Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.Comment: Published in WWW 201

Key Strains, Anger and Delinquency: The General Strain Theory Test on Sourth Korean Youths From Low-Income Households

General Strain Theory: Negative relations of strains create unpleasant emotions (e.g., anger and depression), which lead strained people to commit crimes as their coping methods. Strains are more likely to lead to crimes

Joint analysis of user-generated content and product information to enhance user experience in e-commerce

The development of Internet has brought us a more convenient way to purchase goods through e-commerce, which has gradually pervaded our life. However, shopping experience of users in e-commerce has been far from the optimum. In order to enhance user experience in e-commerce, we propose a series of novel studies based on joint analysis of user-generated content and product information; in this dissertation, user-generated content includes user reviews and social media text data, and product information includes product descriptions and product specifications in general. This dissertation aims at assisting e-commerce users in two directions: discovering products and making purchase decisions. To help users discover products, we first propose to leverage user reviews to improve accuracy of product search. We carefully combine product descriptions and user reviews to improve product search. Then, we also propose to recommend products via inference of implicit intent in social media text. We infer implicit intent in user status text leveraging parallel corpora we build from social media, and we recommend products whose descriptions satisfy the inferred intent. In order to help users make purchase decisions, we first propose to generate augmented product specifications leveraging user reviews. Product specifications are often difficult to understand especially for high-technology products that contain many advanced features. We jointly model user reviews and product specifications to augment product specifications with useful information in the user reviews. We also propose to retrieve relevant opinions for new products. New or unpopular products often have no reviews, and such lack of information makes consumers hesitate to make a purchase decision. We leverage user reviews of similar products, where similarity is estimated using product specifications, to retrieve relevant opinions for new products. The experiment results show the proposed models are effective in general. The models are also general enough to be applied to any entities with their text data. Furthermore, the models can benefit both product manufacturers and consumers, so their potential impact may be even bigger

Efficiency Analysis of Project Management Offices for Large-scale Information System Projects: Insights for Construction Megaprojects

In this study, the efficiencies of Project Management Offices (PMOs) in large-scale information system (IS) projects are addressed by using data envelopment analysis. Moreover, the potential improvement levels for each input and output factors of inefficient PMOs are examined. The effects of performance levels of PMO functions on project outcomes with respect to efficiency levels are also analyzed. A total of forty-nine PMOs are analyzed for this study. The result shows that twenty-four PMOs are found to be efficient. As a result of analyzing the impact of efficiency on project performance depending on the functional levels of PMOs, those groups with a high degree of efficiency show higher outcomes compared with the groups with a low degree of efficiency regardless of the functional levels of PMOs. Furthermore, the gap in outcome between the groups with a high degree of efficiency and the groups with a low degree of efficiency is maintained at almost the same level, regardless of the functional levels of PMOs, with the exception of the case of practice management. This indicates that even those groups with a low degree of efficiency could expect high outcomes in terms of schedule and cost compliance if their level of practice management is high

Nitric oxide directly activates calcium-activated potassium channels from rat brain reconstituted into planar lipid bilayer

AbstractUsing the planar lipid bilayer technique, we tested whether NO directly activates calcium-activated potassium (Maxi-K) channels isolated from rat brain. We used streptozotocin (STZ) as NO donor, and the NO release was controlled with light. In the presence of 100–800 μM STZ, the Maxi-K channel activity increased up to 3-fold within several tens of seconds after the light was on, and reversed to the control level several minutes after shutting off the light. Similar activation was observed with other NO donors such as S-nitroso-N-acetylpenicillamine and sodium nitroprusside. The degree of activity increase was dependent upon the initial open probability (Pinit). When the Pinit was lower, the activity increase was greater. These results demonstrate that NO can directly affect the Maxi-K channel activity, and suggest that the Maxi-K channel might be one of the physiological targets of NO in brain