Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality

We focus on addressing the challenges in responsible beauty product recommendation, particularly when it involves comparing the product's color with a person's skin tone, such as for foundation and concealer products. To make accurate recommendations, it is crucial to infer both the product attributes and the product specific facial features such as skin conditions or tone. However, while many product photos are taken under good light conditions, face photos are taken from a wide range of conditions. The features extracted using the photos from ill-illuminated environment can be highly misleading or even be incompatible to be compared with the product attributes. Hence bad illumination condition can severely degrade quality of the recommendation. We introduce a machine learning framework for illumination assessment which classifies images into having either good or bad illumination condition. We then build an automatic user guidance tool which informs a user holding their camera if their illumination condition is good or bad. This way, the user is provided with rapid feedback and can interactively control how the photo is taken for their recommendation. Only a few studies are dedicated to this problem, mostly due to the lack of dataset that is large, labeled, and diverse both in terms of skin tones and light patterns. Lack of such dataset leads to neglecting skin tone diversity. Therefore, We begin by constructing a diverse synthetic dataset that simulates various skin tones and light patterns in addition to an existing facial image dataset. Next, we train a Convolutional Neural Network (CNN) for illumination assessment that outperforms the existing solutions using the synthetic dataset. Finally, we analyze how the our work improves the shade recommendation for various foundation products.Comment: 7 pages, 5 figures. Presented in FAccTRec202

What's Trust Got To Do With It?

Based on focus groups with parents, explores why school turnaround options such as closing failing schools and replacing principals and staff provoke community opposition. Outlines ways for leaders to build trust, address concerns, and engage parents

Passive Creditors

Creditors are often passive because they are reluctant to show bad debts on their own balance sheets. We propose a simple general equilibrium model to study the externality effect of creditor passivity. The model yields rich insights in the phenomenon of creditor passivity, both in transition and developed market economies. Policy implications are deduced. The model also explains in what respect banks differ from enterprises and what this implies for policy. Commonly observed phenomenons in the banking sector, such as deposit insurance, lender of last resort facilities, government coordination to work out bad loans and special bank closure provisions, are interpreted in our framework.http://deepblue.lib.umich.edu/bitstream/2027.42/40123/3/wp737.pd

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd

Spartan Daily May 4, 2010

Volume 134, Issue 48https://scholarworks.sjsu.edu/spartandaily/1263/thumbnail.jp

Unpacking the State’s Reputation

International law scholars debate when international law matters to states, how it matters, and whether we can improve compliance. One of the few areas of agreement is that fairly robust levels of compliance can be achieved by tapping into states’ concerns with their reputation. The logic is intuitively appealing: a state that violates international law develops a bad reputation, which leads other states to exclude the violator from future cooperative opportunities. Anticipating a loss of future gains, states will often comply with international rules that are not in their immediate interests. The level of compliance that reputation can sustain depends, however, on how the government decision makers value the possibility of being excluded from future cooperative agreements. This Article examines how governments internalize reputational costs to the “state” and how audiences evaluate the predictive value of violating governments’ actions. The Article concludes that international law’s current approach to reputation is counterproductive, because it treats reputation as an error term that makes rationalists’ claims invariably correct

Biases in Race and Social Desirability within Jury Simulations

This study examined how the defendant’s race and the victim’s social desirability influence sentencing. Participants were randomly assigned to read one of four crime scenarios, featuring either a Black or White defendant or a socially desirable or undesirable victim. For each scenario the defendant’s race was manipulated and participants were shown a picture of either a Black or White male. Data were collected at two different time periods, because of potential influence of media coverage of racial bias in jury decisions. Therefore, the effects of defendant race, victim social desirability, and time period were tested through an experiment using a 2x2x2 design. The predicted main effect was that Black defendants would receive a harsher sentence than White defendants. It was also predicted that crimes against a socially desirable victim would lead to greater sentencing than for a socially undesirable victim. The predicted interaction was that the defendant’s race would influence sentencing less for the socially desirable victim, because the crime of hurting someone who is good is uniformly negative. However it was predicted that the defendant’s race would influence sentencing more for the socially undesirable victim, because the crime of hurting someone who is bad is more ambiguous. While there was a significant three-way interaction, results did not map on to predictions. Future research should continue to examine the effect of racial bias on jury decisions