1,259 research outputs found
Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce
Healthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering
AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments
Plagiarism is one of the growing issues in academia and is always a concern
in Universities and other academic institutions. The situation is becoming even
worse with the availability of ample resources on the web. This paper focuses
on creating an effective and fast tool for plagiarism detection for text based
electronic assignments. Our plagiarism detection tool named AntiPlag is
developed using the tri-gram sequence matching technique. Three sets of text
based assignments were tested by AntiPlag and the results were compared against
an existing commercial plagiarism detection tool. AntiPlag showed better
results in terms of false positives compared to the commercial tool due to the
pre-processing steps performed in AntiPlag. In addition, to improve the
detection latency, AntiPlag applies a data clustering technique making it four
times faster than the commercial tool considered. AntiPlag could be used to
isolate plagiarized text based assignments from non-plagiarised assignments
easily. Therefore, we present AntiPlag, a fast and effective tool for
plagiarism detection on text based electronic assignments
Predicting Rising Follower Counts on Twitter Using Profile Information
When evaluating the cause of one's popularity on Twitter, one thing is
considered to be the main driver: Many tweets. There is debate about the kind
of tweet one should publish, but little beyond tweets. Of particular interest
is the information provided by each Twitter user's profile page. One of the
features are the given names on those profiles. Studies on psychology and
economics identified correlations of the first name to, e.g., one's school
marks or chances of getting a job interview in the US. Therefore, we are
interested in the influence of those profile information on the follower count.
We addressed this question by analyzing the profiles of about 6 Million Twitter
users. All profiles are separated into three groups: Users that have a first
name, English words, or neither of both in their name field. The assumption is
that names and words influence the discoverability of a user and subsequently
his/her follower count. We propose a classifier that labels users who will
increase their follower count within a month by applying different models based
on the user's group. The classifiers are evaluated with the area under the
receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy,
NY, US
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
Evaluation and Implementation of n-Gram-Based Algorithm for Fast Text Comparison
This paper presents a study of an n-gram-based document comparison method. The method is intended to build a large-scale plagiarism detection system. The work focuses not only on an efficiency of the text similarity extraction but also on the execution performance of the implemented algorithms. We took notice of detection performance, storage requirements and execution time of the proposed approach. The obtained results show the trade-offs between detection quality and computational requirements. The GPGPU and multi-CPU platforms were considered to implement the algorithms and to achieve good execution speed. The method consists of two main algorithms: a document's feature extraction and fast text comparison. The winnowing algorithm is used to generate a compressed representation of the analyzed documents. The authors designed and implemented a dedicated test framework for the algorithm. That allowed for the tuning, evaluation, and optimization of the parameters. Well-known metrics (e.g. precision, recall) were used to evaluate detection performance. The authors conducted the tests to determine the performance of the winnowing algorithm for obfuscated and unobfuscated texts for a different window and n-gram size. Also, a simplified version of the text comparison algorithm was proposed and evaluated to reduce the computational complexity of the text comparison process. The paper also presents GPGPU and multi-CPU implementations of the algorithms for different data structures. The implementation speed was tested for different algorithms' parameters and the size of data. The scalability of the algorithm on multi-CPU platforms was verified. The authors of the paper provide the repository of software tools and programs used to perform the conducted experiments.he appropriate fast document comparison system. Its performance is given in the paper
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