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arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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Hybrid Estimation of Distribution Based on Knowledge Transfer for Flexible Job-shop Scheduling Problem 4

Hybrid Estimation of Distribution Based on Knowledge Transfe...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Cao, Lulu Jiang, Min Feng, Liang Lin, Qiuzhen Pan, Renhu Tan, Kay Chen Xiamen University School of Informatics Fujian China Chongqing University College of Computer Science Chongqing China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longking CO.LTD. Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this work, we introduce a new method called G-EDA for solving flexible job-shop scheduling problems (FJSP). Based on the investigation of current works, it can be found that the solution of FJSP mainly focuses on the intelligent optimization algorithm, such as the genetic algorithm, which obtains new population through crossover and mutation. When searching individuals, this algorithm does not fully use the knowledge hidden in the excellent individuals in the previous generation population, and its performance is poor when facing high-dimensional problems. This paper hopes to find a method to find the distribution law of high-quality individuals in the previous generation population and transfer the knowledge contained in the previous generation population to the next generation to improve the performance of the algorithm. In this paper, we propose a hybrid estimation of distribution algorithm using population grouping mechanism(G-EDA). We conduct numerical simulations based on two sets of international standard examples. And we compare G-EDA with some existing advanced algorithms. The results show that G-EDA is effective and practical in solving multi-objective FJSP. © 2022 IEEE.

关键词： Genetic algorithms

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Collaborative City Digital Twin for the COVID-19 Pandemic:A Federated Learning Solution

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Tsinghua Science and Technology 2021年第5期26卷 759-771页

作者： Junjie Pang Yan Huang Zhenzhen Xie Jianbo Li Zhipeng Cai the College of Computer Science and Technology Qingdao UniversityQingdao 266000China the Business School Qingdao UniversityQingdao 266000China the College of Computing and Software Engineering Kennesaw State UniversityAtlantaGA 30060USA the College of Computer Science and Technology Jilin UniversityChangchun 130012China the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

The novel coronavirus,COVID-19,has caused a crisis that affects all segments of the *** the knowledge and understanding of COVID-19 evolve,an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the *** studies indicate that a city Digital Twin(DT)is beneficial for tackling this health crisis,because it can construct a virtual replica to simulate factors,such as climate conditions,response policies,and people's trajectories,to help plan efficient and inclusive ***,a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions,limiting its advantages when facing urgent crises,such as the COVID-19 *** Learning(FL),in which all clients can learn a shared model while retaining all training data locally,emerges as a promising solution for accumulating the insights from multiple data sources ***,the enhanced privacy protection settings removing the privacy barriers lie in this *** this work,we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status *** particular,an FL central server manages the local updates of multiple collaborators(city DTs),providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection *** approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a"global view"of city crisis ***,it also helps improve each city's DT by consolidating other DT's data without violating privacy *** this paper,we use the COVID-19 pandemic as the use case of the proposed *** experimental results on a real dataset with various response plans val

关键词： COVID-19 Digital Twin(DT) Federated Learning(FL) deep learning

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Global Reliable Diagnosis of Networks Based on Self-Comparative Diagnosis Model and G-Good-Neighbor Property

SSRN

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SSRN 2024年

作者： Wang, Mujiangshan Xu, Shuhao Jiang, Jincheng Xiang, Dong Hsieh, Sun-Yuan Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Guangdong Academy of Agricultural Sciences China

In the research field of system-level diagnosis, people used to focus on studying different diagnosabilities with restrictions. By studying this, reliability measured by these diagnosabilities of various of networks is obtained. However, for the decentralized parallel computing systems, there are always upper bonds for diagnosabilities, which means there exists an undiagnosable subnetwork cut off by a faulty vertex cut if the subnetwork is large enough. In this paper, we set up a 2-stage plan for the global reliable diagnosis of networks combining Self-Comparative diagnosis (SC) model and the g-good-neighbor property, which means every vertex in the networks is diagnosed with high accuracy. Six algorithms are proposed for the realization of the plan and diagnosis model for g≥2. In the end, we code a simulator for the plan and test how well it works and the codes are provided via Github link for readers' check. © 2024, The Authors. All rights reserved.

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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An Exploratory Comparative Study on the Impacts of Technical Support on Student Successes in computing Project-Based Learning

An Exploratory Comparative Study on the Impacts of Technical...

引用

Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman Jan DeWaters Daqing Hou Yu Liu David Shepherd Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Institute for STEM Education Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of software engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of software engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for suppor

关键词： Surveys Statistical analysis Education Decision making Database systems software measurement Continuous improvement Springs software engineering

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Reinforcement Learning-based Dynamic Resource Allocation For Grant-Free Access

Reinforcement Learning-based Dynamic Resource Allocation For...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mariam Elsayem Hatem Abou-zeid Ali Afana Sidney Givigi School of Computing Queen's University of Kingston Kingston ON Canada Department of Electrical and Software Engineering ICT 402 University of Calgary Calgary AB the Ericsson Ottawa ON

ISBN: (纸本)9781665435413

Cellular networks have evolved to deliver high-speed broadband services to support the requirements of IoT applications, which demand high speed, low latency, and massive capacity. A primary market goal is to provide support for ultra-reliable low latency communication (URLLC). URLLC requires sub-milliseconds-level latencies as defined by the third generation partnership project (3GPP). One of the promising technologies to achieve the aforementioned specifications is grant-free (GF) access for uplink resources. The GF scheme enables the user equipment (UE) to transmit data over pre-allocated resources which reduces communication latency. This paper proposes an intelligent Reinforcement Learning (RL) based allocator of grants trained via Deep Q-Learning. The experimental results show effect of the number of UEs in the network, and the percentage of unstable UEs on the speed of the RL agent's convergence.

关键词： Q-learning 5G mobile communication Ultra reliable low latency communication Dynamic scheduling Resource management Internet of Things Global communication

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Visual Question Generation for Explicit Questioning Purposes Based on Target Objects

SSRN

引用

SSRN 2022年

作者： Xie, Jiayuan Fang, Wenhao Chen, Jiali Cai, Yi Li, Qing School of Software Engineering South China University of Technology Guangzhou China Ministry of Education China Department of Computing Hong Kong Polytechnic University Hong Kong

Visual question generation aims to focus on some target objects in an image to generate questions with certain questioning purposes. Existing studies mainly utilize an answer to extract the target object corresponding to the questioning purpose for questioning. However, answers fail to accurately and completely map to every target object, such as the objects corresponding to the answer are ambiguous or the answers are the relationship between multiple objects. To address this problem, we propose a content-controlled question generation model, which generates questions based on a given target object set specified from an image. Considering that the target objects have different contributions during the generation process, we design a recurrent generative architecture to explicitly control attention to different objects and their corresponding image information at each generative stage. Extensive experiments on the VQA v2.0 dataset and the Visual7w dataset show that the proposed model outperforms the state-of-the-art models and can controllably generate questions with specified content. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Parallel Global Sequence Alignment with Arbitrary Scoring Scheme: Algorithm Formulation and Evaluation

SSRN

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SSRN 2022年

作者： Sadiq, Muhammad Umair Yousaf, Muhammad Murtaza Department of Computer Science Government College University Lahore54000 Pakistan Department of Software Engineering Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan

Alignment of the sequences is useful in many biological and non-biological domains. Needleman-Wunsch algorithm is deemed as the basic algorithm for finding the optimal alignment between two sequences. It is a dynamic programming based algorithm which has quadratic time and space requirement. Nonetheless, as a result of increase in the capacity of sequence databases in recent years, sizes of sequences may contain billions of characters for computationally intensive problems. Comparison of such large sequences using sequential methods is avoided as it takes unacceptable amount of time for producing the results. In this paper, we proposed a parallel variation of the Needleman-Wunsch algorithm by reformulating the dependencies in the prevalent dynamic programming solution. Proof of correctness of the proposed algorithm is established. It evinces certain characteristics such as it allows arbitrary linear scoring scheme when comparing the sequences which is important to obtain peerless alignment. Moreover, it ensures load balancing among the computational units and maximizes the utilization of resources. It is compared with various state-of-the-art approaches. Experimental results using the real DNA and user trajectories sequences over graphic processing units (GPUs) suggest that the proposed algorithm is notably superior to the existing approaches that allow arbitrary linear scoring schemes. It achieved the GCUPs up to 14.06 and maximum speedup of 48.18x compared to the sequential implementation. © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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