Research Papers in Computer Science Essay

Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API (now including PLoS as well), I decided to take a look at the data to see what people score to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because its got almost really cool ideas to talk about. Heres what I foundWhat I found was a fascinating list of topics, with many of the expect fundamental text file like Shannons Theory of Information and the Google paper, a inviolate wake from Mapreduce and machine learning, but also some amuseing hints that augment reality may be becoming much of an actual reality soon.The top interpret summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a subdiscipline. The bars are colored according to subdiscipline and the modus operandi of readers is shown on the x-axis. The bar graphs for each paper 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 Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)1. Latent Dirichlet Allocation (available full-text)LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannons information theory paper (7) or the paper describing the concept that became Google (3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI re exploreers contributed the majority of readership to 6 out of t he top 10 papers. Presumably, those interested in popular topics such as machine learning list themselves under AI, which explains the strength of this subdiscipline, whereas papers like the Mapreduce one or the Google paper appeal to a broad vomit of subdisciplines, giving those papers a diminisheder numbers shell out across more subdisciplines. Professor Blei is also a bit 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 Processing on Large Clusters (available full-text)Its no surprise to see this in the Top 10 either, given the huge appeal of this parallelization proficiency for breaking down huge computations into easily executable and recombinable chunks. The immenseness of the monolithic Big Iron supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers at bottom a subdisc ipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general social function technique, but given the above its strange that on that point are no AI readers of this paper at all.3. The Anatomy of a large-scale 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 another paper that has high readership across a broad swath of disciplines, including AI, but wasnt prevail by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevancy to their research. Its a fascinating piece of history related to something that has now become crack up of our every day lives.4. Distinctive Image Features from Scale-Invariant KeypointsThis paper was reinvigorated to me, although Im sure its not new to many of you. This paper de scribes how to identify objects in a video stream without regard to how coterminous or far 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 understand 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 conflict machines.5. Reinforcement Learning An Introduction (available full-text)This is another machine learning paper and its presence in the top 10 is primarily due 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 unquiet Networks. Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agen t is is controlled by the amount of positive stimuli, or reinforcement, it receives in an environment where there are many different interacting positive and negative stimuli. This is how well teach the robots behaviors in a human fashion, before they rise up and destroy us.6. Toward the next propagation of recommender systems a survey 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 roar this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong showing here. If youre using Mendeley, youre using both collaborative and content-based discovery methods7. A Mathematical Theory of Communication (available full-text)Now were back 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 uproarious channel and demonstrates a few key 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 field of information theory and enabling the development of the very tubes through which you received this web page youre reading now. Its also the first 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 craftsman of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, its fascinating to look back though it 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 differ ent 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 only grow as we get ever closer to his vision.9. Convex Optimization (available full-text)This is a very popular book on a widely used optimization technique in signal processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed 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 nice 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. Objec t recognition from local scale-invariant features (available in full-text)This is another paper on the same(p) topic as paper 4, and its by the same author. Looking across subdisciplines as we did here, its not surprising to see two related papers, of interest to the main driving discipline, appear 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 good enough to reveal both papers of long-standing importance as well as interesting upcoming trends. gambling stuff can be done 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 couthy competition to see who can get the most readers, month-over-month. Comparing yourself against others in terms of readers per paper could put a big smil e 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 khan 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 on the job(p) on a more flexible 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. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.