We study inventory optimization for locally controlled, continuous‐review distribution systems with stochastic customer demands. Each node follows a base‐stock policy and a first‐come, first‐served allocation policy. We develop two heuristics, the
A Heuristic Method for Identifying Scam Ads on Craigslist
Craigslist is a popular online customer-to-customer marketplace, which has attracted millions of consumers for trading and purchasing secondhand items. Because of the high financial return that sellers could gain from using this site and the anonymity option that the website provides to its users, Craigslist is highly subject to fraudulent activities. The primary objective of this study is to detect scam ads on Craigslist. Based on the related literature and our observations of ads collected from the platform, we develop a heuristic method for identifying scam ads. We evaluate the proposed heuristics by conducting an experiment and performing additional data analyses using real data. The results provide preliminary evidence for efficacy of the heuristics developed in this study.
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- Award ID(s):
- 1912898
- NSF-PAR ID:
- 10095443
- Date Published:
- Journal Name:
- European Intelligence & Security Informatics Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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recursive optimization (RO) heuristic and thedecomposition‐aggregation (DA) heuristic, to approximate the optimal base‐stock levels of all the locations in the system. The RO heuristic applies a bottom‐up approach that sequentially solves single‐variable, convex problems for each location. The DA heuristic decomposes the distribution system into multiple serial systems, solves for the base‐stock levels of these systems using the newsvendor heuristic of Shang and Song (2003), and then aggregates the serial systems back into the distribution system using a procedure we call “backorder matching.” A key advantage of the DA heuristic is that it does not require any evaluation of the cost function (a computationally costly operation that requires numerical convolution). We show that, for both RO and DA, changing some of the parameters, such as leadtime, unit backordering cost, and demand rate, of a location has an impact only on its own local base‐stock level and its upstream locations’ local base‐stock levels. An extensive numerical study shows that both heuristics perform well, with the RO heuristic providing more accurate results and the DA heuristic consuming less computation time. We show that both RO and DA are asymptotically optimal along multiple dimensions for two‐echelon distribution systems. Finally, we show that, with minor changes, both RO and DA are applicable to the balanced allocation policy. -
Most online mobile services make use of location data to improve customer experience. Mobile users can locate points of interest near them, or can receive recommendations tailored to their whereabouts. However, serious privacy concerns arise when location data is revealed in clear to service providers. Several solutions employ searchable encryption (SE) to evaluate spatial predicates directly on location ciphertexts. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting -- Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boost the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Our extensive experimental evaluation on real-life datasets shows that our solutions can improve computational overhead by a factor of two compared to the baseline.more » « less
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Most online mobile services make use of location data to improve customer experience. Mobile users can locate points of interest near them, or can receive recommendations tailored to their whereabouts. However, serious privacy concerns arise when location data is revealed in clear to service providers. Several solutions employ searchable encryption (SE) to evaluate spatial predicates directly on location ciphertexts. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting -- Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boost the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Our extensive experimental evaluation on real-life datasets shows that our solutions can improve computational overhead by a factor of two compared to the baseline.more » « less
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We use the near universe of U.S. online job ads to document four new facts about the skills employers demand from college majors. First, some skills—social and organizational—are demanded from all majors whereas others—financial and customer service—are demanded from only particular majors. Second, some majors have skill demand profiles that mirror overall demand for college graduates, such as Business and General Engineering, while other majors, such as Nursing and Education, have relatively rare skill profiles. Third, cross-major differences in skill profiles explain considerable wage variation. Fourth, although major-specific skill demand varies across place, this variation plays little role in explaining wage variation. College majors can thus be reasonably conceptualized as portable bundles of skills.more » « less
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A bstract We compute 1 /λ corrections to the four-point functions of half-BPS operators in SU( N ) $$ \mathcal{N} $$ N = 4 super-Yang-Mills theory at large N and large ’t Hooft coupling λ = $$ {g}_{\mathrm{YM}}^2N $$ g YM 2 N using two methods. Firstly, we relate integrals of these correlators to derivatives of the mass deformed S 4 free energy, which was computed at leading order in large N and to all orders in 1 /λ using supersymmetric localization. Secondly, we use AdS/CFT to relate these 1 /λ corrections to higher derivative corrections to supergravity for scattering amplitudes of Kaluza-Klein scalars in IIB string theory on AdS 5 × S 5 , which in the flat space limit are known from worldsheet calculations. These two methods match at the order corresponding to the tree level R 4 interaction in string theory, which provides a precise check of AdS/CFT beyond supergravity, and allow us to derive the holographic correlators to tree level D 4 R 4 order. Combined with constraints from [1], our results can be used to derive CFT data to one-loop D 4 R 4 order. Finally, we use AdS/CFT to fix these correlators in the limit where N is taken to be large while g YM is kept fixed. In this limit, we present a conjecture for the small mass limit of the S 4 partition function that includes all instanton corrections and is written in terms of the same Eisenstein series that appear in the study of string theory scattering amplitudes.more » « less
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