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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the current privacy protection methods in mixed attribute data publishing have problems of poor privacy protection effect or poor data utility. This paper proposed a differential privacy mixed attribute data publishing method(DCKPDP) based on improved k-prototype clustering. In order to solve the problem that the traditional k-prototype clustering algorithm did not consider the great influence of different numerical attributes on clustering results, using the information entropy added weight for each numerical attribute. In order to solve the problem of low accuracy of clustering results caused by artificial initial center points or randomly determined by random algorithm, combining the local density and high density of data objects carried out an adaptive selection of initial center points in the process of clustering. In order to solve the problem of high risk of data information leakage, using differential privacy protected the clustering center value. The experimental results show that the DCKPDP algorithm satisfies the requirement of differential privacy protection with less noise and better data availability. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that the zine dust cover handling hole when unload the sherardizing reactor automatically, the single object detection model or method can't detect much well, this paper proposed a special anti-dust-occlusion object detection method with variety of feature model to aid the unloading. Firstly, the depthwise separabile convolution was a substitute for the traditional convolution in Tiny-YOLOv3. It also adjusted the loss function to adapt the new training. Secondly, it judged occlusion of target area by pixel statistics and estimated current frame with the trajectory features and shape features. Finally, the prediction results optimized the results of object detection network with fusion rules. The results show that the improved fusion model perform best with the severe and prolonged occlusion on handling hole, the AP and AIOU reach 99.38% and 88.14% respectively. Furthermore, it's a real-time method, achieving the dynamic detection of the handling hole with serious dust occlusion effectively. [ABSTRACT FROM AUTHOR]

Location-based service (LBS) has brought convenience to users' lives, but there is also a risk of disclosure of location track privacy. How to protect these private data has become a research hotspot. This paper introduced the system architecture of existing privacy protection methods, summarized the location privacy protection technology and trajectory privacy protection technology, classify expound the privacy protection methods based on distortion technology, encryption technology, anonymity technology and differential privacy technology and sorted out the latest algorithms under various privacy protection technologies. According to the understanding of the existing technologies, it put forward the shortcomings in the present research and the future research direction. [ABSTRACT FROM AUTHOR]

Aiming al the problem of flow rules tampering attack as well as single point vulnerability in SDN control layer,this paper proposed a mimic security controller model based on dynamic heterogeneous redundancy. The proposed model used distributed architecture and consistency decision mechanism to improve the system reliability, scheduling mechanism to achieve system dynamics and the combination of heterogeneous factors to improve system security. It designed a consistency fault-tolerant algorithm CFDA to achieve safe data processing with fault detection and scalability. Simulation experiments show that the proposed algorithm has lower communication complexity for the controller cluster network and reduces the multi-controller node redundancy requirements,and that the mimic security controller mechanism can effectively improve the attacker’s difficulty, which increases the safety factor and reliability of the system. [ABSTRACT FROM AUTHOR]

Shilling attacks pose a significant threat to the security of collaborative filtering recommender systems. It has come to be an important task to develop the attack-resistant techniques for robust collaborative recommendation. However, traditional collaborative filtering algorithms have weakness in the balance between stability and predictive accuracy. To address this problem, this paper proposed user reliability and improved the calculation of user similarity, and incorporated both user reliability and similarity into standard collaborative filtering framework. Experiments show that the proposed algorithm performs better than state-of-the-art recommender algorithms in stability and predictive accuracy. [ABSTRACT FROM AUTHOR]

This paper focused on noun phrases event resolution. It improved the semantic features by adding the elements of time and address when computing semantic similarity of tuples (semantic roles information). Experiments on the English portion of OnloNoles 4.0 show that the semantic roles information (Argm-Log, Argm-Tmp) can significantly boost the performance of the baseline for event noun phrases resolution. It outperforms the baseline system by 0.49% in precision and 0.2% in F-measure. Consequently, it proves Argm-Log and Argm-Tmp can improve the event noun phrases resolution. [ABSTRACT FROM AUTHOR]

A collaborative recommendation algorithm based on KNN didn't take in count the neighbours that blindly followed others, ideas, which made the neighbours useless to predicte the rates of the target users to the unknown items. To address this problem, this paper put forward the contribution factor, thought over the non-common evaluation items, considered the contribution of neighbour recommendation, and selected the neighbours with the traditional similarity. And it recalculated the neighbour prediction weights to the unknown projects to advance the performance of recommendation. The experiments show that this algorithm improves the degree of accuracy. [ABSTRACT FROM AUTHOR]