Doctor of Philosophy

dissertationThis thesis consists of three chapters. In Chapter 1, we use a unique dataset of natural disasters, including earthquakes, floods, storms, volcanic eruptions, and wildfires, to test whether investors suffer from behavioral bias such as underreacting to news and investor sentiment. In Chapter 2, we study the rationale for firms' use of inside debt (pension and deferred compensation) by exploiting the relation between firms' default risk and inside debt. In Chapter 3, we research the role of inside debt in the optimal structure of chief executive officer pay by performing a simulation analysis of investment distortions

PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services

Designing smart home services is a complex task when multiple services with a large number of sensors and actuators are deployed simultaneously. It may rely on knowledge-based or data-driven approaches. The former can use rule-based methods to design services statically, and the latter can use learning methods to discover inhabitants' preferences dynamically. However, neither of these approaches is entirely satisfactory because rules cannot cover all possible situations that may change, and learning methods may make decisions that are sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic Based Rule Extractor) is proposed to extract rules from learning methods to realize dynamic rule generation for smart home systems. The expected advantage is that both the explainability of rule-based methods and the dynamicity of learning methods are adopted. We compare PBRE with an existing rule extraction method, and the results show better performance of PBRE. We also apply PBRE to extract rules from a smart home service represented by an NRL (Neural Network-based Reinforcement Learning). The results show that PBRE can help the NRL-simulated service to make understandable suggestions to the inhabitant

An empirical study of touch-based authentication methods on smartwatches

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.Comment: ISWC '17, Proceedings of the 2017 ACM International Symposium on Wearable Computers, 122-125, ACM New York, NY, US

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.Comment: Accepted by WWW 202

Ant-colony optimization-based multi-input multi-output (ACO-MIMO) equalization for low-complexity mode-division multiplexing (MDM) processing

In mode-division multiplexing (MDM) systems, the computational complexity of the multi-input multi-output (MIMO) equalization module is a critical obstacle to practical development. The step size μ and the number of taps K are key parameters in the equalization algorithm, influencing the performance of finite impulse response (FIR) equalizers, including convergence speed and output signal quality. To alleviate the computational burden of locating the optimal μ-K combination, we propose two ant colony optimization (ACO) -based MIMO equalization schemes: the fixed ACO-MIMO and the random ACO-MIMO, corresponding to two optimization strategies. These schemes expedite the initialization process of both parameters. Subsequently, we conduct experiments to evaluate their performance in a 3-mode recirculating-loop transmission system. Our findings demonstrate that, compared to conventional schemes, such as genetic algorithm (GA) and steepest descent algorithm (SDA), the proposed ACO-MIMO schemes significantly reduce the number of calls to the equalization algorithm for locating optimal μ-K combination by up to 42.74% and 80.63%, reducing the complexity of the whole MIMO equalization for MDM systems. And the resulting hit-rate Phit for the optimal μ-K combination reaches up to 99.34%. Moreover, the ACO-MIMO schemes exhibit stable performance across different data collected from various round-trips, confirming the robust operation for the long-haul MDM transmission. Finally, we investigate the performance disparity between the two proposed ACO-MIMO schemes through bit-error-rate (BER) distribution, concluding that under a large dataset with various BER distributions, the performance of both schemes is essentially equivalent

A targeted metabolomic protocol for short-chain fatty acids and branched-chain amino acids

Research in obesity and metabolic disorders that involve intestinal microbiota demands reliable methods for the precise measurement of the short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs) concentration. Here, we report a rapid method of simultaneously determining SCFAs and BCAAs in biological samples using propyl chloroformate (PCF) derivatization followed by gas chromatography mass spectrometry (GC-MS) analysis. A one-step derivatization using 100 µL of PCF in a reaction system of water, propanol, and pyridine (v/v/v = 8:3:2) at pH 8 provided the optimal derivatization efficiency. The best extraction efficiency of the derivatized products was achieved by a two-step extraction with hexane. The method exhibited good derivatization efficiency and recovery for a wide range of concentrations with a low limit of detection for each compound. The relative standard deviations (RSDs) of all targeted compounds showed good intra- and inter-day (within 7 days) precision (< 10%), and good stability (< 20%) within 4 days at room temperature (23–25 °C), or 7 days when stored at −20 °C. We applied our method to measure SCFA and BCAA levels in fecal samples from rats administrated with different diet. Both univariate and multivariate statistics analysis of the concentrations of these target metabolites could differentiate three groups with ethanol intervention and different oils in diet. This method was also successfully employed to determine SCFA and BCAA in the feces, plasma and urine from normal humans, providing important baseline information of the concentrations of these metabolites. This novel metabolic profile study has great potential for translational research

Conception de services pour maison intelligente à l'aide d'approches basées sur l'apprentissage automatique et la représentation de connaissances

L'intelligence de la maison intelligente est réalisée en créant divers services. Chaque service tente d'ajuster un état monitoré en contrôlant les actionneurs associés après avoir pris en compte les états de l'environnement détectés par les capteurs. Cependant, la conception de la logique des services déployés dans une maison intelligente se heurte à des limitations soit d'adaptabilité dynamique (règles prédéfinies) soit d'explicabilité (techniques d'apprentissage). Quatre propositions s'inscrivant dans une approche hybride combinant des règles prédéfinies et des techniques d'apprentissage visent à lever ces limitations. La première proposition consiste à utiliser l'apprentissage renforcé pour créer un service dynamique. Le déploiement de ce service unique comprend deux phases : le pré-entraînement dans la simulation et l'entraînement continu dans le monde réel. Notre étude se concentre uniquement sur la partie simulation. En étendant la première proposition, la deuxième proposition propose plusieurs architectures pour créer plusieurs services dynamiques et sans conflit. Cependant, les services dirigés par les données ne sont pas explicables. Par conséquent, la troisième proposition vise à extraire des services explicables basés sur la connaissance à partir de services dynamiques dirigés par les données. La quatrième proposition tente de combiner les deuxième et troisième propositions pour créer des services dynamiques et explicables. Ces propositions sont évaluées dans un environnement simulé sur des services de contrôle de la température, de l'intensité lumineuse et de la qualité de l'air adaptés aux activités de l'habitant. Elles peuvent être étendues selon plusieurs perspectives, telles que la co-simulation de phénomènes physiques, l'adaptation dynamique à différents profils d'habitant, et l'efficacité énergétique des services déployés.The intelligence of a smart home is realized by creating various services. Eachservice tries to adjust one monitored state by controlling related actuators after consideringenvironment states detected by sensors. However, the design of the logic of the services deployedin a smart home faces limitations of either dynamic adaptability (predefined rules) orexplicability (learning techniques). Four proposals that are parts of a hybrid approach combiningpredefined rules and learning techniques aim at mitigating these limitations.The first proposal is to use reinforcement learning to create a dynamic service. The deploymentof this single service includes two phases : pretraining in the simulation and continuous trainingin the real world. Our study only focuses on the simulation part. Extending the first proposal,the second proposal proposes several architectures to create multiple dynamic and conflictfreeservices. However, the created data-driven services are not explicable. Therefore, the thirdproposal aims to extract explicable knowledgebased services from dynamic data-driven services.The fourth proposal attempts to combine the second and third proposals to create dynamicand explicable services. These proposals are evaluated in a simulated environment ontemperature control, light intensity, and air quality services adapted to the activities of the inhabitant.They can be extended according to several perspectives, such as the co-simulation ofphysical phenomena, the dynamic adaptation to various inhabitant profiles, and the energy efficiencyof the deployed services

Conception de services pour maison intelligente à l'aide d'approches basées sur l'apprentissage automatique et la représentation de connaissances

The intelligence of a smart home is realized by creating various services. Eachservice tries to adjust one monitored state by controlling related actuators after consideringenvironment states detected by sensors. However, the design of the logic of the services deployedin a smart home faces limitations of either dynamic adaptability (predefined rules) orexplicability (learning techniques). Four proposals that are parts of a hybrid approach combiningpredefined rules and learning techniques aim at mitigating these limitations.The first proposal is to use reinforcement learning to create a dynamic service. The deploymentof this single service includes two phases : pretraining in the simulation and continuous trainingin the real world. Our study only focuses on the simulation part. Extending the first proposal,the second proposal proposes several architectures to create multiple dynamic and conflictfreeservices. However, the created data-driven services are not explicable. Therefore, the thirdproposal aims to extract explicable knowledgebased services from dynamic data-driven services.The fourth proposal attempts to combine the second and third proposals to create dynamicand explicable services. These proposals are evaluated in a simulated environment ontemperature control, light intensity, and air quality services adapted to the activities of the inhabitant.They can be extended according to several perspectives, such as the co-simulation ofphysical phenomena, the dynamic adaptation to various inhabitant profiles, and the energy efficiencyof the deployed services.L'intelligence de la maison intelligente est réalisée en créant divers services. Chaque service tente d'ajuster un état monitoré en contrôlant les actionneurs associés après avoir pris en compte les états de l'environnement détectés par les capteurs. Cependant, la conception de la logique des services déployés dans une maison intelligente se heurte à des limitations soit d'adaptabilité dynamique (règles prédéfinies) soit d'explicabilité (techniques d'apprentissage). Quatre propositions s'inscrivant dans une approche hybride combinant des règles prédéfinies et des techniques d'apprentissage visent à lever ces limitations. La première proposition consiste à utiliser l'apprentissage renforcé pour créer un service dynamique. Le déploiement de ce service unique comprend deux phases : le pré-entraînement dans la simulation et l'entraînement continu dans le monde réel. Notre étude se concentre uniquement sur la partie simulation. En étendant la première proposition, la deuxième proposition propose plusieurs architectures pour créer plusieurs services dynamiques et sans conflit. Cependant, les services dirigés par les données ne sont pas explicables. Par conséquent, la troisième proposition vise à extraire des services explicables basés sur la connaissance à partir de services dynamiques dirigés par les données. La quatrième proposition tente de combiner les deuxième et troisième propositions pour créer des services dynamiques et explicables. Ces propositions sont évaluées dans un environnement simulé sur des services de contrôle de la température, de l'intensité lumineuse et de la qualité de l'air adaptés aux activités de l'habitant. Elles peuvent être étendues selon plusieurs perspectives, telles que la co-simulation de phénomènes physiques, l'adaptation dynamique à différents profils d'habitant, et l'efficacité énergétique des services déployés