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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11, Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords：人群计数；卷积神经网络；密度估计；语义分割；多任务学习

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Dynamic Power Management for I/O Devices Under Multi-task Environment

Qi Longning,Zhang Zhe,Huang Shaomin

Strategic Study of CAE 2008, Volume 10, Issue 2, Pages 60-65

Abstract:

More embedded system designers pay attention to how to reduce the po wer consumption of I/O devices. Traditional dynamic power management (DPM) polic ies only focus on the device requests, and neglect the application features behi nd the workload. Because of the assumption about the stationary workload, tradit ional DPM policies can not reach their expected goal under the multi-task envir o nment. The paper presents a stack-based predictive timeout strategy (SBPT). It c an predict the access pattern of the device I/O operations by analyzing the ca lling and stack information of tasks and combine predictions of multiple tasks to form the global prediction according to the multiple-service-requester mode l. At last, classify the I/O request by the global prediction and then make the de cision with the timeout technique based on the distribution of the grouped reque sts. An evaluation study of SBPT using the trace-driven simulation is performed. The results show that SBPT can adapt the non-stationary multi-task environment and reduces power consumption more efficiently than other policies.

Keywords： DPM predictive timeout stack multiple service requesters

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Deep Space Exploration and Its Prospect in China

Ye Peijian,Peng Jing

Strategic Study of CAE 2006, Volume 8, Issue 10, Pages 13-18

Abstract:

The definition, goal and impacts of deep space exploration are summarized. After a retrospect to past deep space exploration activities of human being to date, both recent deep space missions and future missions in 5 years are listed. There are also brief introductions about the future strategic plans of NASA, ESA, RAKA, JAXA and ISRO. Then the authors analyzed some important features of global deep space exploration scheme. Key technologies of deep space exploration are also determined. The status of China´s deep space exploration plan is introduced including CE-1 lunar orbiter, the subsequent China Lunar Exploration Program, especially proposals for the second stage of China Lunar Exploration Program, China-Russia Mars Exploration, Kuafu Mission, Hard X-Ray Modulated Telescope, Space Solar Telescope. At the end, some suggestions for China ´ s future deep space exploration are made.

Keywords： deep space exploration interplanetary exploration multi-object and multi-mission China Lunar Exploration Program

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Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot coordination systems Research Article

Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1174-1188 doi: 10.1631/FITEE.2100280

Abstract: In this study, a novel (RLTS) with memory in a behavioral control framework is proposed for ; (HMRCSs). Existing HMRCSs suffer from high decision-making time cost and large task tracking errors caused by repeated human intervention, which restricts the autonomy of multi-robot systems (MRSs). Moreover, existing s in the (NSBC) framework need to formulate many priority-switching rules manually, which makes it difficult to realize an optimal behavioral priority adjustment strategy in the case of multiple robots and multiple tasks. The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term memory (LSTM) within the NSBC framework, to achieve an optimal behavioral priority adjustment strategy in the presence of task conflict and to reduce the frequency of human intervention. Specifically, the proposed RLTS with memory begins by memorizing human intervention history when the robot systems are not confident in emergencies, and then reloads the history information when encountering the same situation that has been tackled by humans previously. Simulation results demonstrate the effectiveness of the proposed RLTS. Finally, an experiment using a group of mobile robots subject to external noise and disturbances validates the effectiveness of the proposed RLTS with memory in uncertain real-world environments.

Keywords： Human– multi-robot coordination systems Null-space-based behavioral control Task supervisor Reinforcement learning Knowledge base

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Preference transfer model in collaborative filtering for implicit data Project supported by the National Basic Research Program (973) of China (No. 2012CB316400) and the National Natural Science Foundation of China (No. 61571393) Article

Bin JU,Yun-tao QIAN,Min-chao YE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6, Pages 489-500 doi: 10.1631/FITEE.1500313

Abstract: Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Keywords： Recommender systems Collaborative filtering Preference transfer model Cross domain Implicit data

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Dynamic grouping of heterogeneous agents for exploration and strike missions Research Article

Chen CHEN, Xiaochen WU, Jie CHEN, Panos M. PARDALOS, Shuxin DING,xiaofan@bit.edu.cn,wsygdhrwxc@sina.com,pardalos@ufl.edu

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1, Pages 86-100 doi: 10.1631/FITEE.2000352

Abstract: The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents. This study aims to address the problem of dynamic construction of mission groups under new requirements. Agents are heterogeneous, and a method must dynamically form new groups in circumstances where missions are constantly being explored. In our method, a strategy that combines s and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions. The degree of matching between the mission requirements and the group's capabilities, and the communication cost of are used as indicators to evaluate the quality of the group. The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations. The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

Keywords： Multi-agent Dynamic missions Group formation Heuristic rule Networking overhead

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Three New Missions Head for Mars

Mitch Leslie

Engineering 2020, Volume 6, Issue 12, Pages 1336-1338 doi: 10.1016/j.eng.2020.10.007

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Task planning in robotics: an empirical comparison of PDDL-and ASP-based systems Special Feature on Intelligent Robats

Yu-qian JIANG, Shi-qi ZHANG, Piyush KHANDELWAL, Peter STONE

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3, Pages 363-373 doi: 10.1631/FITEE.1800514

Abstract:

Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.

Keywords： Task planning Robotics Planning domain description language (PDDL) Answer set programming (ASP)

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Mars Helicopter Exceeds Expectations

Mitch Leslie

Engineering 2021, Volume 7, Issue 11, Pages 1511-1512 doi: 10.1016/j.eng.2021.09.003

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Asteroid Missions Begin to Pay Off

Chris Palmer

Engineering 2021, Volume 7, Issue 4, Pages 418-420 doi: 10.1016/j.eng.2021.03.005

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Study of Bus Management of Airborne Electromechanical System

Wang Zhanlin,Qiu Lihua

Strategic Study of CAE 2001, Volume 3, Issue 6, Pages 48-52

Abstract:

There are many electromechanical systems in various vehicles. They are managed in a separate subsystem way. This paper proposes the integrated management scheme which can, by means of the data bus, make the management of subsystem have the abilities of redundancy and tolerance failure, besides accomplishing its own independent functions. The paper emphatically introduces how to realize the integrated management by simulation platform and gives the structures of hardware and software of platform, as well as the strategies of task distribution and scheduling.

Keywords： distributed multiprocessor simulation platform tasks distribution task scheduling redundancy

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Storage hierarchy oriented DPM policy based on task information

Huang Shaomin,Qi Longning,Yang Jun,Hu Chen

Strategic Study of CAE 2010, Volume 12, Issue 2, Pages 83-89

Abstract:

Storage hierarchy oriented DPM, which uses buffer to prolong idle time, can achieve lower power than traditional DPM policies. The paper proposes task information based (TIB) policy for storage hierarchy oriented DPM. TIB subdivides the data access mode of tasks and introduces them into policy by modifying access interface to make prefetching and replacement algorithm more energy aware.

Keywords： data buffer task information DPM prefetching policy

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Meeting deadlines for approximation processing in MapReduce environments Article

Ming-hao HU, Chang-jian WANG, Yu-xing PENG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11, Pages 1754-1772 doi: 10.1631/FITEE.1601056

Abstract: To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today’s production environments. Much effort has been devoted to the problem of meeting deadlines, and typically there exist two kinds of solutions. The first is to allocate appropriate resources to complete the entire job before the specified time limit, where missed deadlines result because of tight deadline constraints or lack of resources; the second is to run a pre-constructed sample based on deadline constraints, which can satisfy the time requirement but fail to maximize the volumes of processed data. In this paper, we propose a deadline-oriented task scheduling approach, named ‘Dart’, to address the above problem. Given a specified deadline and restricted resources, Dart uses an iterative estimation method, which is based on both historical data and job running status to precisely estimate the real-time job completion time. Based on the estimated time, Dart uses an approach–revise algorithm to make dynamic scheduling decisions for meeting deadlines while maximizing the amount of processed data and mitigating stragglers. Dart also efficiently handles task failures and data skew, protecting its performance from being harmed. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 64 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximum volumes of data even with tight deadlines and limited resources.

Keywords： MapReduce Approximation jobs Deadline Task scheduling Straggler mitigation

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Task Arrangement of the IPD Process Plan

Lu Jianxia,Cheng Chengsong,Lan Xiuju,Chen Yong,Xie Liewei

Strategic Study of CAE 2004, Volume 6, Issue 5, Pages 56-60

Abstract:

This paper is related to task arrangement of the IPD process plan. The IPD task feature is discussed in regard to coupling and administrative levels. The stratified and distributing IPD process plan strategy is built. The task arrangement modeling and related Hungary algorithms is given with conception of cost matrix, and then related examples are demonstrated.

Keywords： IPD task arrangement process plan goal decision

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Friendship-aware task planning in mobile crowdsourcing Article

Yuan LIANG,Wei-feng LV,Wen-jun WU,Ke XU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 107-121 doi: 10.1631/FITEE.1601860

Abstract: Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of locationspecific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd workers, while few consider crowd workers arriving in a dynamic manner. Therefore, how to arrange suitable location-specific tasks to a set of crowd workers such that the crowd workers obtain maximum satisfaction when arriving sequentially represents a challenge. To address the limitation of existing approaches, we first identify a more general and useful model that considers not only the arrangement of a set of tasks to a set of crowd workers, but also all the dynamic arrivals of all crowd workers. Then, we present an effective crowd-task model which is applied to offline and online settings, respectively. To solve the problem in an offline setting, we first observe the characteristics of task planning (CTP) and devise a CTP algorithm to solve the problem. We also propose an effective greedy method and integrated simulated annealing (ISA) techniques to improve the algorithm performance. To solve the problem in an online setting, we develop a greedy algorithm for task planning. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments using real and synthetic datasets.

Keywords： Mobile crowdsourcing Task planning Greedy algorithms Simulated annealing