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Multiplatform advertising (MPA) is recognized as an effective means of enhancing marketing revenue. In the context, we refer to the scheme of dynamically allocating the advertising expenditure among the selected media platforms as an MPA policy, and we refer to the problem of developing an MPA policy with maximum benefit as the MPA problem. This article is devoted to the solution of the MPA problem. An evolutionary model for the expected market state, in which the influence of both advertising and word-of-mouth (WOM) propagation is accounted for, is established. On this basis, the expected benefit of an MPA policy is calculated. Thereby, the MPA problem is reduced to an optimal control problem we refer to as the MPA model, where the objective functional stands for the expected benefit of an MPA strategy. The optimality system for the MPA model is derived. We refer to the MPA policy obtained by solving the optimality system as the promising MPA policy. The structure of the promising MPA policy is inspected. Through extensive comparative experiments, it is concluded that the promising MPA policy is superior to the majority of MPA policies in terms of expected benefit. Finally, how the expected benefit of the promising MPA policy is influenced by some factors is investigated.

A rumor about an entity can spread rapidly through online social networks (OSNs), which may lead to serious consequences. This article focuses on developing a cost-effective rumor-refuting strategy in the situation where the rumormonger is strategic. Based on a novel individual-based rumor spreading model, we estimate the expected net benefit of the rumormonger as well as the expected total loss of the rumor victim. On this basis, we reduce the original problem to a differential game-theoretic model. Next, we derive a system for solving the model. By solving the system, we get a dynamic strategy pair. Through extensive comparative experiments, we find that this strategy pair is effective in terms of the solution concept of Nash equilibrium. Hence, we conclude that the rumor-refuting strategy contained in this strategy pair is cost-effective. Finally, we examine the effect of the structure of the OSN on the cost effectiveness of the rumor-supporting and rumor-refuting strategies contained in the proposed strategy pair. To our knowledge, this is the first time the rumor-refuting problem is addressed under the assumption that the rumormonger is strategic.

Advanced persistent threat (APT) as a generic highly sophisticated cyber attack poses a severe threat to organizational data security. Since the conventional detection and repair (DAR)-based APT defense mechanism has several conspicuous drawbacks, it is imperative to develop a more effective and efficient APT defense mechanism. Based on the data backup and recovery (DBAR) techniques developed in the field of disaster recovery, we propose a novel APT defense mechanism referred to as DBAR-based APT defense mechanism, which can overcome the main drawbacks of the DAR-based APT defense mechanism and is expected to be implementable efficiently in the software-defined networking (SDN) paradigm. Under the new mechanism, we study the problem of finding a cost-effective DBAR strategy. Based on a novel dynamic model characterizing the evolution of the expected security status of the organizational network, we reduce the problem to a differential game-theoretic problem, which is aimed to seek a cost-effective DBAR strategy in terms of the Nash equilibrium solution concept. Next, we derive the optimality system of the problem. Extensive comparative experiments show that the DBAR strategy obtained from the optimality system is cost-effective in the sense of Nash equilibrium solution concept.

The vision system of a fruit picking robot must perform two difficult tasks, the accurate pixel-wise instance segmentation of fruit and the correct detection of picking points. Due to the unsatisfactory performance of the vision system on these tasks, the picking performance of the robot is insufficient and its application is not prevalent. In this study, performing fruit instance segmentation and the localization of picking points, a vision algorithm using RGB images was designed in a deep learning framework for a potential visual system of mango picking robots. The algorithm not only performed these two tasks successfully, but also integrated them into an end-to-end network, in which parallel branches performed the two tasks simultaneously. The average precision (at IoU of 0.75) and the average recall of instance segmentation reached 0.947 and 0.929 respectively, and the best precision and recall of picking-point detection reached 0.984 and 0.908 respectively. In addition, the tasks face various illumination and background interference in outdoor orchards, along with complex problems in terms of occlusion, overlap and object scale. In this study, the performance of the vision system was analysed in detail on several datasets and subsets of the various complex conditions, and the major factors that affect the performance were discussed. The results demonstrated that this system was robust against various illuminations and complex backgrounds, and yielded satisfactory segmentation and picking-point detection performances for minor and medium occlusion or overlap, and for medium and large mangoes. The model visualization and the influence analysis of model training demonstrated the training process and modelling effect of the deep learning network.

The advanced persistent threat (APT) as a new kind of cyber attack has posed a severe threat to modern organizations. When the APT has been detected, the organization has to deal with the APT response problem, i.e., to allocate the available response resources to fix her insecure hosts so as to mitigate her potential loss. This paper addresses the APT response problem by using the risk management approach. First, we introduce a model characterizing the evolution of the organization's expected state. By analyzing this model, we find the organization's expected state approaches a common limit expected state. Then, we use the organization's expected loss per unit time to measure her potential loss, and we find this measure is determined by the organization's limit expected state. On this basis, we model the APT response problem as a game-theoretic problem (the APT response game) in which the organization seeks a Nash equilibrium. We present a greedy algorithm for solving the game. Comparative experiments show that the algorithm is effective. Therefore, we recommend the response strategy generated by performing the algorithm. These findings contribute to defending against the APT. To our knowledge, this is the first time the APT response problem is addressed.

The automatic detection of postures and posture changes in sows using a computer-vision system has substantial potential for learning their maternal abilities, enhancing their welfare and productivity, and reducing the crushing risk to piglets. The objectives of this study are to (1) detect frame-level sow postures, (2) temporally localise posture change actions, and (3) generate spatio-temporally action tubes parsed from a long-time untrimmed segment of depth video. Depth videos were recorded for five batches of lactating sows, using a Kinect from a top-view in a commercial farm. Three batches were used for training and validation, and the other two for testing. Four postures (standing, sitting, ventral lying, and lateral lying) were automatically detected, with a mean average-precision (mAP) of 0.927. The localisation performance of the clip-level mAP involved eight posture change actions, and achieved 0.774 in the temporal intersection over union (tIoU) ≥ 0.5. A tube optimisation algorithm was used to optimise and smooth the action tubes. When the mean IoU≥0.8 in the tube, the performance of the video-level mAP significantly improved, from 0.313 to 0.796. The error analysis could deepen the understanding of the causes of errors in action detection. The system was applied to test two day videos of various sows, by obtaining the regularity of posture change probability, comparing the action characteristics, and discerning the maternal differences of the sows. The methodology can be applied in large-scale deployments for learning livestock action preferences and behavioural traits, thereby enhancing welfare and productivity on a farm.

The aim of this study was to develop a general and automatic recognition framework for recognising the daily behaviours of lactating sows to save manual labour and promote smart management. The proposed framework used both image analysis techniques in still image and motion analysis techniques in spatiotemporal videos to recognise sow drinking, feeding, nursing, moving, medium active and inactive behaviours in a loose pen. The image analysis techniques, which are based on fully convolutional networks (FCNs) for high-accuracy segmentation, were used to extract spatial features that evaluated the spatial relationships between objects and the appearance of sows. The motion analysis techniques in spatiotemporal videos, which are based on optical flow analysis and changes in the animal centroid, were used to extract temporal features that evaluated the temporal motions of the animals. In the recognition process, these spatial and temporal features were input into a hierarchical classifier for behaviour recognition. The final recognition results were obtained by a temporal-correlation-based correction module for promoting the recognition rate. Testing on 26 h of videos (468,000 frames) of 3 sows, the algorithm realised the following accuracies of behavioural classification compared with the manual observations: 97.49% for drinking, 95.36% for feeding, and 88.09% for nursing. In addition, the amounts of time that the sow spent on the considered behaviours in the daytime (from 8:00 to 17:00), were as follows: 69.34% on inactive behaviours, 14.50% on nursing, 8.38% on medium activities, 4.04% on feeding, 2.26% on drinking and 1.48% on moving. Hence, the proposed method provides an effective approach for the automatic recognition of sow behaviours from video sequences, which facilitates the pig farmer in improving livestock-farming management.

With the advent of Internet-of-Things (IoT) age, IoT-based distributed denial-of-service (DDoS) attacks have become the mainstream of DDoS attacks. This paper is devoted to exploring the possibility of launching an IoT-based DDoS attack at an extremely low cost. First, a new DDoS attack architecture is proposed. Since the proposed architecture enjoys the advantages of zero management cost, good undetectability, and strong robustness, it is especially suited to resource-limited DDoS attackers. In this architecture and based on a novel botnet growth model, the optimal design of attack strategy is reduced to a variational problem, where the objective functional stands for the estimated expected impact of the DDoS attack associated with a DDoS attack strategy. Finally, the variational problem is resolved for three different types of DDoS defense strategies. This work deepens our understanding of IoT-based DDoS attacks.

This paper proposes an end-to-end refined two-stream RGB-D Faster region convolutional neural network (R-CNN) algorithm, which fuses RGB-D image features in the feature extraction stage for recognising five postures of lactating sows (standing, sitting, sternal recumbency, ventral recumbency, and lateral recumbency) in scenes at a pig farm. Based on the Faster R-CNN algorithm, two CNNs were first used to extract the RGB image features and depth image features. Then, a proposed single RGB-D region proposal network was used to generate the regions of interest (ROIs) for the two types of image feature maps in RGB-D. Next, the features of the RGB-D ROIs were extracted and merged using a feature fusion layer. Finally, the fused features of the RGB-D ROIs were input into a Fast R-CNN to obtain the recognition results. A total of 12,600 pairs of RGB-D images of five postures were obtained by a Kinect v2.0 sensor and were randomly selected from the first 21 of 28 pens as the training set, and 5533 pairs were randomly selected from the remaining 7 pens as the test set. The proposed method was used to recognise the five postures of lactating sows. The recognition accuracy of the concatenation fusion method was the highest for the test set with average precisions for the five categories of lactating sow postures of 99.74%, 96.49%, 90.77%, 90.91%, and 99.45%, respectively. Compared with related methods (RGB-only method, depth-only method, RGB-D early fusion, and later fusion), our method attained the highest mean average precision.

Malicious code has posed a severe threat to modern society. Delivering antivirus program to networks is an important task of a cybersecurity company. As the bandwidth resource in a company is limited and precious, cybersecurity companies have to make a tradeoff between the impact(i.e. the economic loss) of malicious codes and the bandwidth assigned to transmit the antivirus programs. This paper addresses the malicious code and bandwidth tradeoff(MCBT) problem. By developing a novel malicious code and antivirus program interacting model, the total loss, which is the sum of the bandwidth usage fee and the economic loss, is quantified. On this basis, the MCBT problem is modelled as a constrained optimization problem that we refer to as the MCBT model, where the independent variable stands for bandwidth, and the objective function stands for the total loss. Some optimal bandwidth is determined by solving the MCBT model. Based on this, we propose a heuristic algorithm named DOWNHILL, which outperforms random strategies. Finally, the influence of some factors on the optimal bandwidth and the corresponding optimal total loss is uncovered through numerical simulations. To our knowledge, this is the first time the MCBT problem is treated in this way.

This paper addresses the issue of discount pricing in word-of-mouth (WOM) marketing. First, we propose a novel discount strategy we refer to as the Influence-Based Discount (IBD) strategy, in which each buyer would enjoy a discount that is linearly proportional to his/her influence in the WOM network. For the purpose of evaluating the performance of an IBD strategy, we model the associated WOM spreading process as a higher-dimensional nonlinear differential dynamical system we refer to as the Dormant-Potential-Adopting (DPA) model. Next, we examine the dynamics of a DPA model through computer experiments. Thereby, we estimate the expected net profit of an IBD strategy and inspect how different factors influence this profit through numerical experiments. On this basis, we suggest some feasible promotional measures.

To restrain escalating computer viruses, new virus patches must be constantly injected into networks. In this scenario, the patch-developing cost should be balanced against the negative impact of virus. This article focuses on seeking best-balanced patch-injecting strategies. First, based on a novel virus-patch interactive model, the original problem is reduced to an optimal control problem, in which (a) each admissible control stands for a feasible patch-injecting strategy and (b) the objective functional measures the balance of a feasible patch-injecting strategy. Second, the solvability of the optimal control problem is proved, and the optimality system for solving the problem is derived. Next, a few best-balanced patch-injecting strategies are presented by solving the corresponding optimality systems. Finally, the effects of some factors on the best balance of a patch-injecting strategy are examined. Our results will be helpful in defending against virus attacks in a cost-effective way.

Advanced persistent threat (APT) for data theft poses a severe threat to cloud storage systems (CSSs). An APT actor may steal valuable data from the target CSS even in a strategic fashion. To protect a CSS from APT, the cloud defender has to dynamically allocate the limited security resources to recover the compromised storage servers, aiming at mitigating his total loss. This paper addresses this dynamic cloud storage recovery (DCSR) problem by employing differential game theory. First, by introducing an expected state evolution model capturing the CSS's expected state evolution process under a combination of attack strategy and recovery strategy, we measure the APT attacker's net benefit and the cloud defender's total loss. On this basis and in the worst-case situation where the cloud defender assumes that the APT attacker has full knowledge of his expected loss, we reduce the DCSR problem to a differential game-theoretic problem (the DCSR* problem) to characterize the strategic interactions between the two parties. Second, we derive a necessary condition for Nash equilibrium of the DCSR* problem and thereby introduce the concept of competitive strategy profile. Next, we study the structural properties of the competitive strategy profile, followed by some numerical examples. Then, we conduct extensive comparative experiments to exhibit that the competitive strategy profile is superior to a large number of randomly generated strategy profiles in the sense of Nash equilibrium solution concept. Finally, we briefly analyze the practicability (scalability and feasibility) of this paper. Our findings will be helpful to enhance the APT defense capabilities of the cloud defender.

Travel/residence time distributions (TTDs/RTDs) are important tools to evaluate the vulnerability of catchments to contamination and understand many aspects of catchment function and behavior. In recent years, the calculation of TTDs/RTDs based on the Lagrangian particle tracking approach together with the integrated hydrologic modeling has become a popular counterpart to analytical approaches and lumped numerical models. As global water availability becomes more stressed due to anthropogenic disturbance and climate change, the requirement of large-scale and long-term simulations for TTDs/RTDs further pushes the high computational costs of Lagrangian particle tracking. Hence, speeding up the Lagrangian particle tracking approach becomes an important barrier to advancement. In this study, we accelerate the Lagrangian particle tracking program EcoSLIM, using a combination of distributed (e.g. MPI) and multi-core accelerator (CUDA) approaches for large-scale and long-term simulations. EcoSLIM was developed to be seamlessly paired with the integrated ParFlow.CLM model for calculations of transient RTDs and source water mixing and was originally developed using threaded OpenMP. This work extends this implementation to compare combinations of MPI, CUDA and OpenMP. Of these combinations, the OpenMP-CUDA parallelism performed the best moving from single-GPU to multi-GPU. The multi-GPU shows strong scalability which becomes increasingly efficient with more particles, demonstrating a potential feasibility for regional-scale, transient residence time simulations. This work largely improves the computational capability of EcoSLIM, and results also show the advantages of using GPU-parallel to traditional parallel-APIs (application programming interfaces) and its potential to widely accelerate the next generation programs in subsurface environment modeling.

IEEE Advanced persistent threat (APT) for cyber espionage poses a great threat to modern organizations. In order to mitigate the impact of APT on an organization, all the compromised systems in the organization must be quarantined and recovered in a timely and effective way. This article focuses on the problem of customizing a dynamic quarantine and recovery (QAR) scheme for an organization so that the APT impact is minimized. Based on a novel node-level epidemic model characterizing the effect of the QAR scheme on the expected state of the underlying network, we estimate the expected impact of APT under a QAR scheme. On this basis, we model the original problem as an optimal control problem. By use of optimal control theory, we derive the optimality system for the optimal control problem and thereby introduce the concept of normal potential optimal (NPO) control. Next, through comparative experiments, we find that the NPO control outperforms a set of heuristic controls. Hence, the QAR scheme associated with the NPO control is satisfactory in terms of the effectiveness of defending against APT. Finally, we examine the effect of some factors on the expected APT impact under the NPO control. This article would be helpful to the defense against APT for cyber espionage.

Rumors have been widely spread in online social networks and they become a major concern in modern society. This paper is devoted to the design of a cost-effective rumor-containing scheme in online social networks through an optimal control approach. First, a new individual-based rumor spreading model is proposed, and the model considers the influence of the external environment on rumor spreading for the first time. Second, the cost-effectiveness is recommended to balance the loss caused by rumors against the cost of a rumor-containing scheme. On this basis, we reduce the original problem to an optimal control model. Next, we prove that this model is solvable, and we present the optimality system for the model. Finally, we show that the resulting rumor-containing scheme is cost-effective through extensive computer experiments.

The aim is to develop a computer vision-based method for automatic recognition of nursing interactions under commercial farm conditions by using spatial and temporal information of nursing behaviour. For spatial information extraction, the spatial distribution between the mother sow and her piglets during nursing was used to detect the possible nursing episodes. Sows were segmented accurately by a fully convolutional network, and udder zones were calculated dynamically by the geometrical properties of the nursing sow and the piglet length. Spatial information from piglets was extracted in the self-adaptive udder zones. For temporal information extraction, to distinguish behaviours similar to nursing, temporal motion information about the intensity of motion and the occupation index was extracted from optical flow of the udder zones. Six sows with 64 piglets, each on different days postpartum, were captured on videos. From these videos, 507 episodes of videos of two sows were selected as training set and 502 episodes of another two sows were used as test set. The accuracy, sensitivity and specificity achieved 96.4%, 92.0% and 98.5%, respectively. In addition, our method was used to recognise the nursing behaviour in four extended videos of the two remaining sows. The accuracy achieved reached 97.6% with a sensitivity of 90.9% and with a specificity of 99.2%. The results show that the recognition method designed represents a significant step forward in automatically identifying nursing behaviour in commercial pig farms.

This paper addresses the discount pricing in word-of-mouth (WOM) marketing. First, a dynamic model capturing WOM spreading processes is suggested. Second, the problem of finding an optimal discount strategy boils down to an optimal control problem. Third, the existence of an optimal control for the control problem is proved, and an optimality system for finding an optimal control is presented. Thereby, the dynamic discount strategy associated with the optimal control is recommended. Some examples of the optimal control are given. Finally, the influence of different factors on the optimal expected net profit is examined.

False rumors can lead to huge economic losses or/and social instability. Hence, mitigating the impact of bogus rumors is of primary importance. This paper focuses on the problem of how to suppress a false rumor by use of the truth. Based on a set of rational hypotheses and a novel rumor-truth mixed spreading model, the effectiveness and cost of a rumor-containing strategy are quantified, respectively. On this basis, the original problem is modeled as a constrained optimization problem (the RC model), in which the independent variable and the objective function represent a rumor-containing strategy and the effectiveness of a rumor-containing strategy, respectively. The goal of the optimization problem is to find the most effective rumor-containing strategy subject to a limited rumor-containing budget. Some optimal rumor-containing strategies are given by solving their respective RC models. The influence of different factors on the highest cost effectiveness of a RC model is illuminated through computer experiments. The results obtained are instructive to develop effective rumor-containing strategies.

The behaviors of livestock on farms are the primary representatives of animal welfare, health conditions and social interactions. Measuring behavior quantitatively in an automatic detection system on computer vision provides valuable behavioral information in an efficient and noninvasive way compared with manual observations or sensing techniques. Lactating sow postures, which are the crucial indicator of maternal evaluation, provide fundamental information for studying the maternal behavioral characteristics and regularities. We introduce a detector, Faster R-CNN, on deep learning framework to identify five postures (standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency) and obtain sows accurate location in loose pens. The detection system consists of a Kinect v2 sensor that acquires depth images and a program that identifies sow postures and locates its bounding-boxes. The depth images of testing dataset of a sow were acquired at 5 frames per second in 24 h on the 15th day of postpartum, and training dataset were collected by some different sows. Since the identification performance from RGB images are impacted by the color and illumination variations caused by in-situ heat lamp and day-night cycle, we show that the automatic detection from depth images could avoid disturbances of the light. We find that the sow spent greater amount of time in recumbency (92.9% at night and 84.1% during the daytime) as compared with standing (0.4% at night and 10.5% during the daytime) and sitting (0.55% at night and 3.4% during the daytime). Statistically, the sow’s activity level is non-uniform in 24-h of a day, and her preferred lying positions is accordant with the pen’s floor design. The posture’s change frequency and average duration are presented. From the estimated general manner of posture change, we find that the sow takes more time in descending body than ascending, which could be a favorable indication of maternal ability with a slow-motion falling to avoid crushing piglets.

As compared to the traditional advertising, word-of-mouth (WOM) communications have striking advantages such as significantly lower cost and much faster propagation, and this is especially the case with the popularity of online social networks. This paper focuses on the modeling and analysis of the WOM marketing. A dynamic model, known as the SIPNS model, capturing the WOM marketing processes with both positive and negative comments is established. On this basis, a measure of the overall profit of a WOM marketing campaign is proposed. The SIPNS model is shown to admit a unique equilibrium, and the equilibrium is determined. The impact of different factors on the equilibrium of the SIPNS model is illuminated through theoretical analysis. Extensive experimental results suggest that the equilibrium is much likely to be globally attracting. Finally, the influence of different factors on the expected overall profit of a WOM marketing campaign is ascertained both theoretically and experimentally. Thereby, some promotion strategies are recommended. To our knowledge, this is the first time the WOM marketing is treated in this way.

© 2018 IEEE. To fight against the evolving computer viruses, we must constantly inject new virus patches into the computer networks. This paper addresses the patch injection problem, i.e., the problem of developing a patch injection strategy to mitigate the negative impact of virus attacks. As the impact of an attack depends on not only the patch injection strategy but the unknown virus injection strategy, the patch injection problem is very complicated. This paper initiates the study of the patch injection problem by means of security economics and differential game theory. First, based on a novel virus-patch mixed propagation model, we model the original problem as a differential game. Second, we develop a method for finding a candidate for the Nash equilibrium of the game, examine the structure of the candidate, and give some examples of the candidate. Furthermore, we demonstrate through comparative experiments that the candidate is better in terms of the Nash equilibrium solution concept. Therefore, we recommend the patch injection strategy in the candidate. Finally, we examine the effects of some factors on the performance of the recommended patch injection strategy. Overall, these findings undoubtedly have guiding significance to defense against virus infections.

Automated detection of sow nursing behavior is beneficial to the health, welfare, and productivity of sows and piglets in the commercial swine industry. We proposed a fast and accurate detection method for sow nursing behavior using CNN-based optical flow and features. The detection method has two major steps. In the first step, a spatial localization network consisting of a sow detector and a keypoint detector was developed in order to localize the nursing zone. In the second step, a nursing behavior recognition network was developed to detect sow nursing behaviors from untrimmed videos. To achieve this, a CNN-based optical flow estimator was used to produce an optical flow within the nursing zone. RGB images of the detected nursing zone and the corresponding optical flow images were then fed into the recognition network in sequence in order to capture spatiotemporal information. In our study, four separate convolutional neural networks were trained. 11.7 h of videos collected from 19 pens in sow farms were used to train the model, and 24.7 h of other videos from 8 pens were tested for validation. The results revealed that the proposed method achieved an accuracy of 97.63%, a sensitivity of 98.55%, a specificity of 97.36% and a detection speed of 6.36 fps from untrimmed videos. Compared with our previous method, this method has a superior performance in terms of accuracy and sensitivity. In addition, this method has a 7.45% smaller model size, yet with a 176.52% faster detection speed, which demonstrates the feasibility of automated monitoring of nursing behavior.

There are plenty of conflicting messages in online social networks. This paper addresses the competition of two conflicting messages. Based on a novel individual-level competing spreading model (the generic UABU model), three criteria for one or two messages to terminate are presented. These criteria manifest the influence of the two message-spreading networks on the evolution of the two messages. Extensive computer simulations show that when a message terminates, the dynamics of a simplified UABU model (the linear UABU model) fits well with the expected evolutionary process of the message. These findings help in understanding the competing spreading process of two conflicting messages.

To assess the prevalence of disruptive computer viruses in the situation that every node in a network has its own virus-related attributes, a heterogeneous epidemic model is proposed. A criterion for the global stability of the virus-free equilibrium and a criterion for the existence of a unique viral equilibrium are given, respectively. Furthermore, extensive simulation experiments are conducted, and some interesting phenomena are found from the experimental results. On this basis, some policies of suppressing disruptive viruses are recommended.

Alerting at the early stage of malware invasion turns out to be an important complement to malware detection and elimination. This paper addresses the issue of how to dynamically contain the prevalence of malware at a lower cost, provided alerting is feasible. A controlled epidemic model with alert is established, and an optimal control problem based on the epidemic model is formulated. The optimality system for the optimal control problem is derived. The structure of an optimal control for the proposed optimal control problem is characterized under some conditions. Numerical examples show that the cost-efficiency of an optimal control strategy can be enhanced by adjusting the upper and lower bounds on admissible controls.

Disruptive computer viruses have inflicted huge economic losses. This paper addresses the development of a cost-effective dynamic control strategy of disruptive viruses. First, the development problem is modeled as an optimal control problem. Second, a criterion for the existence of an optimal control is given. Third, the optimality system is derived. Next, some examples of the optimal dynamic control strategy are presented. Finally, the performance of actual dynamic control strategies is evaluated.

Advanced persistent threats (APTs) pose a grave threat to cyberspace, because they deactivate all the conventional cyber defense mechanisms. This paper addresses the issue of evaluating the security of the cyber networks under APTs. For this purpose, a dynamic model capturing the APT-based cyber-attack-defense processes is proposed. Theoretical analysis shows that this model admits a globally stable equilibrium. On this basis, a new security metric known as the equilibrium security is suggested. The impact of several factors on the equilibrium security is revealed through theoretical analysis or computer simulation. These findings contribute to the development of feasible security solutions against APTs.

Nowadays, online social media have become an integral part of people daily life. To provide high-quality contents, a variety of recommender systems have been applied to social platforms. On the framework of epidemic theory, in this paper we present a novel node-level rumor propagation model that takes into account the influence of recommender systems. With the proposed model, we perform a series of numerical simulations to investigate the dynamics of rumor propagation. The result shows that with a recommendation mechanism rumors can fast spread through a social network. Also, we examine the effects of some factors in the proposed model. This paper provides theoretical insight into the influence of recommender systems on rumor propagation.

© 2005-2012 IEEE. Advanced persistent threat (APT) is a new kind of cyberattack that poses a serious threat to modern society. When an APT campaign on an organization has been identified, the available repair resources must be reasonably allocated to the potentially insecure hosts to mitigate the potential loss of the organization. We refer to the feasible repair resource allocation strategies as repair strategies. This paper focuses on the APT repair problem, i.e., the problem of developing effective repair strategies for organizations. First, for an organization with time-varying communication relationship, we establish an evolution model of the organization's expected state, in which the impact of lateral movement of APT is accommodated. On this basis, we model the APT repair problem as a differential Nash game problem (the APT repair game) in which the attacker attempts to maximize his potential benefit, and the organization manages to minimize its potential loss. Second, we derive a system (the potential system) for calculating a potential Nash equilibrium of an APT repair game, and we examine the structure of the potential attack and repair strategies in a potential Nash equilibrium. Next, we solve some potential systems to get the corresponding potential Nash equilibria. Finally, by comparison with a large number of randomly generated attack and repair strategies, we conclude that the potential Nash equilibrium of each APT repair game is a Nash equilibrium of the game. Therefore, we recommend to organizations their respective potential repair strategies. Our findings help to better understand and effectively defend against APT.

Viral marketing, known also as word-of-mouth marketing, is to use consumer-to-consumer communications to disseminate information about a product or service. Firms must persuade consumers, through their personal network and with the support of the Internet to become the vehicles which can disseminate the positive information. This paper addresses the modeling and analysis of the viral marketing for a consistent set of items. We develop a node-level positive comments mixed spreading model with positive comments and discount pricing mechanism, and thereby we measure the profit of the marketing. Next, we show that the model admits an optimal control, and we present the optimality system for solving the model. Finally, we give some optimal strategies by solving the corresponding optimality systems.

The dangerous body movements of lactating sows are mainly reflected in the frequency and duration of posture changes. To avoid the disturbances from the light environment and illumination variations caused by heat lamp and day-night cycle, depth videos were acquired for posture change detection. In this paper, a CNN-based method was developed to automatically detect sow posture change in untrimmed depth videos. Firstly, clip-level Detect and Classify Posture R-CNN (DCP R-CNN) was proposed for sow detection and posture classification. The DCP R-CNN decoupled the detection and classification task into two branches: detection branch and classification branch so that the detection branch could focus on the task of sow localization. Secondly, a method was proposed to temporally localize the possible posture changing segments from coarse to fine. Finally, a two-stream posture change classification network, which used two CNNs to extract the complementary features from DMM and the first and the last frames, was introduced to classify the possible posture changing segments into change or non-change. At the scene of commercial piggery, the videos were acquired at 5 frames per second (fps) by installing a Microsoft Kinect v2.0 sensor above the pen. The test set consisted of two 24-hour untrimmed depth videos and six 40-minute untrimmed depth videos. The test results showed that our method could detect the posture change correctly. The success rate of DCP R-CNN was 96.38% when the IoU threshold was 0.7, and the average precision and recall rate of 4 types of posture were 97.72% and 95.23%, respectively. The possible posture change was detected with a recall rate of 92.46% and the posture change was classified with an accuracy of 97.71%. The precision of posture change detection was 88.75%, the recall rate was 90.87%. The detection speed of our method was 5.71 fps, which was faster than the video frame rate (5 fps). Our study demonstrated the feasibility of 24-hour detection method for sow posture change.

This paper is intended to investigate the effect of network topology on the spread of computer viruses in the presence of removable storage media. For that purpose, a novel network-based computer virus spreading model is proposed. Both theoretically and experimentally, it is shown that, under proper conditions, viruses on a scale-free network would tend to extinction. Experimental results show that either smaller maximum node degree or larger power-law exponent is conducive to the containment of virus spreading. To our knowledge, this is the first time the effect of network topology on virus spreading is investigated in this context.

Multiple numerical approaches have been developed to simulate porous media fluid flow and solute transport at the pore scale. These include 1) methods that explicitly model the three-dimensional geometry of pore spaces and 2) methods that conceptualize the pore space as a topologically consistent set of stylized pore bodies and pore throats. In previous work we validated a model of the first type, using computational fluid dynamics (CFD) codes employing a standard finite volume method (FVM), against magnetic resonance velocimetry (MRV) measurements of pore-scale velocities. Here we expand that validation to include additional models of the first type based on the lattice Boltzmann method (LBM) and smoothed particle hydrodynamics (SPH), as well as a model of the second type, a pore-network model (PNM). The PNM approach used in the current study was recently improved and demonstrated to accurately simulate solute transport in a two-dimensional experiment. While the PNM approach is computationally much less demanding than direct numerical simulation methods, the effect of conceptualizing complex three-dimensional pore geometries on solute transport in the manner of PNMs has not been fully determined. We apply all four approaches (FVM-based CFD, LBM, SPH and PNM) to simulate pore-scale velocity distributions and (for capable codes) nonreactive solute transport, and intercompare the model results. Comparisons are drawn both in terms of macroscopic variables (e.g., permeability, solute breakthrough curves) and microscopic variables (e.g., local velocities and concentrations). Generally good agreement was achieved among the various approaches, but some differences were observed depending on the model context. The intercomparison work was challenging because of variable capabilities of the codes, and inspired some code enhancements to allow consistent comparison of flow and transport simulations across the full suite of methods. This study provides support for confidence in a variety of pore-scale modeling methods and motivates further development and application of pore-scale simulation methods.

Lattice Boltzmann method (LBM) is widely adopted in simulating flow and reactive transport in porous media due to its easy treatment of boundaries and high computational efficiency. There have been various boundary schemes developed in the LBM due to their vital roles when dealing with fluid-solid interface. In the present work, we aim to review and intercompare all the single-node boundary schemes (including the existing schemes and our newly-developed schemes) in the LBM through a series of numerical experiments from simple benchmarking cases to realistic applications. Both the accuracy and computational efficiency of all the schemes are evaluated and analyzed. Among the existing schemes, it is known that the bounce-back scheme (BB) is commonly employed at the boundaries in the LBM. However, the BB is of only first-order spatial accuracy when zero-velocity is not exactly located in the middle of the fluid and solid nodes, which cannot be guaranteed in porous media flows due to the complex pore structures. Therefore, fine grids are usually generated to obtain satisfactory results yet computationally expensive. Recently, a second-order single-node boundary scheme (SSN), which is more flexible than the BB while retaining the locality, has been proposed (Zhao et al, J. Comput. Phys., 329:1-15, 2017 Zhao and Yong (2017)). Unfortunately, the SSN fails to provide a viscosity-independent permeability, i.e., the predicted permeability is related to the fluid viscosity, which is unphysical. Based on the SSN, we first developed a magic second-order single-node boundary scheme (MSSN) to overcome this shortcoming based on the Chapman-Enskog analysis of the two-relaxation-time lattice Boltzmann equation (TRT-LBE) model. Comparative study of the BB, SSN/MSSN, and another single-node boundary scheme, i.e., the improved bounce-back scheme (IBB)/the magic improved bounce-back scheme (MIBB) is then performed both theoretically and numerically. Numerical simulations of porous media flows in both two and three dimensions confirm that the BB, MIBB, and MSSN can obtain the viscosity-independent permeability, while the IBB and SSN fail to do so. First of all, as benchmarking tests, flow through a 2D fracture is studied using different schemes, the results of which show that the IBB and SSN cannot provide viscosity-independent permeability while such drawbacks can be overcome by the BB/MIBB/MSSN. Furthermore, simulations of flows through a hexagonal array of cylinders and a 3D sphere packing demonstrate that the MSSN is more accurate than both the BB and MIBB. Finally, for porous media samples generated from the micro-CT images where the locations of the boundaries are not exactly known, the MSSN is more flexible to locate the positions of the fluid-solid interface than the BB and MIBB, which in turn can provide more accurate descriptions of the fluid flow. The current intercomparison study will benefit the community by providing comprehensive review and evaluation of the performance when using different single-node boundary schemes in the LBM.

Rumors can spread very rapidly through online social networks (OSNs), leading to huge impact on human society. Pushing anti-rumor messages to OSN users proves to be an effective approach to mitigating the impact of rumors. This paper focuses on the problem of finding a cost-effective anti-rumor message-pushing scheme. First, based on a node-level rumor spreading model with anti-rumor message-pushing mechanism, we estimate the cost effectiveness of an anti-rumor message-pushing scheme and thereby model the original problem as an optimal control problem. Second, we show that the optimal control problem admits an optimal control. Thirdly, we derive the optimality system for the optimal control problem. Finally, we find that a control obtained by solving the optimality system is cost-effective. This work contributes to containing the prevalence of rumors in a cost-effective way.

In order to capture the weak reflection signals in the sea non-cooperative detection system using Beidou satellite signals, the paper studied three existing weak signal accumulation techniques and summarized their advantages and disadvantages. In order to make use of the advantages of Differentially Coherent Integration and reduce computational complexity, the paper optimizes the detection of Differentially Coherent Integration and proposes the algorithm of Envelope Replaced Differentially Coherent Integration (ERDCI). Through the analysis of its detection probability and false alarm probability, the proposed algorithm is superior to the original algorithm. The new algorithm is less dependent on hardware and is suitable for use scenarios with limited hardware conditions.

In satellite-borne Terahertz wave ground detection situation, the quantitative estimation of Terahertz wave atmospheric absorption attenuation loss, especially with designated satellite position and down-looking angle information, has always been fundamental and a key technology application for various Terahertz communication modes, such as wideband & high-speed network, interstellar communication, satellite-ground station link, stratosphere aero-craft communication, long distance vast data transfer, short range wireless security communication etc. This paper designed an atmospheric absorption loss estimation software on satellite global THz wave ground detection. The realized functions of this software including scene establishment, basic functions and calculation methods were explained in detail. Finally, the monthly change calculation results of satellite ground detection with 0.34THz working band in 10°–90° down-looking angle are given.

This article addresses the tradeoff between the losses caused by a new virus and the size of the team for developing an antivirus against the virus. First, an individual-level virus spreading model is proposed to capture the spreading process of the virus before the appearance of its natural enemy. On this basis, the tradeoff problem is modeled as a discrete optimization problem. Next, the influences of different factors, including the infection force, the infection function, the available manpower, the alarm threshold, the antivirus development effort and the network topology, on the optimal team size are examined through computer simulations. This work takes the first step toward the tradeoff problem, and the findings are instructive to the decision makers of network security companies.

Advanced persistent threat (APT) as a new form of cyber attack has posed a severe threat to modern organizations. When an APT has been detected, the target organization has to develop a response resource allocation strategy to mitigate her potential loss. This paper suggests a risk management approach to solving this APT response problem. First, we present three state evolution models. Thereby we assess the organization’s potential loss. On this basis, we propose two kinds of game-theoretic models of the APT response problem. This work initiates the study of the APT response problem.

The new cyberattack pattern of advanced persistent threat (APT) has posed a serious threat to modern society. This paper addresses the APT defense problem, that is, the problem of how to effectively defend against an APT campaign. Based on a novel APT attack-defense model, the effectiveness of an APT defense strategy is quantified. Thereby, the APT defense problem is modeled as an optimal control problem, in which an optimal control stands for a most effective APT defense strategy. The existence of an optimal control is proved, and an optimality system is derived. Consequently, an optimal control can be figured out by solving the optimality system. Some examples of the optimal control are given. Finally, the influence of some factors on the effectiveness of an optimal control is examined through computer experiments. These findings help organizations to work out policies of defending against APTs.

Continuum-scale models, which employ a porous medium conceptualization to represent properties and processes averaged over a large number of solid grains and pore spaces, are widely used to study subsurface flow and reactive transport. Recently, pore-scale models, which explicitly resolve individual soil grains and pores, have been developed to more accurately model and study pore-scale phenomena, such as mineral precipitation and dissolution reactions, microbially-mediated surface reactions, and other complex processes. However, these highly-resolved models are prohibitively expensive for modeling domains of sizes relevant to practical problems. To broaden the utility of pore-scale models for larger domains, we developed a hybrid multiscale model that initially simulates the full domain at the continuum scale and applies a pore-scale model only to areas of high reactivity. Since the location and number of pore-scale model regions in the model varies as the reactions proceed, an adaptive script defines the number and location of pore regions within each continuum iteration and initializes pore-scale simulations from macroscale information. Another script communicates information from the pore-scale simulation results back to the continuum scale. These components provide loose coupling between the pore- and continuum-scale codes into a single hybrid multiscale model implemented within the SWIFT workflow environment. In this paper, we consider an irreversible homogeneous bimolecular reaction (two solutes reacting to form a third solute) in a 2D test problem. This paper is focused on the approach used for multiscale coupling between pore- and continuum-scale models, application to a realistic test problem, and implications of the results for predictive simulation of mixing-controlled reactions in porous media. Our results and analysis demonstrate that the hybrid multiscale method provides a feasible approach for increasing the accuracy of subsurface reactive transport simulations.

In this paper, a new computer virus propagation model, which incorporates the effects of removable storage media and antivirus software, is proposed and analyzed. The global stability of the unique equilibrium of the model is independent of system parameters. Numerical simulations not only verify this result, but also illustrate the influences of removable storage media and antivirus software on viral spread. On this basis, some applicable measures for suppressing virus prevalence are suggested.

Fault tolerance is especially important for interconnection networks, vastly influencing the performance of the parallel processing systems underlying the corresponding networks. This paper studies the fault tolerance of radix-k n-dimensional hypermesh optical interconnection networks, determines the connectivity of partial hypermesh, and derives the conditional connectivity of hypermesh provided that each adjacent set cannot be faulty simultaneously. Under this condition, the hypermesh networks can tolerate up to 2n(k-1)-k-1 fault processors without being disrupted, implying that when the number of dimension n (respectively, radix-k) is a fixed value in the hypermesh network, the larger the value of radix-k (respectively, dimension n) is, the higher the reliability and availability of the network becomes.

Based on the assumption that external computers (particularly, infected external computers) are connected to the Internet, and by considering the influence of the Internet topology on computer virus spreading, this paper establishes a novel computer virus propagation model with a complex-network approach. This model possesses a unique (viral) equilibrium which is globally attractive. Some numerical simulations are also given to illustrate this result. Further study shows that the computers with higher node degrees are more susceptible to infection than those with lower node degrees. In this regard, some appropriate protective measures are suggested.

In reality, a portion of infected external computers could enter the Internet, and removable storage media could carry virus. To our knowledge, nearly all previous models describing the spread of computer virus ignore the combined impact of these two factors. In this paper, a new dynamical model is established based on these facts. A systematic analysis of the model is performed, and it is found that the unique (viral) equilibrium is globally asymptotically stable. Some simulation experiments are also made to justify the model. Finally, a result and some applicable measures for suppressing viral spread are suggested.

Optical network-on-chip (NoC) is a new designing of Multi-Processor System-on-Chip (MPSoC). Global bus is the simplest logical topology of optical NoC. Static routing and wavelength assignment is one important communication mechanism of optical NoC. This paper addresses the routing and wavelength assignment (RWA) problem for locally twisted cube communication pattern on global bus optical NoC. For that purpose, a routing scheme, that is an embedding scheme, is proposed, and a wavelength assignment scheme under the embedding scheme is designed. The number of required wavelengths is shown to attain the minimum, guaranteeing the optimality of the proposed scheme.

Multi-ary n-cubes are a class of communication patterns that are employed by a number of typical parallel algorithms. This paper addresses the implementation of parallel algorithms with bidirectional and unidirectional ternary n-cube communication patterns on a mesh WDM optical network. For each of these two communication patterns, a routing and wavelength assignment scheme is described, and the number of wavelengths required is shown to attain the minimum, which guarantees the optimality of the proposed scheme.

Due to the high dimensionality of an epidemic model of computer viruses over a general scale-free network, it is difficult to make a close study of its dynamics. In particular, it is extremely difficult, if not impossible, to prove the global stability of its viral equilibrium, if any. To overcome this difficulty, we suggest to simplify a general scale-free network by partitioning all of its nodes into two classes: higher-degree nodes and lower-degree nodes, and then equating the degrees of all higher-degree nodes and all lower-degree nodes, respectively, yielding a reduced scale-free network. We then propose an epidemic model of computer viruses over a reduced scale-free network. A theoretical analysis reveals that the proposed model is bound to have a globally stable viral equilibrium, implying that any attempt to eradicate network viruses would prove unavailing. As a result, the next best thing we can do is to restrain virus prevalence. Based on an analysis of the impact of different model parameters on virus prevalence, some practicable measures are recommended to contain virus spreading. The work in this paper adequately justifies the idea of reduced scale-free networks.