教学课程资源库 更多>>

关键词： 计算机科学技术   发展现状   发展趋势  

摘要： 随着经济和社会的不断发展,也同事促进了信息技术的发展进步。信息技术已经成为生产和生活的重要组成部分。它不仅提高了人民的生活水平和生产力,而且是一个国家综合实力的重要表现。因此,研究人员应探讨和研究信息技术的发展和进一步发展。鉴于这种情况,本文先后分析了信息和通信技术发展的现状和趋势,以作为研究人员提供信息。

关键词： Training   Cameras   Computer science   Probes   Extraterrestrial measurements   Adaptation models   Person re-identification   view adaptation   adversarial learning   entropy regularization  

摘要： Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognize paired snapshots, it has to cope with large cross-view variations caused by the camera view shift. The supervised deep neural networks are effective in producing a set of non-linear projections that can transform cross-view images into a common feature space. However, they typically impose a symmetric architecture, leaving the network ill-conditioned on its optimization. In this paper, we learn view-invariant subspace for person re-ID, and its corresponding similarity metric using an adversarial view adaptation approach. The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimizing the cross-entropy view confusion objective. To determine the similarity value, the network is empowered with a similarity discriminator to promote features that are highly discriminant in distinguishing positive and negative pairs. The other contribution includes an adaptive weighing on the most difficult samples to address the imbalance of within-/between-identity pairs. Our approach achieves notably improved performance in comparison with the state-of-the-arts on benchmark datasets.

关键词： Control systems   Software   Protocols   Network architecture   Security   Computer science   Sparks   SDN   Spark   network management   data plane properties verification   packet reachability verification  

摘要： Software-Defined Networking (SDN) is an innovational network architecture which gives network administrators the ability to directly control the whole network by programming on a centralized controller. Due to network complexity, networks are unlikely to be bug-free. The ability to verify data plane properties will make network management easier for network administrators in SDN. In this paper, we present a novel atomic predicates based data plane properties verification method for SDN using Spark which is a big data processing framework. First, we verify packet reachability which is a fundamental data plane property. Then, we verify other data plane properties such as loop-freedom and nonexistence of black holes. In addition, the proposed method can detect a security threat existing in SDN called firewall bypass threat with packet reachability verification. By adopting atomic predicates, we achieve less computational and storage overhead. We implement the methods and study the performance. The results of experiments show that we can efficiently and accurately detect loops, black holes and firewall bypass threats.

关键词： Image segmentation   Head   Training   Manifolds   Semantics   Computer science   Neural networks   Fully convolutional network (FCN)   hierarchical models   semisupervised learning  

摘要： This article presents a semisupervised multilabel fully convolutional network (FCN) for hierarchical object parsing of images. We consider each object part (e.g., eye and head) as a class label and learn to assign every image pixel to multiple coherent part labels. Different from previous methods that consider part labels as independent classes, our method explicitly models the internal relationships between object parts, e.g., that a pixel highly scored for eyes should be highly scored for heads as well. Such relationships directly reflect the structure of the semantic space and thus should be respected while learning the deep representation. We achieve this objective by introducing a multilabel softmax loss function over both labeled and unlabeled images and regularizing it with two pairwise ranking constraints. The first constraint is based on a manifold assumption that image pixels being visually and spatially close to each other should be collaboratively classified as the same part label. The other constraint is used to enforce that no pixel receives significant scores from more than one label that are semantically conflicting with each other. The proposed loss function is differentiable with respect to network parameters and hence can be optimized by standard stochastic gradient methods. We evaluate the proposed method on two public image data sets for hierarchical object parsing and compare it with the alternative parsing methods. Extensive comparisons showed that our method can achieve state-of-the-art performance while using 50% less labeled training samples than the alternatives.

关键词： Games   Training   Tools   Computer security   Computer science   Computer networking education   cyber education   serious games  

摘要： Cyberspace odyssey (CSO) is a novel serious game supporting computer networking education by engaging students in a race to successfully perform various cybersecurity tasks in order to collect clues and solve a puzzle in virtual near-Earth three-dimensional space. Each team interacts with the game server through a dedicated client presenting a multimodal interface, using a game controller for navigation and various desktop computer networking tools of the trade for cybersecurity tasks on the game's physical network. Specifically, teams connect to wireless access points, use packet monitors to intercept network traffic, decrypt and reverse engineer that traffic, craft well-formed and meaningful responses, and transmit those responses. Successful completion of these physical network actions to solve a sequence of increasingly complex problems is necessary to progress through the virtual story-driven adventure. Use of the networking tools reinforces networking theory and offers hands-on practical training requisite for today's cyber operators. This article presents the learning outcomes targeted by a classroom intervention based on CSO, the design and implementation of the game, a pedagogical overview of the overall intervention, and four years of quantitative and qualitative data assessing its effectiveness.

关键词： animal liberation   children   choices   early education   environmental destruction   nonhuman animal welfare  

摘要： This article examined reasons why information pertaining to nonhuman animal welfare and liberation should be introduced during childhood. Studies indicate that animal-welfare activists' and abolitionists' efforts to date may be insufficient given the pervasive environmental destruction and ongoing animal suffering. Moreover, research reveals that education related to animal welfare and liberation is systematically excluded from children's education, and they thus remain unaware of the sources and associated health hazards of meat they consume. Conversely, children's knowledge about animal welfare increases when exposed to literature on the topic, which enables them to make informed choices regarding meat consumption. This paper draws on animal-welfare and liberation literature to argue that augmenting children's knowledge about animal welfare and liberation can foster children's understanding, language, philosophy, and ability to make informed choices about their relationship with animals and the environment in general.

关键词： Optimization   Adaptation models   Computational modeling   Computer science   Topology   Sociology   Statistics   Distributed evolutionary algorithms   elite-guided learning (EGL)   high-dimensional problems   large-scale optimization   particle swarm optimization (PSO)  

摘要： Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods;2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases;and 3) preserve a good scalability to solve higher dimensional problems.

ISBN: (纸本)9787115535122