API Documentation

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The fact that you're here implies that you're looking for the search API documentation used within the ReserchKernel project.

Let's begin!

1. Search papers by category:

This API provides the support to list papers by start_date, end_date, category and primary_category.

GET /papers/search

Examples:

1. Get papers based on primary category.

---

GET http://localhost:8000/papers/search?start_date=01/01/2016&end_date=01/01/2017
{
	"primary_category": "cs"
}

Response:

                "_source": {
                "arxiv_id": "1201.5621",
                "abstract": "We study a model of congestible resources, where pricing and scheduling are\nintertwined. Motivated by the problem of pricing cloud instances, we model a\ncloud computing service as linked $GI/GI/\\cdot$ queuing systems where the\nprovider chooses to offer a fixed pricing service, a dynamic market based\nservice, or a hybrid of both, where jobs can be preempted in the market-based\nservice. Users (jobs), who are heterogeneous in both the value they place on\nservice and their cost for waiting, then choose between the services offered.\nCombining insights from auction theory with queuing theory we are able to\ncharacterize user equilibrium behavior, and show its insensitivity to the\nprecise market design mechanism used. We then provide theoretical and\nsimulation based evidence suggesting that a fixed price typically, though not\nalways, generates a higher expected revenue than the hybrid system for the\nprovider.",
                "authors": [
                    "Abhishek Vineet",
                    "Kash Ian A.",
                    "Key Peter"
                ],
                "categories": [
                    "cs.GT"
                ],
                "created": "2012-01-25",
                "doi": null,
                "primary_category": "cs",
                "title": "Fixed and Market Pricing for Cloud Services",
                "updated": 1483660800000
            }, 
            "_source": {
                "arxiv_id": "1304.4150",
                "abstract": "We investigate some basic questions about the interaction of regular and\nrational relations on words. The primary motivation comes from the study of\nlogics for querying graph topology, which have recently found numerous\napplications. Such logics use conditions on paths expressed by regular\nlanguages and relations, but they often need to be extended by rational\nrelations such as subword or subsequence. Evaluating formulae in such extended\ngraph logics boils down to checking nonemptiness of the intersection of\nrational relations with regular or recognizable relations (or, more generally,\nto the generalized intersection problem, asking whether some projections of a\nregular relation have a nonempty intersection with a given rational relation).\n  We prove that for several basic and commonly used rational relations, the\nintersection problem with regular relations is either undecidable (e.g., for\nsubword or suffix, and some generalizations), or decidable with\nnon-primitive-recursive complexity (e.g., for subsequence and its\ngeneralizations). These results are used to rule out many classes of graph\nlogics that freely combine regular and rational relations, as well as to\nprovide the simplest problem related to verifying lossy channel systems that\nhas non-primitive-recursive complexity. We then prove a dichotomy result for\nlogics combining regular conditions on individual paths and rational relations\non paths, by showing that the syntactic form of formulae classifies them into\neither efficiently checkable or undecidable cases. We also give examples of\nrational relations for which such logics are decidable even without syntactic\nrestrictions.",
                "authors": [
                    "Barcelo Pablo",
                    "Figueira Diego",
                    "Libkin Leonid"
                ],
                "categories": [
                    "cs.FL"
                ],
                "created": "2013-04-14",
                "doi": "10.2168/LMCS-9(3:01)2013",
                "primary_category": "cs",
                "title": "Graph Logics with Rational Relations",
                "updated": 1372723200000
            }

### Get papers for sub category

4. GET http://localhost:8000/papers/search?start_date=01/01/2016&end_date=01/01/2017
{
	"category": "cs.AI",
}

Response:

                "_source": {
                "arxiv_id": "1701.00349",
                "abstract": "In the past, several models of consciousness have become popular and have led\nto the development of models for machine consciousness with varying degrees of\nsuccess and challenges for simulation and implementations. Moreover, affective\ncomputing attributes that involve emotions, behavior and personality have not\nbeen the focus of models of consciousness as they lacked motivation for\ndeployment in software applications and robots. The affective attributes are\nimportant factors for the future of machine consciousness with the rise of\ntechnologies that can assist humans. Personality and affection hence can give\nan additional flavor for the computational model of consciousness in humanoid\nrobotics. Recent advances in areas of machine learning with a focus on deep\nlearning can further help in developing aspects of machine consciousness in\nareas that can better replicate human sensory perceptions such as speech\nrecognition and vision. With such advancements, one encounters further\nchallenges in developing models that can synchronize different aspects of\naffective computing. In this paper, we review some existing models of\nconsciousnesses and present an affective computational model that would enable\nthe human touch and feel for robotic systems.",
                "authors": [
                    "Chandra Rohitash"
                ],
                "categories": [
                    "cs.AI"
                ],
                "created": "2017-01-01",
                "doi": null,
                "primary_category": "cs",
                "title": "An affective computational model for machine consciousness",
                "updated": " "
            },
            "_source": {
                "abstract": "We present an algorithm for building probabilistic rule lists that is two\norders of magnitude faster than previous work. Rule list algorithms are\ncompetitors for decision tree algorithms. They are associative classifiers, in\nthat they are built from pre-mined association rules. They have a logical\nstructure that is a sequence of IF-THEN rules, identical to a decision list or\none-sided decision tree. Instead of using greedy splitting and pruning like\ndecision tree algorithms, we fully optimize over rule lists, striking a\npractical balance between accuracy, interpretability, and computational speed.\nThe algorithm presented here uses a mixture of theoretical bounds (tight enough\nto have practical implications as a screening or bounding procedure),\ncomputational reuse, and highly tuned language libraries to achieve\ncomputational efficiency. Currently, for many practical problems, this method\nachieves better accuracy and sparsity than decision trees; further, in many\ncases, the computational time is practical and often less than that of decision\ntrees. The result is a probabilistic classifier (which estimates P(y = 1|x) for\neach x) that optimizes the posterior of a Bayesian hierarchical model over rule\nlists.",
                "arxiv_id": "1602.08610",
                "authors": [
                    "Yang Hongyu",
                    "Rudin Cynthia",
                    "Seltzer Margo"
                ],
                "categories": [
                    "cs.AI"
                ],
                "created": "2016-02-27",
                "doi": null,
                "primary_category": "cs",
                "title": "Scalable Bayesian Rule Lists",
                "updated": "2017-04-03"
            }

2. Fetch recent papers

This API returns the most recently published papers.

GET /papers/recent