11919242
Detecting and Classifying Emotion in Popular Music
Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.
12971843
Smooth Playout Control for Video Streaming over Error-Prone Channels
The quality of media streaming over best-effort networks suffers from network delays and packet losses. The latter is more profound for wireless video. To enhance the QoS of streaming services, adaptive media playout (AMP) has been developed to adjust the playout interval. With AMP, the risk of delay and buffer underflow is reduced. However, the smoothness of playback is not guaranteed. In this paper, we propose a novel AMP control that enables smooth playout and meanwhile maintains reliable visual quality. Our AMP control adjusts the playout interval based on an estimation of channel quality, so it is more adaptive than conventional AMP controls that are based on buffer fullness. Experimental results are provided to justify our approach. Even at 20% packet loss rate, the proposed AMP control is still able to provide smooth and reliable playback
7655844
Music emotion classification: a fuzzy approach
Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.
16965968
Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems
While structured P2P systems (such as DHTs) are often regarded as an improvement over unstructured P2P systems (such as super-peer networks) in terms of routing efficiency, it is not clear which architecture is better for full text search. This paper provides a quantitative comparison of full text keyword search in structured and unstructured P2P systems. We examine three techniques (and optimizations to those techniques) proposed in the literature: using a DHT along with inverted lists and Bloom filters; using a super-peer network; and using a random walk over an unstructured network. We use real Web documents and user queries to measure the cost for both document publishing and query processing, in terms of bandwidth and response time. Our results show that all three techniques use roughly the same bandwidth to process queries (with the super-peer technique having a slight edge). The structured network provides the best response time (30 percent better than a super-peer network), but has a high cost of document publishing, using six times as much bandwidth as the super-peer system. The random walk technique requires no publishing, but has a very long response time unless multiple random walks operate in parallel.
15260186
Using recursive classification to discover predictive features
Finding most predictive features for statistical classification is a challenging problem and has important applications. Support Vector Machines (SVMs), for example, have been found successful with a recursive procedure in selecting most important genes for cancer prediction. It is not well understood, however, how much the success depends on the choice of the classifier, and how much on the recursive procedure. We answer this question by examining multiple classifiers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings, on a DNA microarray dataset (AMLALL) and a text categorization benchmark (Reuters-21578). We found recursive ridge regression most effective: its best classification performance (zero error) on the AMLALL dataset was obtained when using only 3 genes (selected from over 7000), which is more impressive than the best published result on the same benchmark - zero error of recursive SVM using 8 genes. On Reuters-21578, recursive ridge regression also achieves the best result ever published (the improvement was verified in a significance test). An in-depth analysis of the experimental results shows that the choice of classifier heavily influences the recursive feature selection process: the ridge regression classifier tends to penalize redundant features to a much larger extent than the SVM does.
39342835
Guaranteed Mutually Consistent Checkpointing in Distributed Computations
In this paper, we explore the isomorphism between vector time and causality to characterize consistency of a set of checkpoints in a distributed computing. A necessary and sufficient condition, to determine if a set of local checkpoints can form a consistent global checkpoint, is presented and proved using the isomorphic power of vector time and causality. To the best of our knowledge, this is the first attempt to use the isomorphism for this purpose. This condition leads to a simple and straightforward algorithm for a guaranteed mutually consistent global checkpointing. In our approach, a process can take a checkpoint whenever and wherever it wants while other related process may be asked to take an additional checkpoint for ensuring the mutual consistency. We also show how this condition and the resulting algorithm can be used to obtain a maximum and minimum global checkpoints, another important paradigm for distributed applications.
4914546
Multiple temporal consistency states for dynamical verification of upper bound constraints in grid workflow systems
Conventional upper bound constraint verification in grid workflow systems is based on the key assumption that an upper bound constraint only has two states: consistency and inconsistency. However, due to complexity of grid workflows and dynamic availability of participating grid services, this assumption is too restrictive as there may be some intermediate states. Therefore, in this paper, we introduce four states for an upper bound constraint. Namely, we treat conventional consistency as strong consistency and divide conventional inconsistency into weak consistency, weak inconsistency and strong inconsistency. Correspondingly, we develop their verification methods. For weak consistency, we present some algorithms on how to adjust it to strong consistency without triggering exception handling as in conventional work. For weak inconsistency, we analyse why it can rely on less costly exception handling than conventional work. The final evaluation demonstrates that our four-state approach can achieve better cost-effectiveness than the conventional two-state approach
1466008
Analyzing the Intel Itanium Memory Ordering Rules Using Logic Programming and SAT
We present a non-operational approach to specifying and analyzing shared memory consistency models. The method uses higher order logic to capture a complete set of ordering constraints on execution traces, in an axiomatic style. A direct encoding of the semantics with a constraint logic programming language provides an interactive and incremental framework for exercising and verifying finite test programs. The framework has also been adapted to generate equivalent boolean satisfiability (SAT) problems. These techniques make a memory model specification executable, a powerful feature lacked in most non-operational methods. As an example, we provide a concise formalization of the Intel Itanium memory model and show how constraint solving and SAT solving can be effectively applied for computer aided analysis. Encouraging initial results demonstrate the scalability for complex industrial designs.