An effective algorithm to detect both smoke and flame using color and wavelet analysis

Fire detection is an important task in many applications. Smoke and flame are two essential symbols of fire in images. In this paper, we propose an algorithm to detect smoke and flame simultaneously for color dynamic video sequences obtained from a stationary camera in open space. Motion is a common feature of smoke and flame and usually has been used at the beginning for extraction from a current frame of candidate areas. The adaptive background subtraction has been utilized at a stage of moving detection. In addition, the optical flow-based movement estimation has been applied to identify a chaotic motion. With the spatial and temporal wavelet analysis, Weber contrast analysis and color segmentation, we achieved moving blobs classification. Real video surveillance sequences from publicly available datasets have been used for smoke detection with the utilization of our algorithm. We also have conducted a set of experiments. Experiments results have shown that our algorithm can achieve higher detection rate of 87% for smoke and 92% for flame.     

A speech content authentication algorithm based on a novel watermarking method

Some audio watermark schemes robust against desynchronization attacks are based on synchronization code embedded by quantifying signal energy, which have some shortcomings. Such as, (1) they do not verify the authenticity of watermarked signal detected. (2) They are vulnerable to substitution attack. To address the shortcomings and considering the background, a speech content authentication algorithm is proposed in this paper. Firstly, the original speech signal is framed, and each frame is cut into some segments. Secondly, samples of the segments are scrambled, and self-correlation of the scrambled signal is calculated. Lastly, watermark bit generated by frame number is embedded by quantifying the self-correlation. If watermarked signal is attacked, the attacked frames can be detected according to the frame number extracted. Theoretical analysis and experiments demonstrate that the scheme is robust against desynchronization attacks, improves the security, and has a good performance in ability of tampering location.     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Top-<Emphasis Type="Italic">k</Emphasis> temporal keyword search over social media data

Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over social media data mainly emphasizes on the recency of information. In this paper, we first propose a problem of top-k most significant temporal keyword query to enable more complex query analysis. It returns top-k most popular social items that contain the keywords in the given query time window. Then, we design a temporal inverted index with two-tiers posting list to index social time series and a segment store to compute the exact social significance of social items. Next, we implement a basic query algorithm based on our proposed index structure and give a detailed performance analysis on the query algorithm. From the analysis result, we further refine our query algorithm with a piecewise maximum approximation (PMA) sketch. Finally, extensive empirical studies on a real-life microblog dataset demonstrate the combination of two-tiers posting list and PMA sketch achieves remarkable performance improvement under different query settings.     

Discovery of probabilistic nearest neighbors in traffic-aware spatial networks

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of discovering probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid potential time delay/traffic congestions. We propose and study four novel probabilistic TANN queries. Thereinto two queries target at minimizing the travel time, including a congestion-probability threshold query, and a time-delay threshold query, while another two travel-time threshold queries target at minimizing the potential time delay/traffic congestion. We believe that TANN queries are useful in many real applications, such as discovering nearby points of interest and planning convenient travel routes for users, and location based services in general. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process these TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop four efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.     

Efficient schemes for similarity-aware refinement of aggregation queries

Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.     

Hashtag-based topic evolution in social media

The rise of online social media has led to an explosion of metadata-containing user generated content. The tracking of metadata distribution is essential to understand social media. This paper presents two statistical models that detect interpretable topics over time along with their hashtags distribution. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags, i.e., the hashtag distribution. The models combine a context with a related topic by jointly modeling words with hashtags and time. Experiments with real-world datasets demonstrate that the proposed models discover topics over time with related contexts effectively.     

Application placement in computer clustering in software as a service (SaaS) networks

One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.