New State Recovery Attacks on the Grain v1 Stream Cipher

The Grain v1 stream cipher is one of the seven finalists in the final e STREAM portfolio. Though many attacks have been published,no recovery attack better than exhaustive key search on full Grain v1 in the single key setting has been found yet. In this paper,new state recovery attacks on Grain v1 utilizing the weak normality order of the employed keystream output function in the cipher are proposed. These attacks have remarkable advantages in the offline time,online time and memory complexities,which are all better than exhaustive key search. The success probability of each new attack is 0.632. The proposed attack primarily depends on the order of weak normality of the employed keystream output function. This shows that the weak normality order should be carefully considered when designing the keystream output functions of Grain-like stream ciphers.

Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering

In social tagging systems, users are allowed to label resources with tags, and thus the system builds a personalized tag vocabulary for every user based on their distinct preferences. In order to make the best of the personalized characteristic of users’ tagging behavior, firstly the transfer matrix is used in this paper, and the tag distributions of query resources are mapped to users’ query before the recommendation. Meanwhile, we find that only considering the user’s preference model, the method cannot recommend new tags for users. So we utilize the thought of collaborative filtering, and produce the recommend tags based on the query user and his/her nearest neighbors' preference models. The experiments conducted on the Delicious corpus show that our method combining transfer matrix with collaborative filtering produces better recommendation results.

User Preferences-Based and Time-Sensitive Location Recommendation Using Check-In Data

Location-based social networks have attracted increasing users in recent years. Human movements and mobility patterns have a high degree of freedom and provide us with a lot of trajectory to understand the activity of users. In this paper, we present?a user preferences and time sensitive recommender systems that offer an appropriate venue for a user when he appears in a special time at a particular location. The system considering the factors are: 1) the popularity of a location;2) the preferences of a user;3) social influence of the friends of the user and the friends who are check-in at the same location with the user;and 4) the time feature of the location and the user visiting. We evaluate our system with a large-scale real dataset from a location-based social network of Gowalla. The results confirm that our method provides more accurate location recommendations compared to the baseline.

Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine

Nitrogen(N)is an important mineral element in apple production.Rapid estimation of apple tree N status is helpful for achieving precise N management.The objective of this work was to explore partial least squares(PLS)regression in dimensional reduction of spectral data and build the diagnostic model.The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer,and leaf total N content was obtained at the same time.The raw spectra were pretreated using Savitzky-Golay(SG)smoothing and a combination of SG and first-order derivative(SG_FD)or second-order derivative(SG_SD).The samples were divided into a calibration dataset and a prediction dataset using SPXY.Based on 4 factors of PLS regression,including latent variables(LVs),X-loading,variable importance in projection(VIP)and regression coefficients(RC),the 6 methods(LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02)were derived and used for variable extraction,based on which PLS model and ELM model were established.The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis.The amounts of variables extracted by LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02 were 6,11,18,305,26 and 88,respectively.The method of extracting variables with an RC threshold based on the minimum RMSEP(RC_02)could effectively avoid the omission of effective information.The RC_02 method was recommended for related research which required accurate wavelength information as a variable.The variable extraction method based on LVs generated an ELM model with a simple structure.The prediction results showed that the ELM model outperformed the PLS model.The PLS(LVs)_ELM model was the best;R2P,RMSEP and RPD were 0.837,2.393 and 2.220,respectively.