Abstract The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user “pull” and “push” sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

Multiprocessor platforms have been widely applied in safety-critical domains to accommodate the increasing computation requirement of modern real-time applications. In this paper, we present a workload-aware harmonic partitioned multiprocessor scheduling scheme for periodic real-time tasks with constrained deadlines under the fixed-priority preemptive scheduling policy. In particular, two grouping metrics effectively integrating both harmonicity and workload characteristic are designed to guide our task partition. With those metrics, our scheme can greatly improve system utilization by taking advantage of the combination of harmonic relationship exploration and workload awareness. Experiments show that our proposed scheme significantly outperforms existing approaches in terms of schedulability.

Multiprocessor platforms, widely adopted to realize real-time systems nowadays, bring the probabilistic characteristic to such systems because of the performance variations of complex chips. In this paper, we present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic realtime tasks on multiprocessors under the fixed-priority preemptive scheduling policy. The key idea of this research is to improve the overall schedulability by strategically arranging the workload among processors based on the exploration of the harmonic relationship among probabilistic real-time tasks. In particular, we define a harmonic index to quantify the harmonicity among probabilistic real-time tasks. This index can be obtained via the harmonic period transformation and probabilistic cumulative worst case utilization calculation of these tasks. The proposed scheduling scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task assignment. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over the existing harmonic partitioned scheduling algorithm for probabilistic real-time systems.

D numbers has been previously introduced in linguistic decision making due to its effectiveness and flexibility in dealing with uncertain information. The study applies the integration operator of D numbers to obtain the decision result by aggregating different opinions of experts which may be imprecise and uncertain. However, sometimes it would be more reasonable to consider the risk preference of the decision maker. In this paper, we proposed an improved aggregating method for linguistic information based on D numbers and OWAWA operator. The main advantage is that it can integrate the degree of importance that each experts has and the risk preference of the decision maker in the aggregation of linguistic information. An example is used to demonstrate the flexibility and reasonability of the proposed method.

Spam, also known as unsolicited bulk e-mail (UBE), has recently become a serious threat that negatively impacts the usability of legitimate mails. In this article, an evidential spam-filtering framework is proposed. As a useful tool to handle uncertainty, the Dempster–Shafer theory of evidence (D–S theory) is integrated into the proposed approach. Five representative features from an e-mail header are analyzed. With a machine-learning algorithm, e-mail headers with known classifications are used to train the framework. When using the framework for a given e-mail header, its representative features are quantified. Although in classical probability theory, possibilities are forcedly assigned even when information is not adequate, in our approach, for every word in an e-mail subject, basic probability assignments (BPA) are assigned in a more flexible way, thus providing a more reasonable result. Finally, BPAs are combined and transformed into pignistic probabilities for decision-making. Empirical trials on real-world datasets show the efficiency of the proposed framework.

Dempster's rule of combination can only be used when the bodies of evidence are assumed to be independent. However, such an assumption is often unrealistic. This paper proposes a systematic approach to handle dependence in evidence theory. It includes both the representation of dependence among information sources and the aggregation of the dependent evidence. For the representation of the dependence, the proposed methodology is able to capture both inner dependence (i.e., dependence among features of a system) and outer dependence (i.e., dependence among the evidence sources during the information propagating and evaluating process). We suggest dealing with the inner dependence by applying the analytic network process model, and modeling the outer dependence based on the intersection situations of the identified influencing factors. Then for the combination of dependent evidence, the strategy is to use discounting aggregation where the discounting coefficients are related to the degree of both outer and inner dependence. The discounting operator helps reduce the duplicate calculations in the fusion of dependent evidence and relax the assumption of independence when using Dempster's rule. A case study of transportation project evaluation is used to illustrate the proposed methodology.