiple Domain Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their …
Autonomous Vehicles And The Ethical Tension Between Occupant And Non-Occupant Safety,
2020
Georgia Institute of Technology
Autonomous Vehicles And The Ethical Tension Between Occupant And Non-Occupant Safety, Jason Borenstein, Joseph Herkert, Keith Miller
The Journal of Sociotechnical Critique
Given that the creation and deployment of autonomous vehicles is likely to continue, it is important to explore the ethical responsibilities of designers, manufacturers, operators, and regulators of the technology. We specifically focus on the ethical responsibilities surrounding autonomous vehicles that these stakeholders have to protect the safety of non-occupants, meaning individuals who are around the vehicles while they are operating. The term “non-occupants” includes, but is not limited to, pedestrians and cyclists. We are particularly interested in how to assign moral responsibility for the safety of non-occupants when autonomous vehicles are deployed in a complex, land-based transportation system.
Machine Learning Augmentation Micro-Sensors For Smart Device Applications,
2020
University of Nebraska-Lincoln
Machine Learning Augmentation Micro-Sensors For Smart Device Applications, Mohammad H. Hasan
Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research
Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability …
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction,
2020
University of Missouri-St. Louis
New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger
Theses
Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …
Time-Varying Parameter System Modeling Method Based On Zonotope-Ellipsoid Double Filtering,
2020
Engineering Research Center of Internet of Things Technology Applications (Ministry of Education),Jiangnan University,Wuxi 214122,China;
Time-Varying Parameter System Modeling Method Based On Zonotope-Ellipsoid Double Filtering, Ziyun Wang, Peiyu Wang, Yacong Zhan
Journal of System Simulation
Abstract: The traditional system modeling method using zonotopes as the feasible parameter sets islikely to increase the computational complexity of the algorithm due to the increasing dimensions of the zonotope shape matrix. This paper proposes a time-varying parameter modeling systems method based on zonotope-ellipsoid double filtering technique. Considering the time-varying parameters, a zonotope with the minimum volume is obtained during the iterations of the intersection with the constraint strip. After transforming the shape matrix of the zonotope, the dimensionality reduction is performed, instead of directly finding the row sum of the extended shape matrix, to reduce the algorithm conservativeness originated …
Research On Configurable Simulation Integration Technology For Equipment Software,
2020
1. The Second System Design of the Second Research Academy of CASIC,Beijing 100854,China; ;
Research On Configurable Simulation Integration Technology For Equipment Software, Yuanyuan Wang, Yuxin Duan, Guangzhao Song
Journal of System Simulation
Abstract: The problems of the traditional distributed digital simulation system are analyzed. The method of constructing the digital simulation system based on the equipment software is studied. The configurable simulation integration middleware is designed. The overall structure of the configurable middleware is designed and the design ideas of each module are briefly summarized. An example of a digital simulation system with networked transformation of equipment shows that the middleware can be effectively and flexibly configured and can provide support for constructing digital simulation system with software, which can be promoted and used in other simulation systems.
Generalized Zero-Inflated Binomial Distribution Model Aimed At Air Quality Data Analysis,
2020
1.School of Mathematics and Computer,Tongling University,Tongling 244061,China; ;3.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province,Anqing 246133,China; ;
Generalized Zero-Inflated Binomial Distribution Model Aimed At Air Quality Data Analysis, Benyue Su, Pengpeng Xu, Sheng Min
Journal of System Simulation
Abstract: For the problem of the quality monitoring and counting of excessive gas emissions in chemical industry parks, a generalized zero-inflated binomial distribution model is constructed. Statistics show that the times of number of excessive gas emissions has a typical zero-inflated feature. The traditional zero-inflated Poisson model and negative binomial regression model and so on will underestimate the probability of zero inflation. A generalized zero-inflated binomial distribution model is constructed by extending the traditional binomial regression model to a more general form. This model satisfies the characteristic that the expectation is less than the variance, and better solves the problems …
Hyper-Heuristic De Algorithm For Solving Zero-Wait Fermentation Process Schedulinge,
2020
Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;
Hyper-Heuristic De Algorithm For Solving Zero-Wait Fermentation Process Schedulinge, Shen Peng, Wang Yan, Zhicheng Ji, Jianhua Zhang
Journal of System Simulation
Abstract: A class of zero-wait fermentation process scheduling issues with batch process characteristics are researched. In order to solve the problem of easy deterioration in the process, a super heuristic difference algorithm is proposed, and the maximum makespan is minimized as the optimization goal. The algorithm is divided into two layers. The upper layer is an improved adaptive differential evolution algorithm to select and sort the heuristic operations in lower layer. The lower layer is combined and sorted into a new algorithm to operate on the problem domain, adding simulated annealing algorithm to avoid falling into local optimization. The method …
Modeling And Relevance Analysis Of Urban Epidemic Transmission And Work Resumption Intensity,
2020
1.School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China; ;2.National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 610031,China; ;3.National United laboratory of Comprehensive Transportation,Southwest Jiaotong University,Chengdu 610031,China;
Modeling And Relevance Analysis Of Urban Epidemic Transmission And Work Resumption Intensity, Gan Mi, Yunyi Tian, Wenchang Zhang, Xihan Zhao
Journal of System Simulation
Abstract: On the basis of multi-source big data, the model for analyzing the population migration changes and manpower gaps caused by the COVID-19 epidemic in 34 typical cities across the country is constructed , and the work resumption intensity of other cities is predicited by using the migration base constructed. The SEIR model is used to estimate the basic reproduction number in each city since the simulate results show that it can emulate the transmission trend of this epidemic accurately, and the retrospective matrix analysis of the work resumption intensity is combined with the manpower gap to summarize the anti-epidemic …
Research On Dynamic Flexible Job Shop Scheduling Problem Based On Dynamic Interaction Layer,
2020
Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;
Research On Dynamic Flexible Job Shop Scheduling Problem Based On Dynamic Interaction Layer, Zhang Xiang, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: In order to quickly response to the unforeseen circumstances in flexible job shop, a dynamic flexible job shop scheduling model is constructed, which takes the overall production time and the completion time of emergency orders as the optimization objectives. For the model, a dynamic interaction layer (DIL) model, which has a better performance on DFJSP, is proposed to replace the scroll window. Particle swarm genetic hybrid algorithm (PSGA) is designed to combine the particle swarm optimization algorithm with the genetic algorithm to enhance the ability of local search. Aiming at the unexpected urgent orders in flexible job shop, …
Echo Simulation And Verification Of High Resolution Range Profile,
2020
1. Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China; ;2. Shanghai University,Shanghai 200072,China;
Echo Simulation And Verification Of High Resolution Range Profile, Xiaolin Li, Cheng Yu, Haifei Zang, Shuge Wang, Liu Li, Qingqing Yuan
Journal of System Simulation
Abstract: In order to realize the echo simulation and verification of high-resolution range profile in laboratory environment, the multi-scattering point model and wideband LFM echo signal model are given, and the time-domain convolution and high-precision delay realization methods in echo simulation process are described. According to the different signal bandwidth forms of the tested equipments, a wideband echo simulator is used to realize the corresponding high-resolution range image echo. By comparing with the digital simulation results of the target characteristic modeling software, the fidelity of the high-resolution range echo simulation is verified. Simulation verification is carried out with an aircraft …
Multi-Agent Simulation Model For Covid-19 Virus Prevention And Control,
2020
College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China;
Multi-Agent Simulation Model For Covid-19 Virus Prevention And Control, Lihu Pan, Shipeng Qin, Xiaowen Li, Feiping Lu, Fenyu Yang
Journal of System Simulation
Abstract: The prevention and control of the novel coronavirus (COVID-19) is the priority work to maintain the public health security of the world nowadays. The COVID-19 prevention and control model using multi-agent modeling and simulation technology is proposed. The model can simulate the different dynamic development trend of the epidemic under different prevention and control measures. Taking Taiyuan as an example, according to the researched COVID-19 transmission rules, the prevention and control simulation of COVID-19 has been achieved under the designing rule of the interactive infection process and status transition process between various resident agents. Multi-scenario simulation experiments are realized …
