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arXiv 2020年

作者： Heris, Mostapha Kalami Rahnamayan, Shahryar Intelligent Systems Lab. Research and Development Department FaraDars Online Education Organization Tehran Iran Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Climate change

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Development of an all-based method using blockchain technologies and cuda technologies 2

Development of an all-based method using blockchain technolo...

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2nd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2020

作者： Domkiv, Tetiana Dudnik, Andriy Dakhno, Nataliia Kvasnikov, Volodymyr Trush, Olexander Dorozhynskyi, Serhii National Aviation University Department of Software Engineering Kyiv Ukraine Kyiv National Taras Shevchenko University Department of Network and Internet Technologies Kyiv Ukraine National Aviation University Department of Computerized Electrical Systems and Technologies Kyiv Ukraine National Aviation University NAU Cybersecurity R&D Lab Kyiv Ukraine

ISBN: (纸本)9781728197999

The article presents a method of using parallel computing technology using CUDA technology and blockchain to recover the key(s) of an encrypted PDF file. The analysis and comparison of the speed of calculations using the graphic processor (GPU) and central processing unit (CPU). Testing the implemented method. A statistical analysis of the results of experimental studies is presented. © 2020 IEEE.

关键词： Blockchain

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Exploiting Crowd-based Learning Method in software engineering Course

Exploiting Crowd-based Learning Method in Software Engineeri...

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International Conference on Computer Science & Education (ICCSE)

作者： Xinjun Mao Yao Lu Yi Yang Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

software engineering is one of main major course for computer science. The main objective of this course is to improve the engineering capabilities of students to develop complex software systems. However, current course practice methods are teacher-centered and thus highly depend on the limited talents, experiences and efforts of teachers. Such methods greatly restrict students' scopes to gain necessary skills and artifacts, and to draw on successful experiences to address various personalized engineering issues and therefore enhance abilities to tackle engineering complexity. This paper presents a crowd-based learning method and introduces our exploration to use the method to reform the software engineering course practice. The essence of our exploration is to exploit the wisdoms and artifacts of crowds over Internet to support students to accomplish their course projects. The former is the engineering knowledges shared in question and answering (Q&A) platforms such as StackOverflow, and the latter is the open source software (OSS) hosted in OSS sites such as GitHub. We use the method in two separate course practices that aim to improve students' abilities to develop original, high-quality and large-scale software systems. The results of three-year explorations are positive and impressive and show that the crowd-based learning method is effective.

关键词： Education software engineering Learning systems Crowdsourcing software systems

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Path-following Control of Fish-like Robots: A Deep Reinforcement Learning Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8163-8168页

作者： Tianhao Zhang Runyu Tian Chen Wang Guangming Xie National Engineering Research Center for Software Engineering Peking University Beijing 100871 China The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing 100871 China Institute of Ocean Research Peking University Beijing 100871 China

In this paper, we propose a deep reinforcement learning (DRL) approach for path-following control of a fish-like robot. The desired path may be a randomly generated Bézier curve. First, to implement the locomotion control of the fish-like robot, we design a modified Central Pattern Generated (CPG) model, using which the fish achieves varied swimming behaviors just by adjusting a single control input. To reduce the reality gap between simulation and the physical system, using the experimental data of the real fish-like robot, we build a surrogate simulation environment, which also well balances the accuracy and the speed of training. Second, for the path-following control, we select the advantage actor-critic (A2C) approach and train the control policy in the surrogate simulation environment with a straight line as the desired path. Then the trained control policy is directly deployed on a physical fish-like robot to follow a randomly generated Bézier curve. The experimental results show that our proposed approach has good practical applicability in view of its efficiency and feasibility in controlling the physical fishlike robot. This work shows a novel and promising way to control biomimetic underwater robots in the real world.

关键词： Reinforcement learning control autonomous underwater vehicles biomimetic underwater robots path following deep learning

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6G Internet-of-Things assisted smart homes and buildings: Enabling technologies, opportunities and challenges

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Internet of Things 2025年 32卷

作者： Arif Ullah Fawad Aamir Nadeem Muhammad Arif Muhammad Mehran Bashir Wooyeol Choi School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Gwangju Institute of Science and Technology Gwangju Republic of Korea Chonnam National University Gwangju Republic of Korea Institute of Networked and Embedded Systems University of Klagenfurt 9020 Klagenfurt Austria Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs 9020 Klagenfurt Austria Department of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan Pakistan School of Computer Science and Engineering College of Software Chung-Ang University Seoul Republic of Korea

Smart homes (SHs) and smart buildings (SBs) play a crucial role in addressing global urbanization and are key elements of smart cities (SCs). They utilize integrated systems to process large volumes of data, enabling intelligent responses and improved quality of life. However, the growing number of connected devices and increasing data demands have exceeded the capabilities of existing network technologies, reducing service effectiveness. This article provides a comprehensive survey of emerging technologies, especially those underexplored in SH and SB contexts. It highlights the potential of sixth-generation (6G) wireless networks and the Internet of Things (IoT) to transform smart indoor environments through greater automation, intelligence, and adaptability. The authors first analyze the limitations of current IoT-based systems and then examine advanced 6G technologies, including integrated sensing and communication (ISAC), machine learning (ML), visible light communication (VLC), reconfigurable intelligent surfaces (RIS), and blockchain (BC). These technologies aim to resolve issues related to scalability, connectivity, and interoperability that challenge current IoT/5G models. Unlike previous studies focusing on narrow areas such as energy or security, this survey takes a holistic approach. It aligns 6G capabilities with SH/SB needs like human-device interaction, energy efficiency, and robust security, and introduces new applications such as AI-driven digital twins and edge intelligence. The article also identifies research gaps, including resource allocation in diverse networks and ethical AI deployment. It offers guidance for future research and urges collaboration, standardization, and human-centric design to enable next-generation intelligent living environments.

关键词： 6G AI Blockchain Integrated sensing and communication IoT Smart buildings Smart homes VLC

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A bottom-up paradigm for traffic scene graph representation 9

A bottom-up paradigm for traffic scene graph representation

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Zhixuan Zhang, Chi Liu, Yuehu Su, Yuanqi Li, Ping Zheng, Jinzi School of Software Engineering Xi'An Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China Shaanxi Key Lab of Digital Technology and Intelligent Systems Xi'an China School of Computer Science and Technology Xi'An Jiaotong University Xi'an China China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (纸本)9781450387835

With increasing hardware computing power and model capacity, visual tasks for scene cognitive understanding have attracted more attention, such as visual relationships inference. The scene graph representation formed by a coupling of objects, attributes and relationships nodes displayed by different modalities of information, including original image, foreground things, background stuff and scene attributes, strongly promotes the progress of research area. In this paper, we address the scene graph representation of traffic scenarios for autonomous driving. It should be noted that the universal representation are the specific needs of cognitive understanding of traffic scenes: on the one hand, there is a lack of fine-grained description of key objects and attributes;on the other hand, there are redundant descriptions of objects and relationships. To tackle these problems, we take advantage of the fine-grained instance-level annotation of the traffic scene, proposing a bottom-up representation paradigm. It makes full use of the hierarchical structure of the traffic scene and the sparsity of element classes. In addition, on the basis of the existing methods, we optimize the relationship list of traffic scene graph representation. Moreover, we improve the scene graph annotation methods, proposing a "ground-vision joint location method"to better describe the spatially-distributed visual knowledge. The case analysis showed that compared with existing methods, our paradigm for scene graph can represent more abundant traffic scene information. © 2020 ACM.

关键词： Knowledge representation

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Vehicle Re-Identification by Deep Feature Embedding and Approximate Nearest Neighbors

Vehicle Re-Identification by Deep Feature Embedding and Appr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Artur O. R. Franco Felipe F. Soares Aloísio V. Lira Neto José A. F. de Macêdo Paulo A. L. Rego Fernando A. C. Gomes José G. R. Maia Federal University of Ceará (UFC) Group of Computer Networks Software Engineering and Systems (GREat) Brazilian Federal Highway Police Insight Data Science Lab Virtual UFC Institute

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Disorganized urban growth has led cities to chaos, which has brought countless challenges for their development in several sectors, such as traffic organization, public safety, and transportation. Vehicular re-identification (ReID) technologies have become increasingly important in this context since these allow to produce insights capable of benefiting many areas. As recently, methods frequently resorted to Deep Learning and Convolutional Neural Networks (CNNs), especially in the design of loss functions capable of improving the learning capacity of CNNs. Couple and triplet loss techniques have gained prominence, but their effectiveness depends on the mining of samples to converge properly. In this paper, we investigate a new simple approach for vehicle ReID by combining sample mining strategy and approximate nearest neighbor (ANN) method to improve retrieval quality. By relying only on relatively low-dimensional deep features, we were able to obtain state-of-the-art performance on the VeRi-776 dataset in terms of mAP, HIT@1, and HIT@5 metrics, but using relatively simple CNN and ANN methods, which are feasible in CPU for real-time scenarios.

关键词： Measurement Cameras Training Licenses Image color analysis Safety Urban areas

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Collective Intelligence for Smarter Neural Program Synthesis

Collective Intelligence for Smarter Neural Program Synthesis

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IEEE/ACM International Conference on Automated software engineering - Workshops (ASE Workshops)

作者： Daiyan Wang Wei Dong Yating Zhang College of Computer Science National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781450381284

ISBN: (纸本)9781728172958

We study the problem of automatically generating source code from different forms of user intents. Existing methods treating this problem as a language generating task of the neural network, known as Neural Program Synthesis (NPS). Most of these methods struggle with achieving high generating accuracy, one reason for that is the incompleteness and inaccuracy of user intents for a specific programming task. Inspired by the Swarm Intelligence (SI) and Collective Intelligence (CI) techniques, we proposed an automatic task-specific user intent merging framework combining both the bio-inspired algorithm in SI and CI merged from multiple developers. Empirically, we show that our approach is able to provide more accurate and adequate input for NPS, and our experiment on CI indicates that knowledge merging among isolated software developers in our approach has a significant influence on NPS.

关键词： Task analysis Java Training software Data mining Prediction algorithms Particle swarm optimization

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Boosting Component-based Synthesis with API Usage Knowledge

Boosting Component-based Synthesis with API Usage Knowledge

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IEEE/ACM International Conference on Automated software engineering - Workshops (ASE Workshops)

作者： Jiaxin Liu Wei Dong Binbin Liu College of Computer Science National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781450381284

ISBN: (纸本)9781728172958

Component-based synthesis is one of the hottest research areas in automated software engineering. It aims to generate programs from a collection of components like Java library. However, the program space constituted by all the components in the library is fairly large, which leads to a vast number of candidate programs generated for a long time. The intractability of the program space affects the synthesis efficiency of the program and the size of the program generated. In this paper, we propose ITAs, a framework of iterative program synthesis via API usage knowledge from the Internet, which can significantly improve the efficiency of program synthesis. ITAs aims to constrain the program space by combining two main ideas. First, narrow down the program space from the outside via the guidance of API usage knowledge. Second, expand the program space from the inside via iterative strategy based on knowledge. For evaluation, we collect a set of programming tasks and compare our approach with a program synthesis tool on synthesizing these tasks. The experiment results show that Itas can significantly improve the efficiency of program synthesis, which can reduce the synthesis time by 97.1 % than the original synthesizer.

关键词： Task analysis Libraries Internet Programming Tools Natural languages Java

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Evo-Clocks: software Evolution at a Glance

Evo-Clocks: Software Evolution at a Glance

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Carol V. Alexandru Sebastian Proksch Pooyan Behnamghader Harald C. Gall Software Evolution and Architecture Lab University of Zurich Switzerland Center for Systems and Software Engineering University of Southern California USA

Understanding the evolution of a project is crucial in reverse-engineering, auditing and otherwise understanding existing software. Visualizing how software evolves can be challenging, as it typically abstracts a multi-dimensional graph structure where individual components undergo frequent but localized changes. Existing approaches typically consider either only a small number of revisions or they focus on one particular aspect, such as the evolution of code metrics or architecture. Approaches using a static view with a time axis (such as line charts) are limited in their expressiveness regarding structure, and approaches visualizing structure quickly become cluttered with an increasing number of revisions and components. We propose a novel trade-off between displaying global structure over a large time period with reduced accuracy and visualizing fine-grained changes of individual components with absolute accuracy. We demonstrate how our approach displays changes by blending redundant visual features (such as scales or repeating data points) where they are not expressive. We show how using this approach to explore software evolution can reveal ephemeral information when familiarizing oneself with a new project. We provide a working implementation as an extension to our open-source library for fine-grained evolution analysis, LISA.

关键词： software History Data visualization Visualization Clocks Periodic structures

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