A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Multi-Agent模式下的城市暴雨内涝应急决策方法研究

为厘清应对暴雨内涝灾害动态决策过程中决策主体、决策、决策方案等决策要素间的不确定关系,提出1种多主体(Multi-Agent)和贝叶斯决策网络(BDN)相结合的应急决策方法。首先分阶段构建“主体-任务”可视化网络,分析暴雨内涝灾害各应急阶段的主要任务和参与的决策主体;在考虑到决策要素间的动态不确定性可能造成决策风险的前提下,运用Multi-Agent和BDN方法探究各决策要素间的影响关系,以便进行方案集优选。研究结果表明:该方法具有实用性和现实意义,研究结果可为城市暴雨内涝灾害的应急决策提供理论参考。

Multi-tasking to Address Diversity in Language Learning

With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and developing learning strategies to enable him to make sense of and use of the target language appropriately in varied contexts and with different uses of the language. To attain this, the teacher is tasked with designing, monitoring and processing language learning activities for students to carry out and in the process learn by doing and reflecting on the learning process they went through as they interacted socially with each other. This paper describes a task named"The Fishbowl Technique"and found to be effective in large ESL classes in the secondary level in the Philippines.

基于改进NSGA-Ⅲ的D2D协同MEC多目标优化研究

在当前的移动边缘计算(Mobile Edge Computing,MEC)模型中,由于任务是直接上传到MEC服务器执行,存在边缘服务器的计算压力大、空闲移动设备上的资源未得到充分利用等问题。使用边缘网络中的空闲设备进行协同计算,能够实现用户闲置资源的合理利用,增强MEC的计算能力。因此,提出了一种利用终端直通(Device-to-Device,D2D)进行协同计算的部分卸载MEC模型(D2D Collaborative MEC for Partial Offloading,DCM-PO)。在该模型中,除本地计算和MEC服务器计算外,还能将部分任务上传到空闲D2D设备进行辅助计算。首先,以最小化边缘网络的时延、能耗和费用为目标建立多目标优化问题。然后,在多染色体混合编码、自适应交叉率和变异率等方面对基于参考点的非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅢ,NSGA-Ⅲ)进行改进,使之适合DCM-PO模型中的多目标优化问题求解。最后,仿真结果表明,相比基准MEC模型,DCM-PO模型在多项性能指标上有明显优势。

自动驾驶环境感知多任务去耦-融合算法

自动驾驶车辆在行驶过程中,需要对行人和车辆同时完成目标检测、实例分割和目标跟踪三个任务。提出一种基于深度学习的环境感知模型同时对三个任务进行多任务学习。首先,通过卷积神经网络对连续帧图像提取时空特征;然后,通过注意力机制对时空特征进行去耦再融合,充分利用任务间的相关性,实现不同任务对时空特征的差异化选择;最后,为平衡不同任务间的学习速率,使用动态加权平均的方式对模型进行训练。在KITTI数据集上的实验结果表明,所提模型在目标检测方面,比CenterTrack模型F1得分提高了0.6个百分点;在目标跟踪方面,比TraDeS(Track to Detect and Segment)模型多目标跟踪精度(MOTA)提高了0.7个百分点;在实例分割方面,比SOLOv2(Segmenting Objects by LOcations version 2)模型AP_(50)和AP_(75)分别提高了7.4和3.9个百分点。

Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning

代理人联盟是代理人协作和合作的一种重要方式。形成一个联盟，代理人能提高他们的能力解决问题并且获得更多的实用程序。在这份报纸，新奇多工联盟平行形成策略被介绍，并且多工联盟形成的过程是一个 Markov 决定过程的结论理论上被证明。而且，学习的加强被用来解决多工联盟平行的代理人行为策略，和这个过程形成被描述。在多工面向的领域，策略罐头有效地并且平行形式多工联盟。

A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning

经由联盟形成的任务分配是在多代理人系统(妈) 的几应用程序域的基本研究挑战，例如资源分配，灾难反应管理等等。怎么以一种分布式的方式分配许多未解决的任务到一些代理人，主要处理。在这篇论文，我们在自我组织、自我学习的代理人之中建议一个分布式的平行多工分配算法。处理状况，我们在二维的房间地理上驱散代理人和任务，然后介绍为寻找它的任务由的一个单个代理人的分享学习的利润(PSL ) 不断自我学习。我们也在代理人之中为通讯和协商介绍策略分配真实工作量到每个 tasked 代理人。最后，评估建议算法的有效性，我们把它与 Shehory 和 Krau 被许多研究人员在最近的年里讨论的分布式的任务分配算法作比较。试验性的结果证明建议算法罐头快速为每项任务形成一个解决的联盟。而且，建议算法罐头明确地告诉我们每个 tasked 代理人的真实工作量，并且能因此为实际控制任务提供一本特定、重要的参考书。

基于带阈值的BPE-dropout多任务学习的端到端语音识别

针对语音识别任务中出现的未登录词问题,提出一种带阈值的BPE-dropout多任务学习语音识别方法.该方法采用带随机性的字节对编码算法,在形成子词时引入带字数阈值的策略,将子词作为建模单元,编码器部分采用Conformer结构,与链接时序分类和注意力机制相结合.为进一步提升模型性能,引入动态参数对损失函数进行动态调节,并同时进行多任务训练和解码.实验结果表明,该方法采用子词作为建模单元可有效解决未登录词问题,在多任务学习框架下进一步提升了模型的识别性能.在公开数据集THCHS30和ST-CMDS上,该模型实现了超过95%的识别准确率.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Face Detection Detection, Alignment Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks

This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.

一种多任务学习结合U-Net的微动脉瘤图像分割方法

微动脉瘤是检测糖尿病初期视网膜病变的关键病灶,针对微动脉瘤图像分割问题,提出了多种图像预处理技术与多任务学习网络相结合的方法。该方法使用了多种图像预处理技术,在UNet中引入了注意力机制,并将微动脉瘤图像分割作为主任务,微动脉瘤存在性检测作为副任务,利用多任务学习结合U-Net来提升主任务分割效果。在国际公开数据集上进行实验,获得了AUC为9.48%以及AP为51.40%的结果,与单任务图像分割相比,AP值提升了3.82%,由实验结果可知该方法能够提升微动脉瘤的分割效果。

Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

基于特征筛选的综合能源系统多元负荷日前-日内预测

负荷预测是指导综合能源系统调度与运行的前提。为更加经济高效地实施系统日前计划、日内优化,提出一种基于特征筛选的多元负荷日前-日内预测方法。首先,结合特征工程中3类特征筛选方法筛选预测模型输入特征,简化模型的同时能够保存下最重要的特征,针对日前-日内预测策略分别确立输入特征集;然后通过多任务学习硬共享机制,采用长短期记忆神经网络建立预测模型,实现不同子任务信息共享,并通过随机搜索方法优化网络参数以提高预测精度;最后以北京某产业园区供暖季电、热负荷为案例进行分析,日前、日内预测综合精度分别达到91.3%和95.2%。分析结果表明,该预测方法能够为系统日前调度和日内运行优化提供良好支撑,且预测结果优于未经特征筛选预测和单独负荷预测,证明了该预测方法具有更高的预测精度。

Algorithm Design of CPCI Backboard's Interrupts Management Based on VxWorks'Multi-Tasks

This paper begins with a brief introduction of the embedded real-time operating system VxWorks and CompactPCI standard, then gives the programming interfaces of Peripheral Controller Interface （PCI） configuring, interrupts handling and multi-tasks programming interface under VxWorks, and then emphasis is placed on the software frameworks of CPCI interrupt management based on multi-tasks. This method is sound in design and easy to adapt, ensures that all possible interrupts are handled in time, which makes it suitable for data acquisition systems with multi-channels, a high data rate, and hard real-time high energy physics.

Multi-robot task allocation for exploration

The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.

基于MSFA-Net的车辆及车道线检测算法

车辆检测与车道线分割是自动驾驶感知系统的重要组成部分,其基本要求是具有高精度和实时性。鉴此提出一种双任务多尺度特征聚合网络(MSFA-Net),该网络由1个特征提取网络和2个检测分支网络构成,实现了车辆和车道线同时检测。首先使用E-ELAN网络构造共享主干特征网络;在车辆检测分支网络设计增强卷积模块(CBS+)进行自下而上的特征融合以提升精度;在车道线检测分支网络使用特征融合模块(FeatFuse)对多分辨率特征进行自适应加权融合,配合空洞卷积语义感知模块(CDBS)使用梯形结构的多空洞值卷积对融合特征进行采样,以提升不连续车道线及其他非线性车道的分割精度。结果表明:在BDD100K数据集上,该文网络MSFA-Net其平均精度均值、召回率、像素准确率分别达到了81.3%、90.1%和80.1%,检测帧率达到了41.6帧/s,能较好适应真实行车环境的需求。

Genetic Algorithm Based Combinatorial Auction Method for Multi-Robot Task Allocation

An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.

基于GSLF-SSA的异构多核处理器任务调度

为了提高异构多核处理器平台的计算性能,从任务调度的角度出发,提出了一种使用黄金正弦和莱维飞行机制改进的麻雀搜索算法(Fusion of Golden Sinusoidal and Levy Flight in Sparrow Search Algorithm,GSLF-SSA)来优化异构多核处理器的任务调度。通过对异构任务调度的分析,将异构任务建模为DAG(Directed Acyclic Graph)任务模型,通过对其优先级进行随机编码分配,实现了GSLF-SSA算法求解域从连续到离散的映射,使该算法更能适用于异构多核任务调度之中。将DAG任务的最优调度长度作为算法的适应度值进行迭代寻优,通过与目前应用广泛的麻雀搜索算法(SSA)、混合式任务调度算法(IHSSA)、人工蜂群算法(ABC)等多种启发式算法在异构任务调度环境下的实验对比表明,GSLF-SSA能获得更优的调度长度与更短的调度执行时间。