Difficulty in parking leads to many issues, such as traffic congestion, and hinders the development of intelligent transportation systems. One significant reason is that the cruise parking does not fully utilize real-time information about the parking lot (e.g., availability status of the parking spaces), resulting in low parking efficiency and high parking costs. Even though reservation parking improves parking efficiency, its reservation service is coarse (e.g., could not reserve a specific parking space). Therefore, to achieve more efficient parking and optimize existing reservation services, we propose a real-time parking space view (PSV) framework (called PRPSV). PSV can reflect the position distribution and availability status of each parking space in a parking lot, which enables drivers to quickly and efficiently obtain real-time parking information and complete parking decisions. However, there have been fewer studies on PSV in recent years, and these methods are high-cost, limited scalability, and do not use PSV to optimize parking efficiency and services. Therefore, we propose a method to construct and update PSV accurately. Further, we model cruise and reservation modes in non-PSV and PSV-based scenarios to compare and analyze the impact of PSV on parking efficiency. Finally, the comprehensive qualitative comparison with related work demonstrates the innovativeness of PRPSV and the sufficient experimental results and a case study in an actual parking lot show that PRPSV can efficiently and accurately construct and update PSV, and the introduction of PSV can effectively improve parking efficiency and optimize reservation services.

Software vulnerabilities detection is crucial in cyber security which protects the software systems from malicious attacks. The majority of earlier techniques relied on security professionals to provide software features before training a classification or regression model on the features to find vulnerabilities. However, defining software features and collecting high-quality labeled vulnerabilities for training are both time consuming. To handle these issues, in this paper, we propose an unsupervised and effective method for extracting software features and detecting software vulnerabilities automatically. Firstly, we obtain software features and build a new pre-trained BERT model through constructing C/C++ vocabulary and pre-training on software source code. We then fine-tune the pre-trained BERT model with a deep autoencoder and create low-dimensional embedding from the software features. We finally apply a clustering-based outlier detection method on the embedding to detect vulnerabilities. We evaluate our method on five datasets with programs written in C/C++, experimental results show that our method outperforms state-of-the-art software vulnerability detection methods.

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs.

Collaborative business processes gather a set of business processes with complementary competencies and knowledge to cooperate to achieve more business successes. To ensure their successful implementation, correctness is a key issue that needs to be addressed during their development. To this end, a novel correctness enforcement approach is proposed to support the development of collaborative business processes. In this approach, we first give an algorithm to check the correctness of an original process specified by Petri nets. Then, we prune its reachability graph to obtain its core in case of partially correct, which is a reduced reachability graph that doesn't cover invalid states. Finally, we generate a set of controllers from the core using coordination mapping (i.e., inserting some coordination activities into controllers), and then an enforced process is built by the composition of the original process and the controllers. Our approach is implemented as an analysis module called cetool in the PIPE (Platform Independent Petri Net Editor) and it is validated on a set of real-world cases. The results show the effectiveness and efficiency of the proposed approach.

Software evolution process model (EPM) is created in terms of a formal evolution process meta-model (EPMM) and semi-formal approach to modeling based on EPMM [1]. In order to better manage and control the software evolution process and make the best of existing software technology, the method to transform any EPM to its execution model based logic programming has been proposed. Completeness of conversion depends on completeness of the rules, that is, all the expressions of the original model are found the correspondence in the target model. Since transformation rules are proposed based on precondition or post-condition types of activities in anyone EPM, this need to prove that activity type set in anyone EPM is completeness set. To this end, the precondition and post-condition of activities in EPM are classified based on analyzing all expressions in EPMs and the semantics of the activity execution. Type completeness set of activity’s precondition and its post-condition is presented. Lastly we prove that the activity type set in anyone EPM is completeness set by mathematical induction.