2,727 research outputs found
NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS
Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidates’ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge required to be successful in the job market. Furthermore, the study found that while 96% of chief academic officers believe that their institutions are very or somewhat effective at preparing students for the workforce, only 11% of business leaders strongly agree [11]. An implication of the misalignment is that college graduates lack the skills that companies need and value. Fortunately, the rise of skills-based hiring provides an opportunity for universities and students to establish and follow clearer classroom-to-career pathways. To this end, this paper presents a course recommender system that aims to improve students’ career readiness by suggesting relevant skills and courses based on their unique career interests
Malware Classification based on Call Graph Clustering
Each day, anti-virus companies receive tens of thousands samples of
potentially harmful executables. Many of the malicious samples are variations
of previously encountered malware, created by their authors to evade
pattern-based detection. Dealing with these large amounts of data requires
robust, automatic detection approaches. This paper studies malware
classification based on call graph clustering. By representing malware samples
as call graphs, it is possible to abstract certain variations away, and enable
the detection of structural similarities between samples. The ability to
cluster similar samples together will make more generic detection techniques
possible, thereby targeting the commonalities of the samples within a cluster.
To compare call graphs mutually, we compute pairwise graph similarity scores
via graph matchings which approximately minimize the graph edit distance. Next,
to facilitate the discovery of similar malware samples, we employ several
clustering algorithms, including k-medoids and DBSCAN. Clustering experiments
are conducted on a collection of real malware samples, and the results are
evaluated against manual classifications provided by human malware analysts.
Experiments show that it is indeed possible to accurately detect malware
families via call graph clustering. We anticipate that in the future, call
graphs can be used to analyse the emergence of new malware families, and
ultimately to automate implementation of generic detection schemes.Comment: This research has been supported by TEKES - the Finnish Funding
Agency for Technology and Innovation as part of its ICT SHOK Future Internet
research programme, grant 40212/0
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
Deep generative modeling for single-cell transcriptomics.
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task
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