情景感知(context aware)的应用是当前的一个研究热点,但是,由于情景的复杂性和不确定性,如何获取这些应用的需求面临着巨大挑战,需求工程领域出现了大量的研究来解决这一挑战.使用系统文献综述(systematic literature review)的方法首先分析了不同情景维度对需求获取与建模的支持,统计并深入分析情景感知的需求获取与建模中常用的方法,评估了不同经验方法的技术转移成熟度.最后,基于上述结论,给出了情景感知的需求获取与建模下一步的研究方向.
Traditional security framework in cloud platform usually brings self-vulnerability and considerable additional resource consumption. To solve these problems, we propose an external processes monitoring architecture for current popular cloud platform Open Stack with kernel-based virtual machine（KVM）. With this architecture, we can monitor all active processes in online virtual machine（VMs） and scan them for their potential maliciousness in OpenS tack with no agent, and can also detect hidden processes in offline VMs’ memory snapshots and notice the user to decide whether to kill them when VMs become active. Analysis and experimental results show that our architecture is able to reduce consumption of CPU, memory and bandwidth in cloud platform and can detect viruses and hidden processes effectively in VMs.
Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurately detect those hidden processes by analyzing memory data.WVMI dumps in-memory data of the target Windows operating systems from hypervisor and retrieves EPROCESS structures’address of process linked list first,and then generates Data Type Confidence Table（DTCT）.Next,it traverses the memory and identifies the similarities between the nodes in process linked list and the corresponding segments in the memory by utilizing DTCT.Finally,it locates the segments of Windows’EPROCESS and identifies the hidden processes by further comparison.Through extensive experiments,our experiment shows that the WVMI detects the hidden process with high identification rate,and it is independent of different versions of Windows operating system.
Ontology occupies an important position in artificial intelligence,computer linguistics and knowledge management.However,when different ontologies are constructed to represent the same information in a domain,the so-called heterogeneity problem arises.In order to address this problem,a key task is to discover the semantic relationship of entities between given two ontologies,called ontology alignment.Recently,the meta-heuristic algorithms have already been regarded as an effective approach for solving ontology alignment problem.However,firstly,as the ontologies become increasingly large,meta-heuristic algorithms may be easier to find local optimal alignment in large search spaces.Secondly,many existing approaches exploit the population-based meta-heuristic algorithms so that the massive calculation is required.In this paper,an improved compact particle swarm algorithm by using a local search strategy is proposed,called LSCPSOA,to improve the performance of finding more correct correspondences.In LSCPSOA,two update strategies with local search capability are employed to avoid falling into a local optimal alignment.The proposed algorithm has been evaluated on several large ontology data sets and compared with existing ontology alignment methods.The experimental results show that the proposed algorithm can find more correct correspondences and improves the time performance compared with other meta-heuristic algorithms.
Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE.
