The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.

Transitional Region Phenomenon leads to the existence of lossy links in wireless networks, which results in a transmission between two users who are theoretically connected under the Deterministic Network Model cannot be guaranteed. Therefore, we focus on a more practical network model - Probabilistic Network Model (PNM) which can better characterize the lossy links in wireless networks. To be specific, we focus on the investigation of accelerating data aggregation process in probabilistic wireless networks with the cognitive radio technology. By involving cognitive radio technology, users in the wireless networks can seek extra transmission opportunity if other spectrum resource is available. Otherwise, the data aggregation process still can be done on the default working spectrum. Particularly, we are interested in the time efficient data aggregation scheduling problem. In this work, a two phase scheduling algorithm is proposed. The first phase is finding an efficient routing structure considering the speciality of the network model under investigation. In the second phase, a dynamic scheduling algorithm is introduced. Theoretical analysis is provided to estimate the lower latency bound for the scheduling algorithm, followed by the experimental simulation verification.

Biological frequent patterns usually correspond to the important function (or structure) in biological sequences. Along with the rapid growth of biological sequences, it is significant to find frequent patterns over a large bio-sequence efficiently. However, most of existing algorithms need to produce lots of short patterns or projected databases, which influence the efficiency badly and also increase the cost of space. Graphics processing units (GPUs) embracing many core computing devices, have been extensively applied to accelerate computation performance in many areas. In order to meet the demand of biologists, we redefine the frequent pattern problem with length constraints for finding frequent patterns. We present pruning optimization method for the serial algorithm (POSA), and based on this technique, we propose a parallel algorithm (POPA) which not only reduces the time complexity with a low space cost but also obtains better performance on CUDA. To validate the presented algorithms, we implemented the algorithms on multiple-core CPU and various GPU devices. Also, CUDA optimization techniques are applied to speed up calculation in the paper. Finally, experimental results show that compared with the serial algorithm on CPU with six cores, POSA achieves 1.2~4.5 speedup, and POPA gains 3~20 speedup.

The repetitions in biological sequence analysis are of great biological significance. Finding the repetitions has been a hot topic in gene projects naturally. In recent years, graphics processing unit (GPU) has been far exceeded the CPU in terms of computing capability and memory bandwidth, especially CUDA dramatically increases in computing performance by harnessing the power of the GPUs. This paper proposes efficient parallel algorithms on CUDA to accelerate finding PTRs which is redefined as LPRs based on the SUA Index. The proposed parallel algorithms have been utilized with the parallel primitives offered by Thrust library and the effective parallel bit compression technology based on division to achieve better acceleration. Optimization techniques include CUDA streams technology are also realized to reduce transmission latency. Experimental results show that the proposed parallel algorithms are faster than the benchmark with 1.6∼5.4 speedup.

The complete area coverage problem in Wireless Sensor Networks (WSNs) has been extensively studied in the literature. However, many applications do not require complete coverage all the time. For such applications, one effective method to save energy and prolong network lifetime is to partially cover the area. This method for prolonging network lifetime recently attracts much attention. However, due to the hardness of verifying the coverage ratio, all the existing centralized or distributed but nonparallel algorithms for partial coverage have very high time complexities. In this work, we propose a framework which can transform almost any existing complete coverage algorithm to a partial coverage one with any coverage ratio by running a complete coverage algorithm to find full coverage sets with virtual radii and converting the coverage sets to partial coverage sets via adjusting sensing radii. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.

Area query processing is significant for various applications of wireless sensor networks. No previous study has specifically addressed this issue. We can adopt a naive method, which is to send all data to base station for centralized processing. However, this method wastes a large amount of energy for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the whole monitored area is partitioned into grids, and a gray code is used to represent a grid ID (GID), which is a smart way to describe an area. Furthermore, a reporting tree is constructed to process merging areas and aggregations. Based on the properties of GIDs, useless data can be dropped and areas can be merged as early as possible. Incremental update is used to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is energy-efficient.

Wireless sensor networks (WSNs) are employed in many applications in order to collect data. One key challenge is to minimize energy consumption to prolong network lifetime. A scheme of making some nodes asleep and estimating their values according to the other active nodespsila readings has been proved energy-efficient. For the purpose of improving the precision of estimation, we propose two powerful estimation models, data estimation using physical model (DEPM) and data estimation using statistical model (DESM). DEPM estimates the values of sleeping nodes by the physical characteristics of sensed attributes, while DESM estimates the values through the spatial and temporal correlations of the nodes. Experimental results on real sensor networks show that the proposed techniques provide accurate estimations and conserve energy efficiently.

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customerspsila privacy. In this paper, we consider the problem of preserving customerpsilas privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (sliding window anonymization framework), to solve this problem by continuously facilitating k-anonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.

Due to the limited power supplies of a wireless node, energy efficiency is a crucial aspect to the design of a broadcast protocol. In the minimum energy broadcast problem, each node adjusts its transmission power to minimize the total energy consumption. The minimum energy broadcast problem is proved to be NP-Complete. The Hitch-hiking model introduced recently in [M. Agarwal et al., (2004)] takes advantage of the physical layer to combine partial signals containing the same data in order to decode a complete message. Moreover, the wireless multicast advantage (WMA), that is a single transmission can be received by all the nodes that are within the transmission range of a transmitting node, reduces the total energy of the broadcast tree. In this paper, we take advantages of both Hitch-hiking and WMA to design an energy-efficient broadcast tree algorithm with Hitch-hiking (BHH). The simulation results show that BHH reduces the total energy of the broadcast tree greatly.