Path-Based Model For The Heterogeneous-Fleet Electric Vehicle Routing Problem With Partial Linear Recharging,
2022
1.School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China;3.Department of Information Management, University of International Business and Economics, Beijing 100029, China;
Path-Based Model For The Heterogeneous-Fleet Electric Vehicle Routing Problem With Partial Linear Recharging, Weiquan Wang, Ding Ding, Linsha Yan
Journal of System Simulation
Abstract: The heterogeneous-fleet electric vehicle routing problem with partial linear recharging is studied for realistic logistics distribution scenarios using multiple electric vehicle fleets with different transport capacities, driving ranges and acquisition costs. A path-based mixed integer linear model is proposed. The model enumerates the paths visited by all vehicle types between any non-charging nodes, eliminates the infeasible paths through capacity constraints and time window constraints, and eliminates the dominated paths by the dominance criterion. Compared with the traditional charging station replica-based model, this model eliminates the need to set the number of charging station replicas. The results show that the …
Chatgpt Goes To Law School,
2022
USC Gould School of Law
Chatgpt Goes To Law School, Jonathan H. Choi, Kristin E. Hickman, Amy B. Monahan, Daniel Schwarcz
Journal of Legal Education
No abstract provided.
Objective Measure Of Working Memory Capacity Using Eye Movements,
2022
Old Dominion University
Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna
Undergraduate Research Symposium
Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …
Two-Stage Transfer Learning For Facial Expression Classification In Children,
2022
Old Dominion University
Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin
Undergraduate Research Symposium
Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …
Analyzing Decision-Making In Robot Soccer For Attacking Behaviors,
2022
University of South Florida
Analyzing Decision-Making In Robot Soccer For Attacking Behaviors, Justin Rodney
USF Tampa Graduate Theses and Dissertations
In robotics soccer, decision-making is critical to the performance of a team’s SoftwareSystem. The University of South Florida’s (USF) RoboBulls team implements behavior for the robots by using traditional methods such as analytical geometry to path plan and determine whether an action should be taken. In recent works, Machine Learning (ML) and Reinforcement Learning (RL) techniques have been used to calculate the probability of success for a pass or goal, and even train models for performing low-level skills such as traveling towards a ball and shooting it towards the goal[1, 2]. Open-source frameworks have been created for training Reinforcement Learning …
Localization Techniques Overview Towards 6g Communication,
2022
Multimedia University, Malaysia
Localization Techniques Overview Towards 6g Communication, Nawaid Hasan, Azlan Abd Aziz, Azwan Mahmud, Nur Asyiqin Binte Hamzah, Noor Ziela Abd Rahman
Journal of Informatics and Web Engineering
Worldwide Researchers and scientist have started the investigation of the sixth generation (6G) while the fifth generation (5G) cellular system is being deployed. Under this main investigation the main aim of 6G is to provide intelligent and ubiquitous wireless connectivity with Terabits per second (Tbps) data rates. Accurate location information of the mobile devices is very much useful to accomplish these aims with the improvements of various parameters of wireless communication. The development in communication technology often creates new opportunities to improve the localization efficiency as demonstrated by the expected centimetre-level localization accuracy in 6G. While there are comprehensive literatures …
Incorporating Semi-Automated Approach For Effective Software Requirements Prioritization: A Framework Design,
2022
Multimedia University, Malaysia
Incorporating Semi-Automated Approach For Effective Software Requirements Prioritization: A Framework Design, Fang-Fang Chua, Tek-Yong Lim, Bushra Tajuddin, Amarilis Putri Yanuarifiani
Journal of Informatics and Web Engineering
Software Requirements Prioritization (SRP) is one of the crucial processes in software requirements engineering. It presents a challenging task to decide among the pool of requirements and the variance of the stakeholder’s needs in prioritizing requirements. Semi-automated requirements prioritization is implemented in both manual and automatic processes. When prioritizing requirements, these aspects such as importance, time, cost and risk, should be taken into account. The emergence of machine learning is advancing to improve and automate the SRP process whereby decision making can be performed with minimal human intervention. Incorporating machine learning approaches in prioritization techniques can be implemented in the …
Performance Of Sentiment Classification On Tweets Of Clothing Brands,
2022
Multimedia University, Malaysia
Performance Of Sentiment Classification On Tweets Of Clothing Brands, Muhammad Shafiq Jalani, Hu Ng, Timothy Tzen Vun Yap, Vik Tor Goh
Journal of Informatics and Web Engineering
Social media such as Facebook, Instagram, LinkedIn, and Twitter ease the sharing of ideas, thoughts, videos, and photos and information through the building of virtual networks and communities. This has allowed companies and products to reach a wider audience in terms of marketing and advertising, and to gauge feedback from the public. This research investigates clothing brand mentions on Twitter to perform sentiment analysis on users’ thoughts on three clothing brands, namely Asos, Uniqlo and Topshop. The data is collected by applying python libraries, Tweepy to access data from the Twitter streaming API. Following that, data pre-processing such as tokenization, …
Backup Automation Using Power Automate For Malaysian Vaccination Centres,
2022
Multimedia University, Malaysia
Backup Automation Using Power Automate For Malaysian Vaccination Centres, Raadhesh Kannan, Chin Ji Jian
Journal of Informatics and Web Engineering
The Covid-19 pandemic has tested the world, especially the vaccine centres when it comes to logistics, maintenance of records, and issuing of vaccination certifications. The Malaysian vaccination centres (PPVs) known as Pusat Pemberian Vaksin in Malaysia have a secure, and reliable system to maintain records of who has been vaccinated and when. No system is fully secure and reliable, thus there needs to be a backup if anything happens to the records like data loss or tampering of records. Although the information flow is simplified using the MySejahtera app and scanning of QR codes, there is no contingency prepared should …
The Impacts Of The Use Of Thematic & Chronologic Multi-Modal Information Representation On Sequential And Global Students’ Historical Understanding,
2022
Malaysia University of Science and Technology, Malaysia
The Impacts Of The Use Of Thematic & Chronologic Multi-Modal Information Representation On Sequential And Global Students’ Historical Understanding, Ling Weay Ang, Sellappan Palaniappan
Journal of Informatics and Web Engineering
This study examined the two different modes of multi-modes information presentations that affected sequential and global learners' history understanding: thematic and chronological. A total of 134 secondary schools’ students were enrolled (69 learning in chronological mode, 65 learning in thematic mode). Before the start of the treatment session, students were given a pre-test. The results showed that multimodal information presentation did not have a significantly great impact on historical learning or between pupils who learn in chronological and thematic ways. The chronological frame of reference technique, which reflected an interactive timeline, was reported to have supported students' sequential learning in …
Robustness Analysis Of Classification Using Recurrent Neural Networks With Perturbed Sequential Input,
2022
Leigh University, PA, United States
Robustness Analysis Of Classification Using Recurrent Neural Networks With Perturbed Sequential Input, Guangyi Liu, Arash Amini, Martin Takac, Nader Motee
Machine Learning Faculty Publications
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices. The sequential inputs can be perturbed in various ways, e.g., streaming images can be deformed due to robot motion or imperfect camera lens. Using the notion of the Voronoi diagram and Lipschitz properties of stable RNNs, we provide a thorough analysis and characterize the maximum allowable perturbations while guaranteeing the full accuracy of the classification task. We illustrate and validate our theoretical results using a map dataset with clouds …
Deep Learning Techniques For Diabetic Retinopathy Classification: A Survey,
2022
Mohamed bin Zayed University of Artificial Intelligence
Deep Learning Techniques For Diabetic Retinopathy Classification: A Survey, Mohammad Z. Atwany, Abdulwahab H. Sahyoun, Mohammad Yaqub
Computer Vision Faculty Publications
Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Diabetic Retinopathy is regarded as the leading cause of blindness for diabetic patients, especially the working-age population in developing nations. Treatment involves sustaining the patient's current grade of vision since the disease is irreversible. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient's vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money, and effort consuming and involves …
Pseudo-Stereo For Monocular 3d Object Detection In Autonomous Driving,
2022
Zhejiang University, China
Pseudo-Stereo For Monocular 3d Object Detection In Autonomous Driving, Yi-Nan Chen, Hang Dai, Yong Ding
Computer Vision Faculty Publications
Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in …
Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community,
2022
Singapore Management University
Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay
Centre for AI & Data Governance (2019-2025)
Pivoting on the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the HRI interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed Decision to Trust model considers more extensively the human and situational factors influencing how trust …
Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems,
2022
University of Nebraska-Lincoln
Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler
School of Computing: Faculty Publications
Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …
Innovative Human Motion Sensing With Earbuds,
2022
Singapore Management University
Innovative Human Motion Sensing With Earbuds, Dong Ma, Andrea Ferlini, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
Earbuds, ear-worn wearables, have attracted growing attention from both industry and academia. This trend has witnessed manufacturers embedding multiple sensors on earbuds to enrich their functionalities. For example, Apple AirPods, Sony WF-1000XM3, and Bose QuietControl 30, have been equipped with accelerometers for tapping interaction or multiple microphones for noise cancellation. On the other hand, the research community regards earbuds as a powerful personal-scale human sensing and computing platform. By integrating sensors like PPG, barometer, and ultrasonic sensors, researchers have been devising a plethora of earable sensing applications, such as blood pressure monitoring [1], facial expression recognition [2], and authentication [3].
Deep Learning For Anomaly Detection: A Review,
2022
Singapore Management University
Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. …
Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations,
2022
Missouri University of Science and Technology
Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank
Psychological Science Faculty Research & Creative Works
Artificial agents such as robots, chatbots, and artificial intelligence systems can be the perpetrators of a range of moral violations traditionally limited to human actors. This paper explores how people perceive the same moral violations differently for artificial agent and human perpetrators by addressing three research questions: How wrong are moral foundation violations by artificial agents compared to human perpetrators? Which moral foundations do artificial agents violate compared to human perpetrators? What leads to increased blame for moral foundation violations by artificial agents compared to human perpetrators? We adapt 18 human-perpetrated moral violation scenarios that differ by the moral foundation …
Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!),
2022
Cambridge University
Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire
Research Collection Lee Kong Chian School Of Business
Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n …
Viral Pneumonia Screening On Chest X-Rays Using Confidence-Aware Anomaly Detection,
2022
Singapore Management University
Viral Pneumonia Screening On Chest X-Rays Using Confidence-Aware Anomaly Detection, Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxing Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia
Research Collection School Of Computing and Information Systems
Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware …
