Traffic Behavior Recognition From Traffic Videos Under Occlusion Condition: A Kalman Filter Approach,
2022
Singapore Management University
Traffic Behavior Recognition From Traffic Videos Under Occlusion Condition: A Kalman Filter Approach, Junfeng Jiao, Huihai Wang
Research Collection College of Integrative Studies
Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of …
Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System,
2022
Singapore Management University
Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao
Research Collection School Of Computing and Information Systems
Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which …
Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem,
2022
Singapore Management University
Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao
Research Collection School Of Computing and Information Systems
Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into …
Structured And Natural Responses Co-Generation For Conversational Search,
2022
National University of Singapore
Structured And Natural Responses Co-Generation For Conversational Search, Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Generating fluent and informative natural responses while maintaining representative internal states for search optimization is critical for conversational search systems. Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses directly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural responses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that generates the two concurrently. The key …
What Makes The Story Forward?: Inferring Commonsense Explanations As Prompts For Future Event Generation,
2022
Singapore Management University
What Makes The Story Forward?: Inferring Commonsense Explanations As Prompts For Future Event Generation, Li Lin, Yixin Cao, Lifu Huang, Shu Ang Li, Xuming Hu, Lijie Wen, Jianmin Wang
Research Collection School Of Computing and Information Systems
Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To …
Dynamic Topic Models For Temporal Document Networks,
2022
Singapore Management University
Dynamic Topic Models For Temporal Document Networks, Ce Zhang, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Dynamic topic models explore the time evolution of topics in temporally accumulative corpora. While existing topic models focus on the dynamics of individual documents, we propose two neural topic models aimed at learning unified topic distributions that incorporate both document dynamics and network structure. For the first model, by adding a time dimension, we propose Time-Aware Optimal Transport, which measures the probability of a link between two differently timestamped documents using their semantic distance. Since the gradually evolving topological structure of network may also influence the establishment of a new link, for the second model, we further design a Temporal …
Cosm2ic: Optimizing Real-Time Multi-Modal Instruction Comprehension,
2022
Singapore Management University
Cosm2ic: Optimizing Real-Time Multi-Modal Instruction Comprehension, Weerakoon Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Minh Anh Tuan Tran, Archan Misra
Research Collection School Of Computing and Information Systems
Supporting real-time, on-device execution of multi-modal referring instruction comprehension models is an important challenge to be tackled in embodied Human-Robot Interaction. However, state-of-the-art deep learning models are resource-intensive and unsuitable for real-time execution on embedded devices. While model compression can achieve a reduction in computational resources up to a certain point, further optimizations result in a severe drop in accuracy. To minimize this loss in accuracy, we propose the COSM2IC framework, with a lightweight Task Complexity Predictor, that uses multiple sensor inputs to assess the instructional complexity and thereby dynamically switch between a set of models of varying computational intensity …
Test Mimicry To Assess The Exploitability Of Library Vulnerabilities,
2022
Singapore Management University
Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo
Research Collection School Of Computing and Information Systems
Modern software engineering projects often depend on open-source software libraries, rendering them vulnerable to potential security issues in these libraries. Developers of client projects have to stay alert of security threats in the software dependencies. While there are existing tools that allow developers to assess if a library vulnerability is reachable from a project, they face limitations. Call graphonly approaches may produce false alarms as the client project may not use the vulnerable code in a way that triggers the vulnerability, while test generation-based approaches faces difficulties in overcoming the intrinsic complexity of exploiting a vulnerability, where extensive domain knowledge …
A Recommendation System Approach To Tune A Qubo Solver,
2022
Singapore Management University
A Recommendation System Approach To Tune A Qubo Solver, Siong Thye Goh, Jianyuan Bo, Matthieu Parizy, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
There are two major challenges to solving constrained optimization problems using a QuadraticUnconstrained Binary Optimization or QUBO solver (QS). First, we need to tune both the underlyingproblem parameters and the algorithm parameters. Second, the solution returned from a QSmight not be feasible. While it is common to use automated tuners such as SMAC and Hyperopt totune the algorithm parameters, the initial search ranges input for the auto tuner affect the performanceof the QS. In this paper, we propose a framework that resembles the Algorithm Selection(AS) framework to tune algorithm parameters for an annealing-based QS. To cope with constraints,we focus on …
A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets,
2022
Singapore Management University
A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang
Research Collection School Of Computing and Information Systems
Ride-sourcing services are increasingly popular because of their ability to accommodate on-demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand imbalance, as a result of which drivers may spend substantial time on idle cruising and picking up remote passengers. Some platforms attempt to mitigate the imbalance by providing relocation guidance for idle drivers who may have their own self-relocation strategies and decline to follow the suggestions. Platforms then seek to induce drivers to system-desirable locations by offering them subsidies. This paper proposes a mean-field Markov decision process (MF-MDP) model to depict the dynamics in ride-sourcing markets …
Legal And Regulatory Issues On Artificial Intelligence, Machine Learning, Data Science, And Big Data,
2022
Singapore Management University
Legal And Regulatory Issues On Artificial Intelligence, Machine Learning, Data Science, And Big Data, Wai Yee Wan, Michael Tsimplis, Keng Siau, Wei T. Yue, Fiona Fui-Hoon Nah, Gabriel M. Yu
Research Collection School Of Computing and Information Systems
Technological innovation creates numerous opportunities for businesses, organizations, and societies. Artificial intelligence, machine learning, data science, and big data provide opportunities for developing self-controlling systems emulating human intelligence. In some instances, these systems surpass the performance of humans. The relationship of innovative technology with the law is an important underpinning factor that is often overlooked. Law may encourage innovation but may also inhibit its development and application by adopting stringent regulatory provisions and liability regimes. This article examines the legal and regulatory issues related to new technologies such as artificial intelligence, machine learning, data science, and big data.
Identifying Legal And Ethical Values In Ai,
2022
Singapore Management University
Identifying Legal And Ethical Values In Ai, Keng Siau, Fiona Nah, Chunyan Ding, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) applications in health care, education, finance, mining, communications, and arts have brought about rapid and dramatic advances in these fields (Hyder et al., 2019). The rapidly expanding potential of AI in the economy and society has raised a set of legal and ethical issues (Wang and Siau, 2019; Siau and Wang, 2020). In-depth research into the ethical and legal aspects of AI to enable policymakers to introduce effective legislation and regulate AI development and applications is needed. This research uses a systematic qualitative research methodology, Value-Focused Thinking (VFT) (Keeney, 1996; Sheng et al., 2007), to identify the …
Analysis Of Digital Image Segmentation Algorithms,
2022
Nurafshan branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Analysis Of Digital Image Segmentation Algorithms, Khalilov Sirojiddin
Karakalpak Scientific Journal
Ushbu maqolada zamonaviy axborot-kommunikatsiya texnologiyalaridan foydalanishni kengaytirish maqsadida raqamli tasvirni qayta ishlash usullari va algoritmlari tahlil qilinadi. Maqolada, shuningdek, raqamli tasvirni qayta ishlash, tasvirni segmentatsiyalash usullari, WaterShed, MeanShift, FloodFill, GrabCut algoritmlarining afzalliklari va kamchiliklari o'rganiladi.
Interdisciplinary Communication By Plausible Analogies: The Case Of Buddhism And Artificial Intelligence,
2022
University of South Florida
Interdisciplinary Communication By Plausible Analogies: The Case Of Buddhism And Artificial Intelligence, Michael Cooper
USF Tampa Graduate Theses and Dissertations
Communicating interdisciplinary information is difficult, even when two fields are ostensibly discussing the same topic. In this work, I’ll discuss the capacity for analogical reasoning to provide a framework for developing novel judgments utilizing similarities in separate domains. I argue that analogies are best modeled after Paul Bartha’s By Parallel Reasoning, and that they can be used to create a Toulmin-style warrant that expresses a generalization. I argue that these comparisons provide insights into interdisciplinary research. In order to demonstrate this concept, I will demonstrate that fruitful comparisons can be made between Buddhism and Artificial Intelligence research.
Explainable And Cooperative Autonomy Across Networks Of Distributed Systems,
2022
University of South Florida
Explainable And Cooperative Autonomy Across Networks Of Distributed Systems, Peter Joseph Jorgensen
USF Tampa Graduate Theses and Dissertations
Large networks of complex systems-of-systems are commonplace and evermore present in both mundane and extraordinary facets of human existence. From the exponential growth of connectivity via the internet and other information networks, to the miniaturization of computers and sensors, to cross-domain sensor and communication networks, these networks of distributed systems-of-systems (NDSS) present incredible benefits and challenges. Autonomy is perhaps the most important and most difficult to achieve enabling technology for efficient performance of the NDSS. Giving each individual agent in a network the ability to manage its internal state in dynamic operating environments and in pursuit of multiple complex and …
Edgenext: Efficiently Amalgamated Cnn-Transformer Architecture For Mobile Vision Applications,
2022
Mohamed bin Zayed University of Artificial Intelligence
Edgenext: Efficiently Amalgamated Cnn-Transformer Architecture For Mobile Vision Applications, Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal, Salman Khan, Syed Waqas Zamir, Rao Anwer, Fahad Shahbaz Khan
Computer Vision Faculty Publications
In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficient general purpose networks due to their usefulness in several application areas. In this work, we strive to effectively combine the strengths of both CNN and Transformer models and propose a new efficient hybrid architecture EdgeNeXt. Specifically in EdgeNeXt, we introduce split depth-wise transpose attention (SDTA) encoder that splits input tensors into multiple channel groups and utilizes depth-wise convolution along with self-attention across channel …
Dynamic Prototype Convolution Network For Few-Shot Semantic Segmentation,
2022
University of Amsterdam, Netherlands
Dynamic Prototype Convolution Network For Few-Shot Semantic Segmentation, Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, Efstratios Gavves
Machine Learning Faculty Publications
The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among sup-port and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods im-plement such support/query interactions by solely leveraging plain operations - e.g., cosine similarity and feature concatenation - for segmenting the query objects. How-ever, these interaction approaches usually cannot well capture the intrinsic object details in the query images that are widely encountered in FSS, e.g., if the query object to be segmented has holes and slots, inaccurate segmentation al-most always happens. To this end, we propose a dynamic …
An Unsupervised Deep Neural Network For Image Fusion,
2022
School of Electrical and Information Engineering, Changzhou Institute of Technology, Changzhou 213032, China;
An Unsupervised Deep Neural Network For Image Fusion, Peipei Zhou, Xinglin Hou
Journal of System Simulation
Abstract: Due to the low dynamic range of camera, can not be expressed in the different region of the high dynamic scene a single-exposure image. An unsupervised depth neural network is constructed to fuse the multi-exposure images into a high dynamic image. Based on the VGG-Net, encoding and decoding sub-networks are designed. Guided by the structural similarity of the images before and after fusion, a loss function suitable for image fusion is designed by introducing the weight factors based on the local image information, and the valid information of the different input images is given consideration. Compared with the …
Research And Simulation Of Internet Of Vehicles Task Offloading Based On Mobile Edge Computing,
2022
School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;
Research And Simulation Of Internet Of Vehicles Task Offloading Based On Mobile Edge Computing, Peng Cheng, Wenzhu Zhang, Shuhan Xie, Zixuan Yang
Journal of System Simulation
Abstract: In order to use the computing resources of edge devices to provide high-quality services, a joint resource allocation and task offloading mechanism is designed for the Internet of Vehicles architecture based on mobile edge computing. In the mechanism, the original problem is decomposed into two sub-problems of resource allocation and offloading decision. The original problem is simplified into the resource allocation of maximizing system capacity, and the initial offloading set is obtained through the proportional resource allocation algorithm; the above problem is solved by the joint offloading decision-making and resource allocation mechanism. The stable experimental results are obtained …
Design And Realization Of 6-Dof Parachuting Simulation Training System,
2022
Naval Aviation University, Huludao 125001, China;
Design And Realization Of 6-Dof Parachuting Simulation Training System, Xiaoguang Zhou, Peng Zhu, Yuanyuan Zhang, Huan Lu, Yuan Zhou
Journal of System Simulation
Abstract: Aiming at restoring the 6-DOF motion process of each stage during parachuting, a 6-DOF parachuting simulation training system is designed and implemented. The architecture of the simulator is designed, and the realization of the sub-systems such as the motion calculation, 6-DOF motion platform, control loading system, virtual reality scene, somatosensory system and management console is explained. Compared with the same type of parachute simulator, this system has introduced a 6-DOF motion platform which can drive the trainees to simulate the various postures of parachuting. It can also help the trainees master the control methods of parachute and enhance the …
