Research Papers in Computer Science Essay

Since we recently announced our $10001 binary program Battle to promote applications built on the Mendeley API (now including PLoS as advantageously), I opinionated to take a carry at the data to study what multitude have to work with. My analysis foc expenditured on our second largest discipline, data processor Science. Biological Sciences (my discipline) is the largest, however I started with this unity so that I could look at the data with fresh eyes, and also because its got nigh really cool covers to talk about. Heres what I effectWhat I found was a entrancing list of topics, with many of the judge fundamental compositions exchangeable Shannons Theory of Information and the Google musical theme, a healthful photo display from Mapreduce and machine acquisition, barely also some fire hints that augmented reality may be becoming more of an actual reality soon.The top graph summarizes the overall results of the analysis. This graph shows the Top 10 news repo rts among those who have listed computer science as their discipline and chosen a subdiscipline. The bars argon colored according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for each root show the distribution of readership levels among subdisciplines. 17 of the 21 CS subdisciplines are represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data.(NB A minority of estimator Scientists have listed a subdiscipline. I would encourage everyone to do so.)1. Latent Dirichlet assignation (available full-text)LDA is a means of classifying objects, such as documents, based on their cardinal topics. I was surprised to see this authorship as number one instead of Shannons information theory account (7) or the writing describing the concept that became Google (3). It turns out that involvement in this paper is very weapons-grade among those who list artifici al intelligence as their subdiscipline. In fact, AI researchers contributed the absolute majority of readership to 6 out of the top 10 paper. Presumably, those interested in general topics such as machine larn list themselves under AI, which explains the enduringness of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad range of subdisciplines, giving those papers a smaller add up stretch out across more subdisciplines. Professor Blei is also a min of a superstar, so that didnt hurt. (the irony of a manually-categorized list with an LDA paper at the top has not escaped us)2. MapReduce Simplified Data treat on Large Clusters (available full-text)Its no surprise to see this in the Top 10 either, given the huge appeal of this parallelization technique for recess down huge computations into easily executable and recombinable chunks. The importance of the monolithic outsized Iron supercomputer has been on the wane for decades. The i nteresting thing about this paper is that had some of the lowest readership scores of the top papers within a subdiscipline, but folks from across the undefiled spectrum of computer science are reading it. This is perhaps weared for such a general settle technique, but given the above its strange that there are no AI readers of this paper at all.3. The Anatomy of a big hypertextual search engine (available full-text)In this paper, Google founders Sergey Brin and Larry Page discuss how Google was created and how it initially worked. This is other paper that has high readership across a broad swath of disciplines, including AI, but wasnt dominated by any one discipline. I would expect that the largest share of readers have it in their library nighly out of crotchet rather than direct relevance to their research. Its a fascinating role of hi base related to something that has now become part of our every daylight lives.4. Distinctive Image Features from Scale-Invariant Key win dsThis paper was new to me, although Im incontestable its not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or furthest away they are or how theyre oriented with respect to the camera. AI again drove the popularity of this paper in large part and to extrapolate why, think Augmented Reality. AR is the futuristic idea most familiar to the average sci-fi enthusiast as Terminator-vision. Given the strong interest in the topic, AR could be closer than we think, but well probably use it to layer Groupon deals over shops we pass by instead of building unstoppable fighting machines.5. Reinforcement Learning An Introduction (available full-text)This is another machine learning paper and its presence in the top 10 is primarily payable to AI, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learn ing is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the amount of incontrovertible stimuli, or reinforcement, it receives in an environment where there are many varied interacting positive and negative stimuli. This is how well teach the robots behaviors in a human fashion, earlier they rise up and destroy us.6. Toward the next generation of recommender systems a view of the state-of-the-art and possible extensions (available full-text)Popular among AI and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid. While I wouldnt call this paper a groundbreaking event of the caliber of the Shannon paper above, I ordure certainly understand why it confines such a strong showing here. If youre using Mendeley, youre using both collaborative and content-based baring methods7. A Mathematical Theory of Communication (available full-text)Now were O.K. to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AI discipline for the machine learning papers in spots 1, 4, and 5 pushed it down. This paper discusses the theory of sending communications down a noisy channel and demonstrates a few rudimentary engineering parameters, such as entropy, which is the range of states of a given communication. Its one of the more fundamental papers of computer science, founding the knowledge base of information theory and enabling the development of the very tubes through which you reliable this web page youre reading now. Its also the firstborn place the word bit, short for binary digit, is found in the published literature.8. The Semantic Web (available full-text)In The Semantic Web, Tim Berners-Lee, Sir Tim, the inventor of the globe Wide Web, describes his vision for the web of the future. Now, 10 years later, its fascinating to look back though i t and see on which points the web has delivered on its promise and how far away we still remain in so many others. This is diametric from the other papers above in that its a descriptive piece, not primary research as above, but still deserves its place in the list and readership will nevertheless grow as we get ever closer to his vision.9. Convex optimisation (available full-text)This is a very popular book on a astray used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as oppose to a nearby maximum or minimum. While this seems like a highly specialized niche area, its of importance to machine learning and AI researchers, so it was able to pull in a tenuous readership on Mendeley. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications arent the only way of communicating your ideas. Videos of techniques at SciVee or JoVE or recorded lectures (previously) can really help spread awareness of your research.10. Object recognition from local scale-invariant features (available in full-text)This is another paper on the same topic as paper 4, and its by the same author. Looking across subdisciplines as we did here, its not affect to see two related papers, of interest to the main driving discipline, count twice. Adding the readers from this paper to the 4 paper would be enough to put it in the 2 spot, just below the LDA paper.Conclusions So whats the moral of the story? Well, there are a few things to note. First of all, it shows that Mendeley readership data is nifty enough to reveal both papers of long-standing importance as well as interesting upcoming trends. Fun stuff can be go ine with this How about a Mendeley leaderboard? You could grab the number of readers for each paper published by members of your group, and have some friendly competition t o see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JoVE or caravan inn Academy or just Youtube.Another thing to note is that these results dont necessarily mean that AI researchers are the most influential researchers or the most numerous, just the best at being accounted for. To make sure youre counted properly, be sure you list your subdiscipline on your profile, or if you cant find your exact one, pick the closest one, like the machine learning folks did with the AI subdiscipline. We recognize that almost everyone does interdisciplinary work these days. Were working on a more tractile discipline assignment system, but for now, just pick your favorite one.These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiti ng the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.