Five Challenges In Cloud-Enabled Intelligence And Control,
2020
Singapore Management University
Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen
Research Collection School Of Computing and Information Systems
The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling …
Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval,
2020
Singapore Management University
Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of …
Generating Realistic Stock Market Order Streams,
2020
Singapore Management University
Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman
Research Collection School Of Computing and Information Systems
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.
Gdface: Gated Deformation For Multi-View Face Image Synthesis,
2020
Singapore Management University
Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He
Research Collection School Of Computing and Information Systems
Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views …
Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding,
2020
Universiti Malaya
Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed
Student Works (2020-2029)
The use of social media platform in the airline industries have increased rapidly to allow analysis introduce the quality and performance of the services. The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. This problem has led to low accuracy in …
Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques,
2020
Universiti Malaya
Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza
Student Works (2020-2029)
Electricity price forecasting is considered as one of prime factors for operation, planning and scheduling of price-setter market participants. However, possessing time variant, non-linear and non-stationary behaviors make the electricity price a complex signal. The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy …
Bounding Regret In Empirical Games,
2020
Carnegie Mellon University
Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh
Research Collection School Of Computing and Information Systems
Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive …
Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning,
2020
Singapore Management University
Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe
Research Collection School Of Computing and Information Systems
Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at …
Topic Modeling On Document Networks With Adjacent-Encoder,
2020
Singapore Management University
Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …
Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories,
2020
Fudan University
Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang
Research Collection School Of Computing and Information Systems
Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We …
Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching,
2020
Singapore Management University
Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu
Research Collection School Of Computing and Information Systems
Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vectorrepresentation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary
Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence,
2020
Technological University Dublin
Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence, Pierpaolo Dondio, Luca Longo, Stefano Bistarelli
Articles
Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as evident. The study of argumentation has been the focus of attention of philosophers and scholars, from Aristotle and classical rhetoric to the present day. The computational study of arguments has emerged as a field of research in AI in the last two decades, mainly fuelled by the interest from …
Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation,
2020
Technological University Dublin
Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation, Pierpaolo Dondio, Luca Longo
Articles
In Dung’s abstract semantics, the label undecided is always propagated from the attacker to the attacked argument, unless the latter is also attacked by an accepted argument. In this work we propose undecidedness blocking abstract argumentation semantics where the undecided label is confined to the strong connected component where it was generated and it is not propagated to the other parts of the argumentation graph. We show how undecidedness blocking is a fundamental reasoning pattern absent in abstract argumentation but present in similar fashion in the ambiguity blocking semantics of Defeasible logic, in the beyond reasonable doubt legal principle or …
Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity,
2020
University of Kentucky
Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan
Information Science Faculty Publications
Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …
Research On The Strategy Of Adaptive Uvls Based On Rtds Simulation,
2020
1. XJ Electric Co., Ltd., Xuchang 461000, China;;
Research On The Strategy Of Adaptive Uvls Based On Rtds Simulation, Zhao Dan, Man Ji, Yunling Ni, Dengxin Liu, Weidong Wang
Journal of System Simulation
Abstract: Aiming at the improvement of the slow and unstable system voltage recovery after the low voltage load shedding load protected by centralized station area, the traditional low voltage load shedding load strategy will be researched. Considering the factors such as the power shortage and the important grade of the load, the power factor of the load to be cut is introduced, and the low load shedding model is established by minimizing the amount of cut load and minimizing the reactive power. The research is transformed into a belt Constrained multi-objective optimization research. The multi- objective particle swarm optimization algorithm …
Design Of Air Defense Missile Weapon System Simulation Platform Based On Xsim Platform,
2020
Shanghai Institute of Electro-Mechanical Engineering, Shanghai 201109, China;
Design Of Air Defense Missile Weapon System Simulation Platform Based On Xsim Platform, Kaizhi Ruan, Qingqing Yuan, Wenhua Zhai, Zhiqiang Zhang
Journal of System Simulation
Abstract: Taking the system simulation technology which applied to the scheme argumentation, optimization design, flight test forecast, battle effectiveness evaluation of air defense missile weapon system as background, the design method of air defense missile weapon system simulation platform based on Xsim platform was put forward. The total configuration design, model design and simulation process design of the platform were discussed. An simulation platform of an air defense missile weapon system was accomplished. The result proves the platform can simulate the battle process of air defense missile weapon system, support the simulation work of air defense weapon at different …
Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse,
2020
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China;
Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun
Journal of System Simulation
Abstract: Because the traditional methods can hardly analyze the complex combustion characteristics of cement kiln mixed with domestic refuse, a data mining technology is introduced. A domestic cement plant is selected as the object, and its operating data and relevant parameters are collected. The influence coefficient of each parameter on coal consumption and NOx emission is analyzed by using Stability Selection algorithm. The mathematical model of coal consumption and NOx emission is established with Random Forest algorithm, and the key optimization parameters and their optimal values are obtained by K-means clustering algorithm. The result shows that this method …
An Enhanced Multi-Modal Function Optimization Fireworks Algorithm Base On Loser-Out Tournament,
2020
1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China;;2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China;;3. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing 210044, China;
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 …
A Xor-Based Visual Cryptography Scheme For (2, N) Access Structure With Ideal Structure Division,
2020
Information Engineering University, Zhengzhou 450000, China;
A Xor-Based Visual Cryptography Scheme For (2, N) Access Structure With Ideal Structure Division, Yuqiao Cheng, Zhengxin Fu, Bin Yu
Journal of System Simulation
Abstract: We propose a XOR-based visual cryptography scheme for (2, n) access structures. According to the definition of ideal access structure, the relationship of shares among the minimal qualified subsets is analyzed. And based on it, a division algorithm of access structures is presented with the theory of graph. By this approach, we can obtain the least number of ideal access structures. Additionally the processes of secret sharing and recovering are given. Experimental results show that this scheme can achieve a perfect secret recovery. Compared with existing schemes, the pixel expansion of our paper is the best.
Study On Hardware-In-Loop Simulation Of Space-Feed Low-Frequency Guidance With Turntable External,
2020
1. Beihang University, Beijing 100191, China;;
Study On Hardware-In-Loop Simulation Of Space-Feed Low-Frequency Guidance With Turntable External, Linpeng Wang, Chaolei Wang, Yuting Dai
Journal of System Simulation
Abstract: Low-frequency detection, tracking and guidance of stealthy targets need new requirements for hardware-in-loop simulation verification technology. Turntable built-in usually produces electromagnetic interference to seeker. A turntable external method for space- feed low-frequency guidance of hardware in- loop simulation system is proposed. The space-feed low-frequency guidance simulation model is established, and the influence of turntable electromagnetic interference on the seeker is completely eliminated by the turntable external. The simulation environment of non-inertial space motion is constructed to solve the information fusion problem of multiple spaces for hardware-in-loop simulation. The feasibility of the simulation method is verified. The results show that …
