Customers are aware of the service quality which has a close relationship with front-line service providers. High quality of human resources, like the good attitude and behavior of front-line service providers, becomes one of the key sources of business success and competitive advantages. Therefore, more and more companies pay attention to the concept of ¡§internal marketing¡¨, and before they sell the product or service to external customers, they should sell them to internal customer. Because the service industry plays an important role in this era, this study focuses on real estate agencies. The purposes of this study are: (1) To understand internal marketing influences employee¡¦s customer orientation. (2) The relationship between behavior-based evaluation, internal marketing and employee¡¦s customer orientation. (3) The relationship between empowerment, internal marketing and employee¡¦s customer orientation. (4) The relationship between organizational customer orientation, internal marketing and employee¡¦s customer orientation. The study adopts questionnaire investigating. Besides, this study modifies the questionnaire of customer orientation, let the questionnaire can be used in Taiwan. The analysis approaches are reliability analysis, factor analysis, regression analysis, cluster analysis, one-way and two-way ANOVA. This study provides support for four hypotheses that internal marketing will influence employee¡¦s customer orientation. And behavior-based evaluation, empowerment and customer orientation of organization are the moderate variables between internal marketing and employee¡¦s customer orientation. Therefore, the manager should regards internal marketing, empowerment, behavior-based evaluation and organizational customer orientation as the most vital and important components in the company.

Integration of knowledge to the control system of a robot is the best way to emerge intelligence to robot. The most useful knowledge for a robot control system that aims to visit the landmarks in an environment is the enviromental knowledge. The most natural representation of the robot&rsquo / s environment is a map. This study presents a behavior based robot control system architecture that is based on subsumption and motor schema architectures and enables the robot to construct the map of the environment by using proximity sensors, odometry sensors, compass and image. The knowledge produced after processing the sensor values, is stored in Short Term Memory (STM) or Long Term Memory (LTM) of the robot, according to the persistence requirements of the knowledge. The knowledge stored in the STM acts as a sensor value, while LTM stores the map of the environment. The map of the environment is not a priori information for the robot, but it constructs the map as it moves in the environment. By the help of the map constructed the robot will be enabled to visit non-visited areas in the environment and to localize itself in its internal world. The controller is designed for a real robot Khepera equipped with the sensors required. The controller was tested on simulator called Webots version 2.0 on Linux operating system.

The interval programming model (IvP) is a mathematical programmingmodel for representing and solving multi-objective optimizationproblems. The central characteristic of the model is the use ofpiecewise linearly defined objective functions and a solution methodthat searches through the combination space of pieces rather thanthrough the actual decision space. The piecewise functions typicallyrepresent an approximation of some underlying function, but thisconcession is balanced on the positive side by relative freedom fromfunction form assumptions as well as the assurance of global optimality.In this paper the model and solution algorithms are described, and theapplicability of IvP to certain applications arediscussed.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20660号 / 理博第4325号 / 新制||理||1621(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 友永 雅己, 准教授 足立 幾磨, 准教授 鈴木 樹理 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Background: Ransomware attacks are a significant threat to digital informa-tion, and with the increasing adoption of cloud storage services, attackers now targetcloud environments. The existing literature on ransomware detection has primarilyfocused on local environments, and there is a limited body of research on applyingthese approaches to the cloud environment. Objectives: In this thesis, we aim to develop a behavior-based ransomware de-tection system for cloud environments, specifically focusing on Google Drive, usingmachine learning techniques. We will create a dedicated Google Workspace and uti-lize the Google Cloud Platform for developing the anomaly detection classifier. Methods: We will review related work in ransomware detection and machinelearning approaches to select suitable techniques for our research. Our anomaly de-tection classifier will analyze user activities in the cloud, such as file access patternsand permission changes, to detect deviations indicative of ransomware attacks. Results: We will validate our system’s performance by conducting experimentsin our Google Workspace, emulating ransomware attacks, and comparing the classi-fier’s performance against existing techniques. Conclusions: Our thesis aims to contribute a novel, behavior-based detectionsystem for ransomware attacks in cloud environments, advancing the state-of-the-artand providing a scalable solution for various cloud storage providers.Keywords: ransomware detection, cloud environments, behavior-based detec-tion, machine learning, Google Drive.

Self-monitoring procedures have been applied to various populations, in diverse settings, and have focused on a wide variety of behaviors. While the relation between the accuracy of self-monitored data and changes in target behavior has been examined in research, there has been relatively less research focusing on the accuracy of self-monitored data in relation to the change in safety behavior. Studies (n=11) where self-monitoring procedures were introduced to target specific safety behaviors in adults were reviewed and analyzed to determine the relation between the accuracy of self-monitored data and various other aspects of an intervention, including the percent change of target behavior. This analysis found a positive but small correlation between the relevant measures. This analysis suggests that the act of self-monitoring in and of itself is important for behavior change. There is a possibility that increases in self-monitoring accuracy could lead to desirable changes in targeted safety behaviors, but further analysis of a larger sample size of studies would be required to determine the relationships among these measures.Keywords: behavior based safety, behavioral self-monitoring, self-monitoring accuracy / Applied Behavioral Analysis

This dissertation presents our work on network intrusion detection and intrusion sim- ulation. The work in intrusion detection consists of two different network anomaly-based approaches. The work in intrusion simulation introduces a model using explicit traffic gen- eration for the packet level traffic simulation. The process of anomaly detection is to first build profiles for the normal network activity and then mark any events or activities that deviate from the normal profiles as suspicious. Based on the different schemes of creating the normal activity profiles, we introduce two approaches for intrusion detection. The first one is a frequency-based approach which creates a normal frequency profile based on the periodical patterns existed in the time-series formed by the traffic. It aims at those attacks that are conducted by running pre-written scripts, which automate the process of attempting connections to various ports or sending packets with fabricated payloads, etc. The second approach builds the normal profile based on variations of connection-based behavior of each single computer. The deviations resulted from each individual computer are carried out by a weight assignment scheme and further used to build a weighted link graph representing the overall traffic abnormalities. The functionality of this system is of a distributed personal IDS system that also provides a centralized traffic analysis by graphical visualization. It provides a finer control over the internal network by focusing on connection-based behavior of each single computer. For network intrusion simulation, we explore an alternative method for network traffic simulation using explicit traffic generation. In particular, we build a model to replay the standard DARPA traffic data or the traffic data captured from a real environment. The replayed traffic data is mixed with the attacks, such as DOS and Probe attack, which can create apparent abnormal traffic flow patterns. With the explicit traffic generation, every packet that has ever been sent by the victim and attacker is formed in the simulation model and travels around strictly following the criteria of time and path that extracted from the real scenario. Thus, the model provides a promising aid in the study of intrusion detection techniques.

This dissertation introduces the concept of an artificial society based on the use of an action based social language combined with the behavior-based approach to the construction of multi-agent systems to address the problem of developing decentralized, self-organizing networks that dynamically fit into their environment. In the course of accomplishing this, social language is defined as an efficient method for communicating coordination information among cognitive radios inspired by natural societies. This communication method connects the radios within a network in a way that allows the network to learn in a distributed holistic manner. The behavior-based approach to developing multi-agent systems from the field of robotics provides the framework for developing these learning networks. In this approach several behaviors are used to address the multiple objectives of a cognitive radio society and then combined to achieve emergent properties and behaviors. This work presents a prototype cognitive radio society. This society is implemented, using low complexity hardware, and evaluated. The work does not focus on the development of optimized techniques, but rather the complementary design of techniques and agents to create dynamic, decentralized self-organizing networks / Ph. D.

This dissertation contains three essays on theoretical analysis of pricing and promotional strategies. Chapter 1 serves as a brief introduction that provides a motivation and an overview of the topics covered in the subsequent chapters. In Chapter 2, we study optimal couponing strategies in a differentiated duopoly with repeat purchase. Both firms can distribute defensive coupons alone, defensive and offensive coupons together, or mass media coupons. They can also determine how many coupons to offer. Allowing consumers to change their tastes for the firms' products over time, we find that the optimal couponing strategy for the firms is to only distribute coupons to all of the customers who buy from them. The effects of intertemporally constant preferences and consumer myopia on the profitability of the optimal couponing are investigated as well. Chapter 3 examines the profitability of behavior-based price discrimination (BBPD) by duopolists producing horizontally differentiated experience goods. We consider a three-stage game in which the firms first make price discrimination decisions followed by two-stage pricing decisions. The main findings are: (i) there are two subgame perfect Nash equilibria where both firms do not collect information about consumers' purchase histories so that neither firm price discriminates and where both firms collect consumer information to practice BBPD; and (ii) BBPD is more profitable than uniform pricing if sufficiently many consumers have a poor experience with the firms' products. The asymmetric case where one firm produces experience goods and the other search goods is also investigated. Chapter 4 provides a possible explanation of the fact that one ticket price is charged for all movies (regardless of their quality) in the motion-picture industry. Considering a model a la Hotelling in which moviegoers form their beliefs about movie quality through pricing schemes to which an exhibitor commits, we characterize the conditions under which committing to uniform pricing is more profitable than committing to variable pricing. The welfare consequences of a uniform pricing commitment and some extensions of the model are discussed as well. / Ph. D.

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