On recommendation problems beyond points of interest

Recommendation systems aim to recommend items or packages of items that are likely to be of interest to users. Previous work on recommendation systems has mostly focused on recommending points of interest (POI), to identify and suggest top-k items or packages that meet selection criteria and satisfy compatibility constraints on items in a package, where the (packages of) items are ranked by their usefulness to the users. As opposed to prior work, this paper investigates two issues beyond POI recommendation that are also important to recommendation systems. When there exist no sufficiently many POI that can be recommended, we propose (1) query relaxation recommendation to help users revise their selection criteria, or (2) adjustment recommendation to guide recommendation systems to modify their item collections, such that the users׳ requirements can be satisfied.We study two related problems, to decide (1) whether the query expressing the selection criteria can be relaxed to a limited extent, and (2) whether we can update a bounded number of items, such that the users can get desired recommendations. We establish the upper and lower bounds of these problems, all matching, for both combined and data complexity, when selection criteria and compatibility constraints are expressed in a variety of query languages, for both item recommendation and package recommendation. To understand where the complexity comes from, we also study the impact of variable sizes of packages, compatibility constraints and selection criteria on the analyses of these problems. Our results indicate that in most cases the complexity bounds of query relaxation and adjustment recommendation are comparable to their counterparts of the basic recommendation problem for testing whether a given set of (resp. packages of) items makes top-k items (resp. packages). In other words, extending recommendation systems with the query relaxation and adjustment recommendation functionalities typically does not incur extra overhead.     

Towards an optimum language data model

Maximizing the satisfaction of a value in an engineering design is usually limited by tradeoffs in which other values become unacceptably sacrificed. In a few cases, however, the maximization is limited by a boundary between what is mathematically possible and what is not. Round wheels, vertical pillars, and binary memory elements are examples of optimum engineering structures which result from such mathematical limits. It is proposed that optimum characteristics of a language data model result similarly by minimizing the variety of primitive data objects, the complexity of those objects, and the number of objects needed to represent data states. Reducing these measures is needed to combine both rich data structure and powerful operations in one language. The minimizations lead to a narrow range of designs for language semantics in which the potential advantages of specialization is small compared with the advantages of commonality. Universal language for support of technical literacy appears to be an appropriate scope of generality in language design.     

基于改进最大熵算法系统传输速率优化研究

研究系统速率优化提高问题。随着用户对系统的传输速率要求越来越高,传统的系统优化传输速率算法由于参数设置难,而造成算法复杂度特别高,使得系统的传输速率降低,为了解决上述问题,在基于误比特率和功率的分析基础上,提出了一种改进的最大熵算法优化系统传输速率方法。方法首先给出系统模型并提出自适应最大熵调制算法理论,然后在发送端和接收端能够完全知道信道状态信息的情况下,给出了最大化传输速率的低复杂度比特和功率分配算法。仿真结果表明,提出的算法可以有效地满足系统地误比特率要求,同时大大降低了系统的计算复杂度,是一种有效地系统传输速率优化方法。     

Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching

Abstract. This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov‐switching parameters. A model selection procedure is proposed which is based on optimization of complexity‐penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments.     

Belief revision within fragments of propositional logic

Belief revision has been extensively studied in the framework of propositional logic, but just recently revision within fragments of propositional logic has gained attention. Hereby it is not only the belief set and the revision formula which are given within a certain language fragment, but also the result of the revision has to be located in the same fragment. So far, research in this direction has been mainly devoted to the Horn fragment of classical logic. Here we present a general approach to define new revision operators derived from known operators, such that the result of the revision remains in the fragment under consideration. Our approach is not limited to the Horn case but applicable to any fragment of propositional logic where the models of the formulas are closed under a Boolean function. Thus we are able to uniformly treat cases as dual Horn, Krom and affine formulas, as well.     

SNAKE(2)算法新的Square攻击

重新评估了分组密码SNAKE(2)算法抵抗Square攻击的能力。指出文献[4]中给出的基于等价结构的错误5轮Square区分器。综合利用算法原结构与其等价结构,给出了一个新的6轮Square区分器。利用新的区分器,对不同轮数的SNAKE(2)算法应用了Square攻击来恢复部分等价密钥信息,7轮、8轮、9轮SNAKE(2)算法的Square攻击时间复杂度分别为212.19、221.59、230.41次加密运算,数据复杂度分别为29、29.59、210选择明文。攻击结果优于文献[4]中给出的Square攻击。     

Call for papers The 34th Chinese Control Conference & SICE Annual Conference 2015

正The 34th Chinese Control Conference and SICE Annual Conference 2015(CCCSICE2015)is organized by the Technical Committee on Control Theory(TCCT)of Chinese Association of Automation(CAA)and the Society of Instrument and Control Engineers(SICE)of Japan,and locally organized by Hangzhou Dianzi University(HDU).     

高校自动排课系统算法比较分析

课程表的编排是高校教务管理中最为重要和复杂的一项工作。通过对几种自动排课算法的合理比较。统筹分析出各自的优劣,得出贪婪算法的综合适用性是最优的结论。在此基础之上．进一步分析贪婪算法是如何逐步解决排课的现实问题,并给出基于贪婪算法的自动排课系统算法的具体实现过程。     

基于边界扫描技术的设计复杂性优化研究

对于边界扫描技术的电路系统,可利用现代优化算法对其进行复杂性和测试性改善度的综合优化。主要以较小的复杂性设计来实现测试性的最大化改善,以提高系统测试性优化和缩短系统的测试时间。应用模拟退火算法和模拟退火遗传算法实现系统的设计复杂性优化,相比于贪婪算法,模拟退火算法和模拟退火遗传算法能得到更好的优化率。     

Greedily computing associative aggregations on sliding windows

We present an algorithm for combining the elements of subsequences of a sequence with an associative operator. The subsequences are given by a sliding window of varying size. Our algorithm is greedy and computes the result with the minimal number of operator applications.