<p>Nowadays, there exist two reporting regimes, rules-based and principle-based (comply-or-explain). In the rules-based environment, researchers have studied the relationship between internal control quality and accounting quality. Prior studies have suggested that reports on internal control are an effective way for investors to evaluate the quality of the firm‟s internal control. By having a sound system of internal control, it creates reliance upon the firm‟s financial reporting. Therefore, the condition of internal control has direct connection to the investors‟ decision making.  In  U.S., where the corporate governance has been known to be typically rules-based, most researches have shown a positive relationship between internal control reporting and accounting quality. In the comply-or-explain reporting regime, there has only been one research as far as we know, that investigates the relationship between internal control reporting and accounting quality. The research took place in Netherlands; their study provided positive association between internal control  reporting quality and accounting quality, particularly earnings quality. The result is consistent with other research in rules-based regime.    The Swedish code has  been enacted for  five years and it is in the same regime as Netherlands. However, under current Swedish code, we are not aware of any empirical research  that  has taken place to address the effectiveness of the code. Therefore, we conducted the study with 152 firms listed on Swedish NASDAQ OMXS to test internal control reporting quality and accounting quality. We used proxies for internal control reporting quality: effectiveness statement of  internal control, internal control reporting score and internal control proportion. Simultaneously, we use discretionary accruals as a proxy of accounting quality. Our result showed a negative significant relationship between internal control reporting score and discretionary accruals but this relationship is quite weak and sensitive. It is broken with the presence of one more variable in regression, such as size variable. The result could not give any evidence for relationships between either effectiveness statements or extent of internal control reporting with discretionary accruals. Although, the relationship between internal control reporting score and discretionary accruals is not tight, it still encourage the  notion  that firms with good internal control report will improve their accounting quality. Our research contributes knowledge to wide-range of concerned parties. Moreover, it also suggests valuable ideas for further studies in the same area.</p>

Urban land cover mapping represents one of the most important remote sensing applications in the context of rapid global urbanization. In recent years, high resolution spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) has been increasingly used for urban land cover/land-use mapping, since more information could be obtained in multiple polarizations and the collection of such data is less influenced by solar illumination and weather conditions.  The overall objective of this research is to develop effective methods to extract accurate and detailed urban land cover information from spaceborne PolSAR data. Six RADARSAT-2 fine-beam polarimetric SAR and three RADARSAT-2 ultra-fine beam SAR images were used. These data were acquired from June to September 2008 over the north urban-rural fringe of the Greater Toronto Area, Canada. The major landuse/land-cover classes in this area include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, roads, streets, parks, golf courses, forests, pasture, water and two types of agricultural crops. In this research, various polarimetric SAR parameters were evaluated for urban land cover mapping. They include the parameters from Pauli, Freeman and Cloude-Pottier decompositions, coherency matrix, intensities of each polarization and their logarithms.  Both object-based and pixel-based classification approaches were investigated. Through an object-based Support Vector Machine (SVM) and a rule-based approach, efficiencies of various PolSAR features and the multitemporal data combinations were evaluated. For the pixel-based approach, a contextual Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) and a modified Multiscale Pappas Adaptive Clustering (MPAC), contextual information was explored to improve the mapping results. To take full advantages of alternative PolSAR distribution models, a rule-based model selection approach was put forward in comparison with a dictionary-based approach.  Moreover, the capability of multitemporal fine-beam PolSAR data was compared with multitemporal ultra-fine beam C-HH SAR data. Texture analysis and a rule-based approach which explores the object features and the spatial relationships were applied for further improvement. Using the proposed approaches, detailed urban land-cover classes and finer urban structures could be mapped with high accuracy in contrast to most of the previous studies which have only focused on the extraction of urban extent or the mapping of very few urban classes. It is also one of the first comparisons of various PolSAR parameters for detailed urban mapping using an object-based approach. Unlike other multitemporal studies, the significance of complementary information from both ascending and descending SAR data and the temporal relationships in the data were the focus in the multitemporal analysis. Further, the proposed novel contextual analyses could effectively improve the pixel-based classification accuracy and present homogenous results with preserved shape details avoiding over-averaging. The proposed contextual SEM algorithm, which is one of the first to combine the adaptive MRF and the modified MPAC, was able to mitigate the degenerative problem in the traditional EM algorithms with fast convergence speed when dealing with many classes. This contextual SEM outperformed the contextual SVM in certain situations with regard to both accuracy and computation time. By using such a contextual algorithm, the common PolSAR data distribution models namely Wishart, G0p, Kp and KummerU were compared for detailed urban mapping in terms of both mapping accuracy and time efficiency. In the comparisons, G0p, Kp and KummerU demonstrated better performances with higher overall accuracies than Wishart. Nevertheless, the advantages of Wishart and the other models could also be effectively integrated by the proposed rule-based adaptive model selection, while limited improvement could be observed by the dictionary-based selection, which has been applied in previous studies. The use of polarimetric SAR data for identifying various urban classes was then compared with the ultra-fine-beam C-HH SAR data. The grey level co-occurrence matrix textures generated from the ultra-fine-beam C-HH SAR data were found to be more efficient than the corresponding PolSAR textures for identifying urban areas from rural areas. An object-based and pixel-based fusion approach that uses ultra-fine-beam C-HH SAR texture data with PolSAR data was developed. In contrast to many other fusion approaches that have explored pixel-based classification results to improve object-based classifications, the proposed rule-based fusion approach using the object features and contextual information was able to extract several low backscatter classes such as roads, streets and parks with reasonable accuracy. / <p>QC 20121112</p>

Nowadays, there exist two reporting regimes, rules-based and principle-based (comply-or-explain). In the rules-based environment, researchers have studied the relationship between internal control quality and accounting quality. Prior studies have suggested that reports on internal control are an effective way for investors to evaluate the quality of the firm‟s internal control. By having a sound system of internal control, it creates reliance upon the firm‟s financial reporting. Therefore, the condition of internal control has direct connection to the investors‟ decision making.  In  U.S., where the corporate governance has been known to be typically rules-based, most researches have shown a positive relationship between internal control reporting and accounting quality. In the comply-or-explain reporting regime, there has only been one research as far as we know, that investigates the relationship between internal control reporting and accounting quality. The research took place in Netherlands; their study provided positive association between internal control  reporting quality and accounting quality, particularly earnings quality. The result is consistent with other research in rules-based regime.    The Swedish code has  been enacted for  five years and it is in the same regime as Netherlands. However, under current Swedish code, we are not aware of any empirical research  that  has taken place to address the effectiveness of the code. Therefore, we conducted the study with 152 firms listed on Swedish NASDAQ OMXS to test internal control reporting quality and accounting quality. We used proxies for internal control reporting quality: effectiveness statement of  internal control, internal control reporting score and internal control proportion. Simultaneously, we use discretionary accruals as a proxy of accounting quality. Our result showed a negative significant relationship between internal control reporting score and discretionary accruals but this relationship is quite weak and sensitive. It is broken with the presence of one more variable in regression, such as size variable. The result could not give any evidence for relationships between either effectiveness statements or extent of internal control reporting with discretionary accruals. Although, the relationship between internal control reporting score and discretionary accruals is not tight, it still encourage the  notion  that firms with good internal control report will improve their accounting quality. Our research contributes knowledge to wide-range of concerned parties. Moreover, it also suggests valuable ideas for further studies in the same area.

Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, crowded public places and borders. The rise in computer speed, availability of cheap large-capacity storage devices and high speed network infrastructure enabled the way for cheaper, multi sensor video surveillance systems. In this thesis, the problem of tracking multiple targets with multiple cameras has been discussed. Cameras have been located so that they have overlapping fields of vision. A dynamic background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene changes and periodic motion, such as illumination change and swaying of trees. After segmentation of foreground scene, the objects to be tracked have been acquired by morphological operations and connected component analysis. For the purpose of tracking the moving objects, an active contour model (snakes) is one of the approaches, in addition to a Kalman tracker. As the main tracking algorithm, a rule based tracker has been developed first for a single camera, and then extended to multiple cameras. Results of used and proposed methods are given in detail.

An expert system is useful when conventional programming techniques fall short of capturing human expert knowledge and making decisions using this information. In this study, we describe a framework for capturing expert knowledge under a decision tree form and this framework can be used for making decisions based on captured knowledge. The framework proposed in this study is generic and can be used to create domain specific expert systems for different problems. Features are created or processed by the nodes of decision tree and a final conclusion is reached for each feature. Framework supplies 3 types of nodes to construct a decision tree. First type is the decision node, which guides the search path with its answers. Second type is the operator node, which creates new features using the inputs. Last type of node is the end node, which corresponds to a conclusion about a feature. Once the nodes of the tree are developed, then user can interactively create the decision tree and run the supplied inference engine to collect the result on a specific problem. The framework proposed is experimented with two case studies / &quot / Airport Runway Detection in High Resolution Satellite Images&quot / and &quot / Urban Area Detection in High Resolution Satellite Images&quot / . In these studies linear features are used for structural decisions and Scale Invariant Feature Transform (SIFT) features are used for testing existence of man made structures.

Wireless sensor networks are application-specific networks that necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. The most important challenge related to wireless sensor networks is the limited energy and computational resources of the battery powered sensor nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted and the total energy consumed by sensor nodes, but also produces scalable and fault-tolerant networks. Another important challenge associated with wireless sensor networks is that the possibility of sensory data being imperfect and imprecise is high. The requirement of precision necessitates employing expensive mechanisms such as redundancy or use of sophisticated equipments. Therefore, approximate computing may need to be used instead of precise computing to conserve energy. This thesis presents two schemes that distribute information processing for event-driven reactive applications, which are interested in higher-level information not in the raw sensory data of individual nodes, to appropriate nodes in sensor networks. Furthermore, based on these schemes, a fuzzy rule-based system is proposed that handles imprecision, inherently present in sensory data.

This paper main goal is to introduce the basic language and programming styles of the mathematical software Mathematica (5.2), and applies it to solve problems in algebra, calculus, linear algebra, probability and statistics. We hope readers can enhance the interest of learning mathematics by the aid of software, verify the common theorem using the software, and solving mathematical problems.

Situation awareness and collective intelligence are two technologies used in smart systems. The former renders those systems able to reason upon their abstract knowledge of what is going on. The latter enables them learning and deriving new information from a composition of experiences of their users. In this dissertation we present a doctoral research on an attempt to combine the two in order to obtain, in a collaborative fashion, situation-based rules that the whole community of entities would benefit of sharing. We introduce the KRAMER recommendation system, which we designed and implemented as a solution to the problem of not having decision support tools both situation-aware and collaborative. The system is independent from any domain of application in particular, in other words generic, and we apply its prototype implementation to context-enriched social communication scenario.

Spatial Decision Making as a branch of decision making science deals with geographically related data in order to achieve complex spatial decision problems. Fuzzy set theory is one of the methods that can be used to come up with these types of problems. On the other hand, Geographical Information Systems (GIS) is one of the most powerful tools that we can use to accomplish spatial decision problems. Selection of the suitable site or land-use for the real estate is also a spatial decision making problem. When we consider the initial dynamics of the suitably located property from the point of view of value and potential we observe that the &ldquo / good location&rdquo / is the dominating factor. This study reports on the development of a kind of decision support system for locational suitability of settlements that integrates the fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. This study is thought as the assistant for the property managers that are buyers and sellers. It can function as the property consultant for the buyers when they are looking for a property to buy and also it helps the real estate agencies to sell their properties. On the other hand, different scenarios of the potential areas according to the different user&rsquo / s preferences are depicted and they are joined and compared with the results of the vulnerability to earthquake hazards&rsquo / of the same area. Odunpazari - Eskisehir area is selected for implementation of the case study because of the data availability. As a result of this study, it can be said that most suitable property changes depending on the people&rsquo / s preferences. In addition, it is seen that most of the buildings that are locationally suitable are highly vulnerable to the earthquake hazards.

銀行個人金融中，信用卡產品的競爭相當激烈，而為了吸引持卡人消費，行銷人員也設計了許多的信用卡行銷優惠。現有銀行的資訊部門為了滿足新行銷方案的設計與行銷方案優惠的判斷，採取的作法是在現有的系統上不斷的新增外掛程式，來滿足新的需求，但這樣的作法會產生許多問題，每當一個新的行銷優惠方案產生後，就必須在現有系統上新增外掛，不但可能會危及現有系統的穩定度，同時這些外掛的系統會造成系統的複雜度，增加維護與修改的困難度與成本。 本研究運用邏輯符號以及物件導向的觀念，分析行銷人員設計信用卡行銷優惠方案，提出規則模型以及事實模型。規則模型提供了一套通用的邏輯符號架構，並將邏輯的表達透過XML的標準，轉化成電腦可處理的形式；而事實模型架構出通用的事實模版架構，提供在事實與實際資料庫對應上的處理標準。並透過以規則為基礎的推論機制，建構出實際的系統，來驗證研究的可行性。希望透過本研究的規則模型以及事實模型架構，將行銷人員的知識結構化與知識儲存，達到邏輯符號的可重複使用性與可延伸性，並提供了系統未來修改與延伸的彈性與準則，改善過去新增行銷優惠方案時，就必須增加外掛系統的問題。 / Competition for credit card is sharp in personal finance of banking and financial service industry. In order to attract customer to use credit card, the marketing staff designs lots of marketing campaigns. But for information system department of the bank, it takes more effort to manage and process these campaigns. The approach that the information system staff uses is to create lots of plug-in systems. But this approach harms the stability of the system architecture and increases the complexity of maintenance and cost. In this research, we use the concept of symbolic logic and object-oriented concept to analysis how the marketing staff designs the credit card campaigns and provide the rule model and fact model. The rule model provides standard symbolic logic architecture and uses the XML standard to transform logic symbols into RuleML. The fact model is a standard architecture and provides the mapping between facts and database. And we build a prototype rule-based system to validate the feasibility. The research contributes to construct and store the knowledge of credit card marketing campaign of the marketing staff by the rule model and the fact model. And the rule-based system in this research also provides flexibility and modifiability in the future to reform the drawback of original plug-in system approach.