Alleviating Naive Bayes attribute independence assumption by attribute weighting

Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in other machine learning algorithms, use weighting to place more emphasis on highly predictive attributes than those that are less predictive. In this paper, we argue that for naive Bayes attribute weighting should instead be used to alleviate the conditional independence assumption. Based on this premise, we propose a weighted naive Bayes algorithm, called WANBIA, that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions. We perform extensive evaluations and find that WANBIA is a competitive alternative to state of the art classifiers like Random Forest, Logistic Regression and A1DE. © 2013 Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman and Geoffrey I. Webb.This research has been supported by the Australian Research Council under grant DP110101427 and Asian Office of Aerospace Research and Development, Air Force Office of Scientific Research under contract FA23861214030. The authors would like to thank Mark Hall for providing the code for CFS and MH. The authors would also like to thank anonymous reviewers for their insightful comments that helped improving the paper tremendously.Peer Reviewe

FlexQueue: Simple and Efficient Priority Queue for System Software

Existing studies of priority queue implementations often focus on improving canonical operations such as insert and deleteMin, while sacrificing design simplicity and pre- dictable worst-case latency. Design simplicity is sacrificed as the algorithm becomes more and more optimized, taking into account characteristics of the input workload distribution. Predictable worst-case latency is sacrificed when operations such as memory allocation and structural re-organization are deferred until absolutely necessary. While these techniques often yield performance improvement to some degree, it is possible to take a step back and ask a more basic question: is it possible to achieve similar performance while retaining a simple design? By combining techniques such as hierarchical bit-vector and dynamic horizon resizing, all of which are straight-forward in principle, this thesis presents a new priority queue design called FlexQueue, that answers this question with a definitive “yes”

Genetic correlation between milk urea nitrogen and reproductive performance in seasonal grazing dairy cows : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science (Animal Science) at Massey University, Palmerston North, New Zealand

These copyrighted Figures were removed: 2.2 (=Bach et al., 2005 Fig 1); 2.5 (=Johnson and Young, 2003 Fig 1); 2.7 (=Grosshans et al., 1997 Fig 1); 2.8 (=McNamara et al., 2008 Fig 3; 2.9 (=Jordan et al., 1983 Fig 6) & 2.10 (=Butler, 1998 Fig 4).Milk urea nitrogen (MUN) concentration is closely related to blood urea nitrogen concentration and is an indicator of the level of crude protein ingested by the cow. Studies with cows in indoor systems have reported an antagonistic phenotypic relationship between MUN and fertility traits, but no studies have reported estimates of genetic correlation between MUN and fertility traits in pasture-based systems. The objective of this study was to estimate genetic parameters for MUN concentration, fertility and production traits in New Zealand dairy grazing cows from two herds. Milk test records were collected from 637 cows from once-a-day and twice-a-day milking dairy herds from Massey University, Palmerston North, New Zealand, during the production seasons of 2016-17 and 2017-18. The average concentration of MUN ranged between 8.2 and 11.4 mg/dL with estimates of heritability and repeatability of 0.24 (0.09 SE) and 0.45 (0.05 SE). There were no significant heterosis nor breed effect for MUN. The estimates of genetic correlations between MUN and fertility traits (submission and pregnancy rate during the first 21 days after the start of mating) were moderate negative (-0.55 and -0.45), but the standard errors of the estimates were large due to the small data set. The phenotypic correlations were close to zero. The estimates of genetic parameters indicate that MUN concentration in milk can be reduced by genetic selection with a potential to increase the submission and pregnancy rate during the first 21 days after the start of mating, which are the two most important reproductive traits in grazing dairy cows. Further studies with a larger dataset would enable more accurate estimates of the genetic parameters

階層型ピア・ツー・ピアファイル検索のための負荷管理の研究

In a Peer-to-Peer (P2P) system, multiple interconnected peers or nodes contribute a portion of their resources (e.g., files, disk storage, network bandwidth) in order to inexpensively handle tasks that would normally require powerful servers. Since the emergency of P2P file sharing, load balancing has been considered as a primary concern, as well as other issues such as autonomy, fault tolerance and security. In a process of file search, a heavily loaded peer may incur a long latency or failure in query forwarding or responding. If there are many such peers in a system, it may cause link congestion or path congestion, and consequently affect the performance of overall system. To avoid such situation, some of general techniques used in Web systems such as caching and paging are adopted into P2P systems. However, it is highly insufficient for load balancing since peers often exhibit high heterogeneity and dynamicity in P2P systems. To overcome such a difficulty, the use of super-peers is currently being the most promising approach in optimizing allocation of system load to peers, i.e., it allocates more system load to high capacity and stable super-peers by assigning task of index maintenance and retrieval to them. In this thesis, we focused on two kinds of super-peer based hierarchical architectures of P2P systems, which are distinguished by the organization of super-peers. In each of them, we discussed system load allocation, and proposed novel load balancing algorithms for alleviating load imbalance of super-peers, aiming to decrease average and variation of query response time during index retrieval process. More concretely, in this thesis, our contribution to load management solutions for hierarchical P2P file search are the following: • In Qin’s hierarchical architecture, indices of files held by the user peers in the bottom layer are stored at the super-peers in the middle layer, and the correlation of those two bottom layers is controlled by the central server(s) in the top layer using the notion of tags. In Qin’s system, a heavily loaded super-peer can move excessive load to a lightly loaded super-peer by using the notion of task migration. However, such a task migration approach is not sufficient to balance the load of super-peers if the size of tasks is highly imbalanced. To overcome such an issue, in this thesis, we propose two task migration schemes for this architecture, aiming to ensure an even load distribution over the super-peers. The first scheme controls the load of each task in order to decrease the total cost of task migration. The second scheme directly balances the load over tasks by reordering the priority of tags used in the query forwarding step. The effectiveness of the proposed schemes are evaluated by simulation. The result of simulations indicates that all the schemes can work in coordinate, in alleviating the bottleneck situation of super-peers. • In DHT-based super-peer architecture, indices of files held by the user peers in the lower layer are stored at the DHT connected super-peers in the upper layer. In DHT-based super-peer systems, the skewness of user’s preference regarding keywords contained in multi-keyword query causes query load imbalance of super-peers that combines both routing and response load. Although index replication has a great potential for alleviating this problem, existing schemes did not explicitly address it or incurred high cost. To overcome such an issue, in this thesis, we propose an integrated solution that consists of three replication schemes to alleviate query load imbalance while minimizing the cost. The first scheme is an active index replication in order to decrease routing load in the super-peer layer, and distribute response load of an index among super-peers that stored the replica. The second scheme is a proactive pointer replication that places location information of an index, for reducing maintenance cost between the index and its replicas. The third scheme is a passive index replication that guarantees the maximum query load of super-peers. The result of simulations indicates that the proposed schemes can help alleviating the query load imbalance of super-peers. Moreover, by comparison it was found that our schemes are more cost-effective on placing replicas than other approaches.広島大学(Hiroshima University)博士(工学)Doctor of Engineering in Information Engineeringdoctora

CROSS-STACK PREDICTIVE CONTROL FRAMEWORK FOR MULTICORE REAL-TIME APPLICATIONS

Many of the next generation applications in entertainment, human computer interaction, infrastructure, security and medical systems are computationally intensive, always-on, and have soft real time (SRT) requirements. While failure to meet deadlines is not catastrophic in SRT systems, missing deadlines can result in an unacceptable degradation in the quality of service (QoS). To ensure acceptable QoS under dynamically changing operating conditions such as changes in the workload, energy availability, and thermal constraints, systems are typically designed for worst case conditions. Unfortunately, such over-designing of systems increases costs and overall power consumption. In this dissertation we formulate the real-time task execution as a Multiple-Input, Single- Output (MISO) optimal control problem involving tracking a desired system utilization set point with control inputs derived from across the computing stack. We assume that an arbitrary number of SRT tasks may join and leave the system at arbitrary times. The tasks are scheduled on multiple cores by a dynamic priority multiprocessor scheduling algorithm. We use a model predictive controller (MPC) to realize optimal control. MPCs are easy to tune, can handle multiple control variables, and constraints on both the dependent and independent variables. We experimentally demonstrate the operation of our controller on a video encoder application and a computer vision application executing on a dual socket quadcore Xeon processor with a total of 8 processing cores. We establish that the use of DVFS and application quality as control variables enables operation at a lower power op- erating point while meeting real-time constraints as compared to non cross-stack control approaches. We also evaluate the role of scheduling algorithms in the control of homo- geneous and heterogeneous workloads. Additionally, we propose a novel adaptive control technique for time-varying workloads

Network analysis of large scale object oriented software systems

PhD ThesisThe evolution of software engineering knowledge, technology, tools, and practices has seen progressive adoption of new design paradigms. Currently, the predominant design paradigm is object oriented design. Despite the advocated and demonstrated benefits of object oriented design, there are known limitations of static software analysis techniques for object oriented systems, and there are many current and legacy object oriented software systems that are difficult to maintain using the existing reverse engineering techniques and tools. Consequently, there is renewed interest in dynamic analysis of object oriented systems, and the emergence of large and highly interconnected systems has fuelled research into the development of new scalable techniques and tools to aid program comprehension and software testing. In dynamic analysis, a key research problem is efficient interpretation and analysis of large volumes of precise program execution data to facilitate efficient handling of software engineering tasks. Some of the techniques, employed to improve the efficiency of analysis, are inspired by empirical approaches developed in other fields of science and engineering that face comparable data analysis challenges. This research is focused on application of empirical network analysis measures to dynamic analysis data of object oriented software. The premise of this research is that the methods that contribute significantly to the object collaboration network's structural integrity are also important for delivery of the software system’s function. This thesis makes two key contributions. First, a definition is proposed for the concept of the functional importance of methods of object oriented software. Second, the thesis proposes and validates a conceptual link between object collaboration networks and the properties of a network model with power law connectivity distribution. Results from empirical software engineering experiments on JHotdraw and Google Chrome are presented. The results indicate that five considered standard centrality based network measures can be used to predict functionally important methods with a significant level of accuracy. The search for functional importance of software elements is an essential starting point for program comprehension and software testing activities. The proposed definition and application of network analysis has the potential to improve the efficiency of post release phase software engineering activities by facilitating rapid identification of potentially functionally important methods in object oriented software. These results, with some refinement, could be used to perform change impact prediction and a host of other potentially beneficial applications to improve software engineering techniques

Scheduling and locking in multiprocessor real-time operating systems

With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due to priority inversion can be bounded a priori. Several spinlock- and semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes of schedulability analysis, namely suspension-oblivious and suspension-aware analysis, that yield two different lower bounds on blocking. Five asymptotically optimal locking protocols are designed and analyzed: a family of mutex, RW, and k-exclusion protocols for global, partitioned, and clustered scheduling that are asymptotically optimal in the suspension-oblivious case, and a mutex protocol for partitioned scheduling that is asymptotically optimal in the suspension-aware case. A LITMUSRT-based empirical evaluation is presented that shows these protocols to be practical

DHash table

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 123-132) and index.DHash is a new system that harnesses the storage and network resources of computers distributed across the Internet by providing a wide-area storage service, DHash. DHash frees applications from re-implementing mechanisms common to any system that stores data on a collection of machines: it maintains a mapping of objects to servers, replicates data for durability, and balances load across participating servers. Applications access data stored in DHash through a familiar hash-table interface: put stores data in the system under a key; get retrieves the data. DHash has proven useful to a number of application builders and has been used to build a content-distribution system [31], a Usenet replacement [115], and new Internet naming architectures [130, 129]. These applications demand low-latency, high-throughput access to durable data. Meeting this demand is challenging in the wide-area environment. The geographic distribution of nodes means that latencies between nodes are likely to be high: to provide a low-latency get operation the system must locate a nearby copy of the data without traversing high-latency links.(cont.) Also, wide-area network links are likely to be less reliable and have lower capacities than local-area network links: to provide durability efficiently the system must minimize the number of copies of data items it sends over these limited capacity links in response to node failure. This thesis describes the design and implementation of the DHash distributed hash table and presents algorithms and techniques that address these challenges. DHash provides low-latency operations by using a synthetic network coordinate system (Vivaldi) to find nearby copies of data without sending messages over high-latency links. A network transport (STP), designed for applications that contact a large number of nodes, lets DHash provide high throughput by striping a download across many servers without causing high packet loss or exhausting local resources. Sostenuto, a data maintenance algorithm, lets DHash maintain data durability while minimizing the number of copies of data that the system sends over limited-capacity links.by Frank Dabek.Ph.D

On the design of efficient caching systems

Content distribution is currently the prevalent Internet use case, accounting for the majority of global Internet traffic and growing exponentially. There is general consensus that the most effective method to deal with the large amount of content demand is through the deployment of massively distributed caching infrastructures as the means to localise content delivery traffic. Solutions based on caching have been already widely deployed through Content Delivery Networks. Ubiquitous caching is also a fundamental aspect of the emerging Information-Centric Networking paradigm which aims to rethink the current Internet architecture for long term evolution. Distributed content caching systems are expected to grow substantially in the future, in terms of both footprint and traffic carried and, as such, will become substantially more complex and costly. This thesis addresses the problem of designing scalable and cost-effective distributed caching systems that will be able to efficiently support the expected massive growth of content traffic and makes three distinct contributions. First, it produces an extensive theoretical characterisation of sharding, which is a widely used technique to allocate data items to resources of a distributed system according to a hash function. Based on the findings unveiled by this analysis, two systems are designed contributing to the abovementioned objective. The first is a framework and related algorithms for enabling efficient load-balanced content caching. This solution provides qualitative advantages over previously proposed solutions, such as ease of modelling and availability of knobs to fine-tune performance, as well as quantitative advantages, such as 2x increase in cache hit ratio and 19-33% reduction in load imbalance while maintaining comparable latency to other approaches. The second is the design and implementation of a caching node enabling 20 Gbps speeds based on inexpensive commodity hardware. We believe these contributions advance significantly the state of the art in distributed caching systems