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Displaying the large number of bands in a hyper- spectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also has natural colors. Manifold learning preserves the image structure by forcing pixels with similar signatures to be displayed with similar colors. A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space. The colors of the output image are constrained by a corresponding natural-looking RGB image, which can either be generated from the HSI itself (e.g., band selection from the visible wavelength) or be captured by a separate device. Our method can be done at instance-level and feature-level. Instance-level learning directly obtains the RGB coordinates for the pixels in the HSI while feature-level learning learns an explicit mapping function from the high dimensional spectral space to the RGB space. Experimental results demonstrate the advantage of the proposed method in information preservation and natural color visualization.

Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition., Comment: 10 pages, 9 figures

The new cyber attack pattern of advanced persistent threat (APT) has posed a serious threat to modern society. This paper addresses the APT defense problem, i.e., 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.

This paper addresses the issue of suppressing a rumor using the truth in a cost-effective way. First, an individual-level dynamical model capturing the rumor-truth mixed spreading processes is proposed. On this basis, the cost-effective rumor-containing problem is modeled as an optimization problem. Extensive experiments show that finding a cost-effective rumor-containing strategy boils down to enhancing the first truth-spreading rate until the cost effectiveness of the rumor-containing strategy reaches the first turning point. This finding greatly reduces the time spent for solving the optimization problem. The influences of different factors on the optimal cost effectiveness of a rumor-containing strategy are examined through computer simulations. We believe our findings help suppress rumors in a cost-effective way. To our knowledge, this is the first time the rumor-containing problem is treated this way.

We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed., Comment: 8 pages, Appeared at KDD 2017, August 13--17, 2017, Halifax, NS, Canada

This paper is devoted to measuring the security of cyber networks under advanced persistent threats (APTs). First, an APT-based cyber attack-defense process is modeled as an individual-level dynamical system. Second, the dynamic model is shown to exhibit the global stability. On this basis, a new security metric of cyber networks, which is known as the limit security, is defined as the limit expected fraction of compromised nodes in the networks. Next, the influence of different factors on the limit security is illuminated through theoretical analysis and computer simulation. This work helps understand the security of cyber networks under APTs.

As a new type of cyber attacks, advanced persistent threats (APTs) pose a severe threat to modern society. This paper focuses on the assessment of the risk of APTs. Based on a dynamic model characterizing the time evolution of the state of an organization, the organization's risk is defined as its maximum possible expected loss, and the risk assessment problem is modeled as a constrained optimization problem. The influence of different factors on an organization's risk is uncovered through theoretical analysis. Based on extensive experiments, we speculate that the attack strategy obtained by applying the hill-climbing method to the proposed optimization problem, which we call the HC strategy, always leads to the maximum possible expected loss. We then present a set of five heuristic attack strategies and, through comparative experiments, show that the HC strategy causes a higher risk than all these heuristic strategies do, which supports our conjecture. Finally, the impact of two factors on the attacker's HC cost profit is determined through computer simulations. These findings help understand the risk of APTs in a quantitative manner., Comment: advanced persistent threat, risk assessment, expected loss, attack strategy, constrained optimization

This paper addressed the issue of estimating the damage caused by a computer virus. First, an individual-level delayed SIR model capturing the spreading process of a digital virus is derived. Second, the damage inflicted by the virus is modeled as the sum of the economic losses and the cost for developing the antivirus. Next, the impact of different factors, including the delay and the network structure, on the damage is explored by means of computer simulations. Thereby some measures of reducing the damage of a virus are recommended. To our knowledge, this is the first time the antivirus-developing cost is taken into account when estimating the damage of a virus.

Spreading truths and blocking rumors are two typical strategies for inhibiting rumors. In practice, a tradeoff between the two strategies, which is known as the TSRB strategy, may achieve a better cost-effectiveness. This paper is devoted to assessing the effectiveness of the TSRB strategy. For that purpose, an individual-level spreading model (the generic URQT model) capturing the interaction between a rumor and the truth is established. Under the model, a set of criteria for the dying out of a rumor is presented. These criteria capture the combined influence of the basic parameters and the network structures on the effectiveness of the TSRB strategy. Experimental results show that, when the rumor dies out, the dynamics of a simplified URQT model (the linear URQT model) fits well with the actual rumor-truth interacting process. Therefore, the generic URQT model and sometimes the linear URQT model provide a proper basis for assessing the effectiveness of the TSRB strategy., Comment: rumor spreading, truth-spreading/rumor-blocking strategy, effectiveness, individual-level spreading model, qualitative analysis of dynamical system, network structure. arXiv admin note: substantial text overlap with arXiv:1705.06604; text overlap with arXiv:1705.04818

Spreading truths is recognized as a feasible strategy for inhibiting rumors. This paper is devoted to assessing the effectiveness of the truth-spreading strategy. An individual-level rumor-truth spreading model (the generic URTU model) is derived. Under the model, two criteria for the termination of a rumor are presented. These criteria capture the influence of the network structures on the effectiveness of the truth-spreading strategy. Extensive simulations show that, when the rumor or the truth terminates, the dynamics of a simplified URTU model (the linear URTU model) fits well with the actual rumor-truth interplay process. Therefore, the generic URTU model forms a theoretical basis for assessing the effectiveness of the truth-spreading strategy for restraining rumors., Comment: arXiv admin note: text overlap with arXiv:1705.04818

As compared to the traditional advertising, the word-of-mouth (WOM) communications have striking advantages such as significantly lower cost and rapid delivery; this is especially the case with the popularity of online social networks. This paper addresses the issue of maximizing the overall profit of a WOM marketing campaign. A marketing process with both positive and negative WOM is modeled as a dynamical model knwn as the SIPNS model, and the profit maximization problem is modeled as a constrained optimization problem. The influence of different factors on the dynamics of the SIPNS model is revealed experimentally. Also, the impact of different factors on the expected overall profit of a WOM marketing campaign is uncovered experimentally. On this basis, some promotion strategies are suggested. To our knowledge, this is the first time a WOM marketing campaign is treated this way.

This paper addresses the discount pricing in word-of-mouth (WOM) marketing. A new discount strategy known as the Infection-Based Discount (IBD) strategy is proposed. The basic idea of the IBD strategy lies in that each customer enjoys a discount that is linearly proportional to his/her influence in the WOM network. To evaluate the performance of the IBD strategy, the WOM spreading process is modeled as a dynamic model known as the DPA model, and the performance of the IBD strategy is modeled as a function of the basic discount. Next, the influence of different factors, including the basic discount and the WOM network, on the dynamics of the DPA model is revealed experimentally. Finally, the influence of different factors on the performance of the IBD strategy is uncovered experimentally. On this basis, some promotional measures are recommended., Comment: word-of-mouth marketing, discount strategy, marketing profit, dynamic model