Recommender systems (RS) exploit user ratings on items and side information to make personalized recommendations. In order to recommend the right products to users, RS must accurately model the implicit preferences of each user and the properties of each product. In reality, both user preferences and item properties are changing dynamically over time, so treating the historical decisions of a user or the received comments of an item as static is inappropriate. Besides, the review text accompanied with a rating score can help us to understand why a user likes or dislikes an item, so temporal dynamics and text information in reviews are important side information for recommender systems. Moreover, compared with the large number of available items, the number of items a user can buy is very limited, which is called the sparsity problem. In order to solve this problem, utilizing item correlation provides a promising solution. Although famous methods like TimeSVD++, TopicMF and CoFactor partially take temporal dynamics, reviews and correlation into consideration, none of them combine these information together for accurate recommendation. Therefore, in this paper we propose a novel combined model called TmRevCo which is based on matrix factorization. Our model combines the dynamic user factor of TimeSVD++ with the hidden topic of each review text mined by the topic model of TopicMF through a new transformation function. Meanwhile, to support our five-scoring datasets, we use a more appropriate item correlation measure in CoFactor and associate the item factors of CoFactor with that of matrix factorization. Our model comprehensively combines the temporal dynamics, review information and item correlation simultaneously. Experimental results on three real-world datasets show that our proposed model leads to significant improvement compared with the baseline methods.

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations.However, for the new items with no historical rating records,the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation,we further bring temporal dynamics,text and relevance in our models to release item cold start.Two new cold start recommendation models TmTx(Time,Text) and TmTI(Time,Text,Item correlation) proposed to solve the item cold start problem for different cold start scenarios.While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics,comments,and item correlations into consideration to solve the cold start problem but none of them combines these information together.Two models proposed in this paper fused features such as time,text,and relevance can effectively improve the performance under item cold start.We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items.Both proposed models can effectively improve the performance with item cold start.Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods.

In this paper, we address the problem of scheduling operations into control steps with a dual threshold voltage (dual-Vth) technique, under timing and resource constraints. We present a two-stage algorithm for leakage power optimization. In the threshold voltage (Vth) assignment stage, the proposed algorithm first initializes all the operations to high-Vth, and then it iteratively shortens the critical path delay by reassigning the set of operations covering all the critical paths to low-Vth until the timing constraint is met. In the scheduling stage, a modified force-directed scheduling is implemented to schedule operations and to adjust threshold voltage assignments with a consideration of the resource constraints. To eliminate the potential resource constraint violations, the operations' threshold voltage adjustment problem is formulated as a “weighted interval scheduling” problem. The experimental results show that our proposed method performs better in both running time and leakage power reduction compared with MWIS [3].

Scheduling is a key problem in high level synthesis, as the scheduling results affect most of the important design metrics. In this paper, we propose a novel scheduling method to simultaneously optimize the leakage power of functional units with dual-Vth techniques and the number of registers under given timing and resource constraints. The mobility overlaps between operations are removed to eliminate data dependencies, and a simulated-annealing-based method is introduced to explore the mobility overlap removal solution space. Given the overlap-free mobilities, the resource usage and register usage in each control step can be accurately estimated. Meanwhile, operations are scheduled so as to optimize the leakage power of functional units with minimal number of registers. Then, a set of operations is iteratively selected, reassigned as low-Vth, and rescheduled until the resource constraints are all satisfied. Experimental results show the efficiency of the proposed algorithm.

Bent functions have been applied to cryptography, spread spectrum, coding theory, and combinatorial design. Permutations play an important role in the design of cryptographic transformations such as block ciphers, hash functions and stream ciphers. By using the Kronecker product this paper presents a general recursive construction method of permutations over finite field. As applications of our method, several infinite classes of permutations are obtained. By means of the permutations obtained and M-M functions we construct several infinite families of bent functions.

In a sector of a single cell, due to the fading characteristic of wireless channels, several decode-and-forward relay stations are deployed to form a two-hop relay-assisted multicast system. We propose two schemes for the system, the first scheme combines the use of space-time code and distributed space-time code (DSTC), and the second one combines the use of DSTC and maximum ratio combining. We give an outage probability analysis for both of them. Based on this analysis, we manage to maximize the spectral efficiency under a preset outage probability confinement by finding out the optimal power allocation and relay location strategies. We use genetic algorithms to verify our analysis and numerical results show that the schemes proposed by us significantly outperform the scheme in previous work. We also show the effect of path loss exponent on the optimal strategy.