Biblio

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.

As a plethora of wearable devices are being introduced, significant concerns exist on the privacy and security of personal data stored on these devices. Expanding on recent works of using electrocardiogram (ECG) as a modality for biometric authentication, in this work, we investigate the possibility of using personal ECG signals as the individually unique source for physical unclonable function (PUF), which eventually can be used as the key for encryption and decryption engines. We present new signal processing and machine learning algorithms that learn and extract maximally different ECG features for different individuals and minimally different ECG features for the same individual over time. Experimental results with a large 741-subject in-house ECG database show that the distributions of the intra-subject (same person) Hamming distance of extracted ECG features and the inter-subject Hamming distance have minimal overlap. 256-b random numbers generated from the ECG features of 648 (out of 741) subjects pass the NIST randomness tests.