知识搜索引擎与翻译

信息化进程的纵深发展对翻译行业产生了深刻的影响,译者如何从海量信息中高效获取信息关系到其翻译服务的质量。因此,译者的信息素养和搜索能力显得愈发重要。通过探讨搜索引擎的补充和延伸——知识搜索引擎,着重论述知识搜索引擎相对于传统搜索引擎,在主题分类、一站式知识检索、互动式知识问答、个性化知识库等方面具备的突出优势,并对比国内三大知识搜索引擎,论述其在翻译实践中的重要启示。     

一种求解双目标flow shop排序问题的进化算法

提出一种求解双目标flow shop排序的递进多目标进化算法.算法采用改进的精英复制策略,在实现精英保留的前提下降低了计算复杂性;通过递进进化模式增加群体多样性,改善了算法收敛性;通过群体进化过程中对非劣解集进行竞争型可变邻域启发式搜索,增强了算法局部搜索性能.采用新算法和参照算法NSGA-II对31个标准双目标flow shop算例进行优化.研究结果表明,新算法在所有算例的求解中均获得了优于NSGA-II的非劣解集,验证了算法的有效性.     

工作搜索行为研究的回顾和展望

工作搜索行为是个体付出努力和时间,获取潜在的工作信息的有目的的意志性行为模式.关于工作搜索行为的结构有成分说、阶段说和维度说.影响工作搜索行为的因素主要有个体的自尊、自我效能感、工作搜索动机以及人格特质等,而个体的工作搜索行为又会影响到个体的就业情况和就业质量.未来的研究可以从以下方面做出努力:对不同的亚群体分别进行具体研究;使多种研究方法相互交叉来补充以往研究的不足;进一步拓展研究领域、发展工作搜索行为的理论以及建构完整的模型;在应用领域中进一步研究工作搜索行为的干预与培训措施的有效性.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

The triggers of local and distant search: Relative magnitude and persistence in explaining acquisition relatedness

Research on problemistic search has assumed negative attainment discrepancy to be the trigger of both local and distant search. Extending this research, we present and compare two additional triggers: (1) relative attainment discrepancy, which reflects how much a firm's attainment discrepancy deviates from its past negative attainment discrepancies; and (2) persistent attainment discrepancy, which reflects how often the firm experiences below-aspirations performance. Our triggers for distant search model a behavioral explanation for the timing and relatedness of acquisitions. We find support for baseline arguments of problemistic search whereby firms increase both industry- and skill-related acquisitions when they perform below aspirations. When they persistently perform below aspirations, however, this likelihood is reduced and firms engage in acquisitions that are more unrelated, thereby providing support for the notion of expanding search boundaries from local to distant search. Of the two triggers of distant search proposed, relative attainment discrepancy does not induce firms to expand search boundaries. Our results indicate that persistent attainment discrepancy is a key construct to consider when studying the expansion of search boundaries.     

A variable-neighbourhood search algorithm for finding optimal run orders in the presence of serial correlation

The responses obtained from response surface designs that are run sequentially often exhibit serial correlation or time trends. The order in which the runs of the design are performed then has an impact on the precision of the parameter estimators. This article proposes the use of a variable-neighbourhood search algorithm to compute run orders that guarantee a precise estimation of the effects of the experimental factors. The importance of using good run orders is demonstrated by seeking D-optimal run orders for a central composite design in the presence of an AR(1) autocorrelation pattern.     

Southern knowledge online? Climate change research discoverability and communication practices

The networked age promises global digital cultures with flattened power relations, given the affordances of information and communication technologies to collapse distance, enable easier cross-country collaborations and create new opportunities for knowledge production and sharing. In the academic domains, indications are that knowledge patterns continue to reflect physically based geopolitical realities – where knowledge from the South is still peripheral while knowledge from the North still dominates in terms of all the conventional metrics. This study explores the potential role of digital affordances to challenge structural Northern bias and generates questions about knowledge production and dissemination in the climate change knowledge domain. It is framed by the field of scholarly communication within an African setting and by the emergent field of climate change which is fraught with debates and contestations, particularly regarding mitigation and adaptation. It draws on Southern theory which interrogates the global dynamics of knowledge production and dissemination. It explores the intersection of the discoverability and visibility of local climate change research methodologically from the outside in, through an experiment of searches for ‘climate change/South Africa’ and from the inside out by reviewing the online presence of one climate change group in a top-ranked African university.     

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

“欲望”与“禁忌”:中国现代女性作家的“思春”叙事

"思春"叙事是中国现代女性作家表达思想感情的重要方式,她们往往不像男性作家那样消弭了自我而去追随革命,相反,她们多以一种完全有悖于男性想象的心灵独语,十分隐晦地讲述女性对于情爱问题的矛盾心态:既渴望爱情的归宿又惧怕男性的伤害,既有本能欲望的冲动又有理性节制的掌控,她们正是通过文学艺术这种含蓄达意的表现方式,向社会展示了一种为男权话语所忽视了的精神世界。     

基于音乐搜索排行榜测量的研究

在现代生活中,音乐、电影和美术已经是不可或缺的重要组成部分。音乐排行榜在音乐工业中也起着非常重要的作用。针对一种新型的排行榜,实时的搜索排行榜进行研究。通过数据的采集与分析,研究这一类型的排行榜的排名特征和用户行为。从研究结果中,发现了搜索排行榜与传统的商业性排行榜的差别就是这一新兴排行榜中存在一部分特殊用户。近而研究了这些特殊用户的用户行为特征以及给排行榜带来的影响。