Privacy-preserving multi-criteria collaborative filtering

Privacy-preserving collaborative filtering schemes focus on eliminating the privacy threats inherent in single preference values, and the privacy risks in the multi-criteria preference domain are disregarded. In this work, we introduce randomized perturbation-based privacy-preserving approaches for multi-criteria collaborative filtering systems. Initially, the privacy protection methods efficiently used in traditional single-criterion systems are adapted onto multi-criteria ratings. However, these systems require intelligent protection mechanisms that are flexible and adapting to the structure of each sub-criterion. To achieve such a goal, we introduce a novel privacy-preserving protocol by adapting an entropy-based randomness determination procedure that can recover accuracy losses. The proposed protocol adjusts privacy-controlling parameters concerning the information inherent in each criterion. We experimentally evaluate the proposed schemes on three subsets of Yahoo!Movies multi-criteria preference dataset to demonstrate the effects of the proposed privacy-preserving schemes on both user privacy levels and prediction accuracy for differing sparsity rates. According to the obtained experimental outcomes, the proposed entropy-based privacy-preserving scheme can produce significantly more accurate predictions while maintaining an identical level of privacy provided by the traditional privacy protection scenario. The experimental results also confirm that the novel entropy-based privacy-preserving scheme maintains the confidentiality of personal preferences without severely compromising prediction accuracy.     

Hypersensitizing commercial panchromatic plates

Since ammonia, when added to the dye bath in preparing bathed plates, increases the sensitizing action of the dye, its action on commercial plates was investigated.In the course of the work three methods of sensitometry were used. Of these, one was used as a first qualitative test; the second, the spectrograph method, was used to study the effect of the ammonia on the sensitivity of the plate to each wave-length; the third, the Hurter and Driffield method, gives the absolute value of the speed of the plate.It was found that by bathing commercial panchromatic plates in a solution of 25 c.c. ethyl-alcohol, 75 c.c. water, and 3 c.c. of strong ammonia water (20 per cent. NH₃) for four minutes at 18° C. and drying rapidly, the speed to white light is increased 100 per cent. in nearly all cases, and the sensitivity in the red. extended one hundred or more Ångström units. The speed in the red is increased, in many cases, 400 per cent. If the plates be bathed without the alcohol (100 c.c. water, $\text{3}\text{1}\text{2}$ c.c. ammonia water) the speed is still more increased, but the plates should be used immediately after drying.Ordinary plates do not have their sensitivity appreciably changed. Most brands of orthochromatic plates are not improved, although one, the Cramer Trichromatic, showed the same increase as the panchromatic plate.     

Aperiodically intermittent-based filtering in sensor networks

A filtering algorithm with a control strategy is proposed to estimate the target state. Unlike the existing literature, the control strategy adopted is based on asynchronous aperiodically intermittent control, which is more general and universal due to the control strategy of each network node being different. Moreover, a Lyapunov function is proposed, which can handle asynchrony for asynchronous aperiodically intermittent control and simplify the proof process. Using the Lyapunov method and the graph theory, we give sufficient conditions to obtain a criterion of exponentially bounded in the mean square about the filtering algorithm and target. Finally, two numerical examples are given to verify the effectiveness of our results.     

基于共同评分的协同过滤算法

协同过滤是目前电子商务推荐系统中使用最广泛最成功的一种个性化推荐算法.受数据稀疏性影响,传统协同过滤算法在较小共同评分项集上计算出的相似度不能准确反映用户间的相似关系,严重影响了推荐系统的精度.针对该问题,在分析共同评分分布及其与相似度关系的基础上,提出了基于共同评分的协同过滤算法,无须计算相似度,直接将共同评分作为最近邻选择标准.MovieLens实验表明该算法能明显提高预测结果的准确性和覆盖率.     

HEVC中滤波算法研究与优化

在视频编码过程中,为了消除图像的块效应,视频编码标准H．264中采用了去块效应滤波算法．但是没有考虑到原始图像和重建图像之间的相关性,在HEVC中提出了基于块与四叉树的环路滤波算法。该算法用于去块效应滤波。文章提出一种最优对称性的快速自适应环路滤波算法,保证重建图像质量的同时,减少编码时间。     

Diffusion normalized Huber adaptive filtering algorithm

In this paper, we propose an adapt-then-combine (ATC) diffusion normalized Huber adaptive filtering (ATC-DNHuber) algorithm for distributed estimation in impulsive interference environments. Firstly, a normalized Huber adaptive filter (NHuber) is developed to reduce the effect of the eigenvalue spread of the input signal. Then we extend the NHuber to develop an ATC diffusion algorithm by applying the NHuber algorithm at all agents. In addition, the mean stability performance and computational complexity are analyzed theoretically. In addition, Furthermore, simulation results demonstrate that the ATC-DNHuber algorithm can perform better in identifying the unknown coefficients under the complex and changeable impulsive interference environments.     

Bias in algorithmic filtering and personalization

Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.