329318
The garbage collection advantage: improving program locality
As improvements in processor speed continue to outpace improvements in cache and memory speed, poor locality increasingly degrades performance. Because copying garbage collectors move objects, they have an opportunity to improve locality. However, no static copying order is guaranteed to match program traversal orders. This paper introduces <i>online object reordering</i> (OOR) which includes a new dynamic, online class analysis for Java that detects program traversal patterns and exploits them in a copying collector. OOR uses run-time method sampling that drives just-in-time (JIT) compilation. For each <i>hot</i> (frequently executed) method, OOR analysis identifies the hot field accesses. At garbage collection time, the OOR collector then copies referents of hot fields together with their parent. Enhancements include static analysis to exclude accesses in cold basic blocks, heuristics that decay heat to respond to phase changes, and a separate space for hot objects. The overhead of OOR is on average negligible and always less than 2% on Java benchmarks in Jikes RVM with MMTk. We compare program performance of OOR to static class-oblivious copying orders (e.g., breadth and depth first). Performance variation due to static orders is often low, but can be up to 25%. In contrast, OOR matches or improves upon the best static order since its history-based copying tunes memory layout to program traversal.
1106182
A visualization system for multithreaded programming
Almost all modern operating systems, from Windows to Unix, support multithreaded programming. To make sure our students can lead the trend of computer science in the foreseeable future, we need to introduce them to this important technology. However, we have found through experience in teaching multithreaded programming that the paradigm shift from sequential to multithreaded causes students significant problems, such as (1) multithreaded program development requires a new mindset, (2) multithreaded program behavior is dynamic, making debugging very difficult, and (3) proper synchronization is more difficult than anticipated [10, 11]. Moreover, detecting race conditions and deadlocks is more easily said than done.
30724966
Motivational System for Human-Robot Interaction
Human-Robot Interaction (HRI) has recently drawn increased attention. Robots can not only passively receive information but also actively emit actions. We present a motivational system for human-robot interaction. The motivational system signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. No salient feature is predefined in the motivational system but instead novelty based on experience, which is applicable to any task. Novelty is defined as an innate drive. Reinforcer is integrated with novelty. Thus, the motivational system of a robot can be developed through interactions with trainers. We treat vision-based neck action selection as a behavior guided by the motivational system. The experimental results are consistent with the attention mechanism in human infants.
10948786
Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles
Learning from imbalanced datasets is inherently difficult due to lack of information about the minority class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique with an ensemble of SVMs to improve the prediction performance. The integrated sampling technique combines both over-sampling and undersampling techniques. Through empirical study, we show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers.
18862916
Clustering graphs for visualization via node similarities
Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks.
6189075
NodeRank: A New Structure Based Approach to Information Filtering
9414412
Concept-based biomedical text retrieval
One challenging problem for biomedical text retrieval is to find accurate synonyms or name variants for biomedical entities. In this paper, we propose a new concept-based approach to tackle this problem. In this approach, a set of concepts instead of keywords will be extracted from a query first. Then these concepts will be used for retrieval purpose. The experiment results show that the proposed approach can boost the retrieval performance and it generates very good results on 2005 TREC Genomics data sets.