Top 5 Priorities for New HR Managers

Congratulations! You have just been offered the post of an HR manager! Sure, the next couple of days will be celebration, moving, boxing and unboxing. If you have a tiny bit of gap, you may even be able to squeeze in a vacation (do try the Strawberry rhubarb mojito if you get a chance!). And then, you are there at your new place, ready to roll up your mangas and get working. Now, what is that you really need to do? Chances are that the new place is (or at least will appear to be) very chaotic. That is just the nature of HR. Things are constantly moving and that is what makes the job so fulfilling! Here then, is the list of top 5 things – very simple things really, but things that you really need to do in your first week in order to start off well and be a huge success at your new job.

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My Review of Worker skill estimation paper by Rahman et al

My review of “Worker skill estimation in team-based tasks” paper by Rahman, Thirumuruganathan, Roy, Amer-Yahia and Das, is now available on Computing Reviews here. (Requires ACM membership, and it is incidentally, my 11th review for ACM Computing Reviews – you can find the other ones here.)

Companies generally struggle with the problem of estimating the skills of individuals, as it is a hard thing to keep updated and has enormous applications in optimal team formation. Skills are sometimes subjective, can have many variations (Spoken French expertise, or Written Legal French expertise, for example) and change rapidly over time. Historically, skill management used to be only for large companies implementing million dollar SAP HR system implementations, but with the advent of cloud hosted SaaS applications, that situation is now changing. I am fortunate to work on the BizMerlin‘s exciting skill matrix product, so this paper was all the more interesting for me.

My Review of Data Characterization paper by Wang et al

Review of “An improved data characterization method and its application in classification algorithm recommendation” paper by GuangtaoWang, Qinbao Song and Xiaoyan Zhu is now available on Computing Reviews here.

Classification is an active research problem, and numerous classification algorithms have been proposed over the past few years. Some algorithms perform better than others, based on the dataset. “No Silver Bullet” or “No Free Lunch Theorem” is an informal theorem that states that no single classification algorithm outperforms other classification algorithms on all data sets. This informal theorem is essentially what keeps many data scientists in business – each data set has its own idiosyncrasies, and different classification algorithms need to be explored to find the one that best meets the needs of the problem at hand. Continue reading full review.