10 Habits Successful People Give Up to Increase Their Productivity


1. They don’t work in their comfort zone.
2. They don’t do without first learning.

3. They don’t fear asking for advice.

4. They don’t get lost in the small details.

5. They don’t multitask.

6. They don’t lie to themselves.

7. They don’t procrastinate in asking for feedback.

8. They don’t follow, they lead.

9. They don’t let the past dictate their future.

10. They don’t hang around negative people.

http://www.lifehack.org/articles/productivity/10-habits-successful-people-give-increase-their-productivity.html

Comments

Post a Comment

Popular posts from this blog

Influences on Query Reformalution in Collaborative Web Search

Zhen Yue, Shuguang Han, Daqing He, and Jiepu Jiang ( Computer-2014 ) http://ieeexplore.ieee.org/abstract/document/6766167/ In the paper “Influences on Query Reformation in Collaborative Web Search”, Yue et al. investigate a study to analyze the influences of collaboration between members in a group for information seeking. They are motivated to study (1) the influence of collaboration on search query formulation and reformulation; (2) similarities and differences exist in terms of query reformulation for collaborative searches compared with individual searches; and (3) the nature of the query task influence query reformulation in collaborative information seeking (CIS). In order to run the study, Yue et al. used CollabSearch, a Web search system developed by their group for collaborative users. The system includes three frames: a topic statement page which shows the description about the search task a group want to conduct; a web search page which displays Google search results...
Read more

Personalized Access to Scientific Publications: from Recommendation to Explanation

Dario De Nart, Felice Ferrara, and Carlo Tasso ( UMAP-2013 ) http://link.springer.com/chapter/10.1007/978-3-642-38844-6_26 In the paper “Personalized Access to Scientific Publications: from Recommendation to Explanation”, Dario De Nart et al. present a Recommender and Explanation System (RES) that is able to recommend scientific publication and show explanation to users.  In order to do recommendation, Dario De Nart et al., firstly, use the The Dikpe Keyphrase extraction algorithm introduced by Pudota (2010) to extract keyphrases from papers. Each keyphrase has a weight called keyphraseness that reveals the several lexical and statistical indicators exploited in the extraction process. Higher is the keyphraseness, more relevant is the KP. For each paper in the collection, they represent it as a conceptual graph. Each node in the graph is a term broken down from a KP. Two nodes are connected when they appear in the same KP. For instance, the KP “document retrieval” is split int...
Read more

SoRec: Social Recommendation Using Probabilistic Matrix Factorization

Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King ( CIKM-08 ) http://dl.acm.org/citation.cfm?id=1458205 In this paper, Hao Ma et al. present an approach that help to deal with very large datasets and users who have made few ratings in recommender systems. Traditional methods (e.g., Pearson Correlation Coefficient and Cosine) assume that two users have rated at least some items in common so that they can compute the similarity of these users. Thus, memory-based and model-based collaborative filtering algorithms fail to find similarities of users who have never rated any items. In daily life, people usually ask their family or friends for recommendations of new movies, songs, books, restaurants or places to visit. Therefore, the authors propose a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users’ social network information and rating records. Different with traditional persp...
Read more