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2019 International Conference on Information Technology and computer Application, ITCA 2019

作者： Ling, Jiayuan Li, Gangmin Xi'an Jiaotong-Liverpool University Dept. of Computer Science and Software Engineering Suzhou China

ISBN: (纸本)9781728164946

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times. © 2019 IEEE.

关键词： Classification (of information)

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MOOC Student Success Prediction Using Knowledge Distillation

MOOC Student Success Prediction Using Knowledge Distillation

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computer Information and Big Data Applications (CIBDA), International Conference on

作者： Tingting Kang Zhengang Wei Jianxiong Huang Zhaoliang Yao Dept. of Computer Science and Technology Ocean University of China Qingdao Dept. of Software Engineering Zhejiang University Hangzhou Dept. of Computer Science and Engineering Nanjing University of Science and Technology Nanjing

ISBN: (数字)9781728198378

ISBN: (纸本)9781728198385

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. Due to the low success rate of MOOCs, student success prediction has received much attention recently. Yet many utilize post-hoc prediction architectures, where model fitting requires features which are not knowable until a course completes. These works do not match the actual environment in which the prediction model is used, which may lead to unsatisfactory results. In order to build a reliable model that can be used in the actual environment, we propose a new student success prediction algorithm based on knowledge distillation. This algorithm can make the model obtain good prediction results with only a few basic features, and solve the problem of using features which are not knowable until a course completes.

关键词： Predictive models Hidden Markov models Training Prediction algorithms Machine learning Data models Task analysis

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Predicting The Resource Needs And Outcomes of Computationally Intensive Biological Simulations

Predicting The Resource Needs And Outcomes of Computationall...

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Spring Simulation Conference (SpringSim)

作者： Andrew Fisher Bhisma Adhikari Chao Zhai Joshua E. Morgan Vijay K. Mago Philippe J. Giabbanelli Dept. of Computer Science Lakehead University Thunder Bay ON Canada Dept. of Computer Science & Software Engineering Miami University Oxford OH USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

Accurately simulating viral dynamics within a human body is often computationally intensive, thus requiring dedicated computing infrastructures. This limits the use of simulations in personalized medicine since patients may not all have access to such infrastructures or the possibility of waiting weeks for predictions. Lowering the computational burden by simplifying the models is a challenge since simulation results must remain accurate in order to safely support the decision-making activities of patients. In this work, we use machine learning to predict the results of a simulation (i.e. surrogate model) and its computational resources (i.e. performance model). This allows patients to use machine learning as a computationally light proxy to simulations and to know how long an accurate simulation would take. We demonstrate that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling.

关键词： Computational modeling Machine learning Biological system modeling Predictive models Training Interpolation Data models

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Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows

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Digital Communications and Networks 2025年

作者： Xiaoliang Wang Liming Xu Lian Zhou Yuzhen Liu Neal Xiong Kuan-Ching Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Sanya Institute of Hunan University of Science and Technology Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China Department of Computer Mathematical and Physical Sciences Sul Ross State University Alpine TX 79830 USA Dept of Computer Science and Information Engineering Providence University Taichung 43301 Taiwan Republic of China

The convergence of the Internet of Things (IoT) and foundation models, particularly Large Language Models (LLMs), heralds a new era of intelligent and adaptive systems. This investigation explores the integration of LLMs and IoT for probabilistic vehicle trajectory prediction, a critical component in autonomous driving. Traditional deterministic models often fail to capture the complexity and uncertainty of real-world driving environments. To address these limitations, we propose an innovative approach that leverages LLM-driven spatio-temporal encoding and normalizing flows. The proposed method extracts latent motion patterns from historical trajectory data using LLMs, providing a nuanced understanding of vehicle dynamics. Advanced spatio-temporal encoders are employed for comprehensive feature extraction, capturing temporal dependencies and spatial relationships. Additionally, scaled dot-product attention is utilized for effective multimodal feature fusion, enhancing the integration of diverse data sources. The normalizing flows framework enhances the model's ability to capture complex, multimodal probability distributions of future trajectories by constructing an invertible transformation network. The network systematically maps simple base distributions to intricate target distributions through reversible transformations, significantly improving prediction accuracy. Evaluation of the nuScenes dataset demonstrates substantial improvements over existing methods. The results underscore the model's capability to generate diverse and realistic trajectory distributions, paving the way for safer and more efficient autonomous driving systems.

关键词： Internet of things Large language models Normalizing flows Spatio-temporal encoding Trajectory prediction

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Reducing Ambiguity in Requirements Elicitation via Gamification

Reducing Ambiguity in Requirements Elicitation via Gamificat...

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IEEE International Conference on Requirements engineering

作者： Hafsa Shareef Dar Dept. of Computer Science and Software Engineering Int. Islamic University Islamabad Islamabad Pakistan

ISBN: (数字)9781728174389

ISBN: (纸本)9781728174396

The overall quality and success of software highly depends on the involvement of stakeholders. Requirements elicitation supports RE analyst to gather requirements from the stakeholders based on their needs. There are multiple elicitation techniques present in literature and used by the practitioners. Some of them are questionnaires, interviews, prototyping, and user stories etc. However, these techniques are based on textual representation of requirements. These techniques are quite common among the requirement engineers yet problems of ambiguity, inconsistency, incompleteness still exist mostly due to their textual nature and lack of stakeholder involvement. Lack of clarity about the system increases the ambiguity of what exactly are the system requirements. Since elicitation is carried at an early stage of development the users are not sure of what they want, as requirements tend to evolve with the help of discussions and interactions among various stakeholders and technical team. Furthermore, the conventional elicitation methods are limited when it comes to stakeholders' participation and involvement, thus leaving a space for more ambiguous and incomplete requirements. In this work, Gamification, a game-based context will be used in non-gaming context for user involvement in fun ways. During elicitation, gamification would help to involve and interact with the stakeholders, with an intention to develop their interest in eliciting and finalizing system requirements. The goal of this paper is to reduce ambiguity during requirements elicitation. This would help in reducing the cost and time of development. Furthermore, we will elicit software requirements using gamification by developing a gamification tool with a focus to elicit unambiguous requirements by ensuring users' participation and maintaining interest. The validation of tool would be done using multiple confirmatory case studies from software industry.

关键词： Stakeholders Tools software Requirements engineering Games Unified modeling language Interviews

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Efficient Replanning Technique for QoS-aware Web Service Composition

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Transactions of the Korean Institute of Electrical Engineers 2020年第11期69卷 1724-1731页

作者： Kil, Hyunyoung Nam, Wonhong Dept. of Computer Science and Engineering Konkuk University Korea Republic of Dept. of Software Korea Aerospace University KOR Korea Republic of

A Web service is a service provided by software systems to support machine to machine inter-operations over Web. Given a set of Web services and a goal, the QoS-aware Web service composition problem constructs a composite Web service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the real world environment, frequent changes happen inherently-for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we have to solve a new problem again. Ordinary approaches to find a new solution from scratch may waste computation time, since they explore the whole searching space again. Hence, in this paper, we propose a novel replanning algorithm, which does not solve the new composition problem from scratch but explores only the changed space to identify the new optimal solution. In the experiment, our replanning algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch. © 2020 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Web services

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PatrIoT: IoT automated interoperability and integration testing framework

arXiv

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arXiv 2021年

作者： Bures, Miroslav Ahmed, Bestoun S. Rechtberger, Vaclav Klima, Matej Trnka, Michal Jaros, Miroslav Bellekens, Xavier Almog, Dani Herout, Pavel Dept. of Computer Science FEE Czech Technical University in Prague Prague Czech Republic Dept. of Math. & Computer Science Karlstad University Karlstad Sweden Red Hat Czech s.r.o. Brno Czech Republic Dept. of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom Software Engineering Dept. Shamoon College of Engineering Be'er Sheva Israel Dept. of CS and Engineering FAS University of West Bohemia Plzen Czech Republic

With the rapid growth of the contemporary Internet of Things (IoT) market, the established systems raise a number of concerns regarding the reliability and the potential presence of critical integration defects. In this paper, we present a PatrIoT framework that aims to provide flexible support to construct an effective IoT system testbed to implement automated interoperability and integration testing. The framework allows scaling from a pure physical testbed to a simulated environment using a number of predefined modules and elements to simulate an IoT device or part of the tested infrastructure. PatrIoT also contains a set of reference example testbeds and several sets of example automated tests for a smart street use case. Copyright © 2021, The Authors. All rights reserved.

关键词： Integration testing

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Ransomware: A framework for security challenges in internet of things 2

Ransomware: A framework for security challenges in internet ...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Saeed, Soobia Jhanjhi, N.Z. Naqvi, Mehmood Humayun, Mamoona Ahmed, Shakeel Universiti Teknologi Malaysia Dept. of Software Engineering Malaysia Taylor's University Selangor Malaysia Faculty of Engineering Technology Mohawk College Canada Jouf University Dept. of Information Systems Al-Jouf Saudi Arabia King Faisal University Dept. of Computer Science Al-Hofuf Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

With the increasing volume of smartphones, computers, and sensors in the Internet of Things (IoT) model, enhancing security and preventing ransom attacks have become a major concern. Traditional security mechanisms are no longer applicable due to the involvement of devices with limited resources, which require more computing power and resources. Ransomware is comparatively a new and cruel malware in cyberspace with higher rates of attacks around the world. Ransomware could encrypt entire data to make users unable to access their files and important information. In some cases, the system has been hostage completely by the hackers, and the user may receive a demand for ransom money using different resources o access of his/her own data/system. One of the problems associated with the Internet of Things is how to keep your smartphones secure and keep your data safe as most of the antivirus solutions are not useful in this case. This research concludes the impact of ransomware on the IoT, malware processes, and work on detecting and monitoring smartphone infections. The paper also discusses ransomware awareness to end-user with strategy to defeat it. © 2020 IEEE.

关键词： Smartphones

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CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking

arXiv

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arXiv 2025年

作者： Li, Weihong Liu, Xiaoqiong Fan, Heng Zhang, Libo Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China Dept. of Computer Science and Engineering University of North Texas DentonTX76207 United States Institute of Software Chinese Academy of Science Beijing100190 China

Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of hierarchical lightweight networks has become a prevalent strategy for enhancing efficiency in UAV tracking, it often results in a significant drop in network capacity, which further exacerbates challenges in UAV scenarios, such as frequent occlusions and extreme changes in viewing angles. To address these issues, we introduce a novel family of UAV trackers, termed CGTrack, which combines explicit and implicit techniques to expand network capacity within a coarse-to-fine framework. Specifically, we first introduce a Hierarchical Feature Cascade (HFC) module that leverages the spirit of feature reuse to increase network capacity by integrating the deep semantic cues with the rich spatial information, incurring minimal computational costs while enhancing feature representation. Based on this, we design a novel Lightweight Gated Center Head (LGCH) that utilizes gating mechanisms to decouple target-oriented coordinates from previously expanded features, which contain dense local discriminative information. Extensive experiments on three challenging UAV tracking benchmarks demonstrate that CGTrack achieves state-of-the-art performance while running fast. Code will be available at https://***/NightwatchFox11/CGTrack. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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ThunderGBM: fast GBDTs and random forests on GPUs

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4389-4393页

作者： Zeyi Wen Hanfeng Liu Jiashuai Shi Qinbin Li Bingsheng He Jian Chen Dept. of Computer Science and Software Engineering Uni. of Western Australia Australia School of Computing National University of Singapore Singapore School of Software Engineering South China University of Technology Guangzhou China

Gradient Boosting Decision Trees (GBDTs) and Random Forests (RFs) have been used in many real-world applications. They are often a standard recipe for building state-of-the-art solutions to machine learning and data mining problems. However, training and prediction are very expensive computationally for large and high dimensional problems. This article presents an efficient and open source software toolkit called ThunderGBM which exploits the high-performance Graphics Processing Units (GPUs) for GBDTs and RFs. ThunderGBM supports classification, regression and ranking, and can run on single or multiple GPUs of a machine. Our experimental results show that ThunderGBM outperforms the existing libraries while producing similar models, and can handle high dimensional problems where existing GPU-based libraries fail. Documentation, examples, and more details about ThunderGBM are available at https://***/xtra-computing/thundergbm.

关键词： gradient boosting decision trees random forests GPUs efficiency

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