Research Papers in Computer Science Essay Example for Free

Research Papers in Computer Science EssaySince we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API (now including PLoS as well), I decided to set out a go steady at the data to see what people ointment out to work with. My analysis focused on our second freehandedst discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this whiz so that I could view at the data with fresh eyes, and also because its got some really cool written document to sing about. Heres what I foundWhat I found was a fascinating list of topics, with more of the expected primal roots like Shannons Theory of Information and the Google story, a hale exhibit from Mapreduce and machine learning, but also some interesting hints that augmented reality may be congruous more of an actual reality soon.The top graph summarizes the overall results of the analysis. This graph shows the teetotum 10 storys among tho se who have listed computer science as their discipline and chosen a subdiscipline. The bars ar colourise according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for for each one wall stem 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 by means ofout. Click on both graph to explore it in more detail or to grab the raw data.(NB A nonage of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)1. Latent Dirichlet Allocation (available full-text)LDA is a content 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 purpose 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 researchers contributed the majority of readership to 6 out of the top 10 papers. Presumably, those interested in touristed 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 range of subdisciplines, giving those papers a little numbers spread crossways 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 simplify 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 technique for breaking down huge computations into easily viable and recombinable chunks. The importance 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 within a subdiscipline, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a familiar purpose technique, but given the above its strange that there 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 dominated 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 relevance to their research. Its a fascinating pitch of history related to something that has now become part of our every day lives.4. Distinctive Image Features from Scale-Invariant KeypointsThis paper was new to me, althou gh Im sure its not new to many of you. This paper describes how to find objects in a moving picture stream without regard to how near 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 nearer than we think, but well probably use it to layer Groupon deals over shops we pass by instead of building unbeatable 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 due to AI, with a small contribution from folks listing neuronal networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks. Reinforcement learning is essentially a technique that borrows fr om biology, where the behavior of an intelligent agent is is controlled by the amount of verifying 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 beside generation 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 call this paper a groundbreaking issuance 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 numeric 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 noisy channel and demonstrates a few divulge 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 come to the fore the war cry 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 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 promi se and how far away we unsounded remain in so many others. This is different 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 squeeze ever closer to his vision.9. protuberant Optimization (available full-text)This is a very popular book on a widely used optimisation 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 super specialized niche area, its of importance to machine learning and AI researchers, so it was able to buck 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 reco rded lectures (previously) can really financial aid spread awareness of your research.10. Object recognition from local scale-invariant features (available in full-text)This is another paper on the corresponding 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 advanced enough to reveal both papers of long-standing importance as well as interesting future trends. Fun 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 friendly 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 smile on your face, or it could be a gentle nudge to get out to more conferences or possibly record a video of your technique for JoVE or Khan Academy or just Youtube.another(prenominal) thing to note is that these results dont necessarily mean that AI researchers are the most authoritative 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 imminent 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 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. pass the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars.