Biblio
Interval uncertainty can cause uncontrollable variations in the objective and constraint values, which could seriously deteriorate the performance or even change the feasibility of the optimal solutions. Robust optimization is to obtain solutions that are optimal and minimally sensitive to uncertainty. In this paper, a sequential multi-objective robust optimization (MORO) approach based on support vector machines (SVM) is proposed. Firstly, a sequential optimization structure is adopted to ease the computational burden. Secondly, SVM is used to construct a classification model to classify design alternatives into feasible or infeasible. The proposed approach is tested on a numerical example and an engineering case. Results illustrate that the proposed approach can reasonably approximate solutions obtained from the existing sequential MORO approach (SMORO), while the computational costs are significantly reduced compared with those of SMORO.
Tactical networks are generally simple ad-hoc networks in design, however, this simple design often gets complicated, when heterogeneous wireless technologies have to work together to enable seamless multi-hop communications across multiple sessions. In recent years, there has been some significant advances in computational, radio, localization, and networking te, and session's rate i.e., aggregate capacity averaged over a 4-time-slot frame)chnologies, which motivate a clean slate design of the control plane for multi-hop tactical wireless networks. In this paper, we develop a global network optimization framework, which characterizes the control plane for multi-hop wireless tactical networks. This framework abstracts the underlying complexity of tactical wireless networks and orchestrates the the control plane functions. Specifically, we develop a cross-layer optimization framework, which characterizes the interaction between the physical, link, and network layers. By applying the framework to a throughput maximization problem, we show how the proposed framework can be utilized to solve a broad range of wireless multi-hop tactical networking problems.
This paper proposes a context-aware, graph-based approach for identifying anomalous user activities via user profile analysis, which obtains a group of users maximally similar among themselves as well as to the query during test time. The main challenges for the anomaly detection task are: (1) rare occurrences of anomalies making it difficult for exhaustive identification with reasonable false-alarm rate, and (2) continuously evolving new context-dependent anomaly types making it difficult to synthesize the activities apriori. Our proposed query-adaptive graph-based optimization approach, solvable using maximum flow algorithm, is designed to fully utilize both mutual similarities among the user models and their respective similarities with the query to shortlist the user profiles for a more reliable aggregated detection. Each user activity is represented using inputs from several multi-modal resources, which helps to localize anomalies from time-dependent data efficiently. Experiments on public datasets of insider threats and gesture recognition show impressive results.
In this paper, we present an algorithm for estimating the state of the power grid following a cyber-physical attack. We assume that an adversary attacks an area by: (i) disconnecting some lines within that area (failed lines), and (ii) obstructing the information from within the area to reach the control center. Given the phase angles of the buses outside the attacked area under the AC power flow model (before and after the attack), the algorithm estimates the phase angles of the buses and detects the failed lines inside the attacked area. The novelty of our approach is the transformation of the line failures detection problem, which is combinatorial in nature, to a convex optimization problem. As a result, our algorithm can detect any number of line failures in a running time that is independent of the number of failures and is solely dependent on the size of the network. To the best of our knowledge, this is the first convex relaxation for the problem of line failures detection using phase angle measurements under the AC power flow model. We evaluate the performance of our algorithm in the IEEE 118- and 300-bus systems, and show that it estimates the phase angles of the buses with less that 1% error, and can detect the line failures with 80% accuracy for single, double, and triple line failures.
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent. More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames. To calculate the temporal loss during the training stage, a novel two-frame synergic training mechanism is proposed. Compared with directly applying an existing image style transfer method to videos, our proposed method employs the trained network to yield temporally consistent stylized videos which are much more visually pleasant. In contrast to the prior video style transfer method which relies on time-consuming optimization on the fly, our method runs in real time while generating competitive visual results.
In assessing privacy on online social networks, it is important to investigate their vulnerability to reconnaissance strategies, in which attackers lure targets into being their friends by exploiting the social graph in order to extract victims' sensitive information. As the network topology is only partially revealed after each successful friend request, attackers need to employ an adaptive strategy. Existing work only considered a simple strategy in which attackers sequentially acquire one friend at a time, which causes tremendous delay in waiting for responses before sending the next request, and which lack the ability to retry failed requests after the network has changed. In contrast, we investigate an adaptive and parallel strategy, of which attackers can simultaneously send multiple friend requests in batch and recover from failed requests by retrying after topology changes, thereby significantly reducing the time to reach the targets and greatly improving robustness. We cast this approach as an optimization problem, Max-Crawling, and show it inapproximable within (1 - 1/e + $ε$). We first design our core algorithm PM-AReST which has an approximation ratio of (1 - e-(1-1/e)) using adaptive monotonic submodular properties. We next tighten our algorithm to provide a nearoptimal solution, i.e. having a ratio of (1 - 1/e), via a two-stage stochastic programming approach. We further establish the gap bound of (1 - e-(1-1/e)2) between batch strategies versus the optimal sequential one. We experimentally validate our theoretical results, finding that our algorithm performs nearoptimally in practice and that this is robust under a variety of problem settings.
We present a formal method for computing the best security provisioning for Internet of Things (IoT) scenarios characterized by a high degree of mobility. The security infrastructure is intended as a security resource allocation plan, computed as the solution of an optimization problem that minimizes the risk of having IoT devices not monitored by any resource. We employ the shortfall as a risk measure, a concept mostly used in the economics, and adapt it to our scenario. We show how to compute and evaluate an allocation plan, and how such security solutions address the continuous topology changes that affect an IoT environment.
Complex safety-critical devices require dependable communication. Dependability includes confidentiality and integrity as much as safety. Encrypting gateways with demilitarized zones, Multiple Independent Levels of Security architectures and the infamous Air Gap are diverse integration patterns for safety-critical infrastructure. Though resource restricted embedded safety devices still lack simple, certifiable, and efficient cryptography implementations. Following the recommended formal methods approach for safety-critical devices, we have implemented proven cryptography algorithms in the qualified model based language Scade as the Safety Leveraged Implementation of Data Encryption (SLIDE) library. Optimization for the synchronous dataflow language is discussed in the paper. The implementation for public-key based encryption and authentication is evaluated for real-world performance. The feasibility is shown by execution time benchmarks on an industrial safety microcontroller platform running a train control safety application.
Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.
In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.
Emerging cyber-physical systems (CPS) often require collecting end users' data to support data-informed decision making processes. There has been a long-standing argument as to the tradeoff between privacy and data utility. In this paper, we adopt a multiparametric programming approach to rigorously study conditions under which data utility has to be sacrificed to protect privacy and situations where free-lunch privacy can be achieved, i.e., data can be concealed without hurting the optimality of the decision making underlying the CPS. We formalize the concept of free-lunch privacy, and establish various results on its existence, geometry, as well as efficient computation methods. We propose the free-lunch privacy mechanism, which is a pragmatic mechanism that exploits free-lunch privacy if it exists with the constant guarantee of optimal usage of data. We study the resilience of this mechanism against attacks that attempt to infer the parameter of a user's data generating process. We close the paper by a case study on occupancy-adaptive smart home temperature control to demonstrate the efficacy of the mechanism.
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not require optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time.
In this paper, inspired by Gatys's recent work, we propose a novel approach that transforms photos to comics using deep convolutional neural networks (CNNs). While Gatys's method that uses a pre-trained VGG network generally works well for transferring artistic styles such as painting from a style image to a content image, for more minimalist styles such as comics, the method often fails to produce satisfactory results. To address this, we further introduce a dedicated comic style CNN, which is trained for classifying comic images and photos. This new network is effective in capturing various comic styles and thus helps to produce better comic stylization results. Even with a grayscale style image, Gatys's method can still produce colored output, which is not desirable for comics. We develop a modified optimization framework such that a grayscale image is guaranteed to be synthesized. To avoid converging to poor local minima, we further initialize the output image using grayscale version of the content image. Various examples show that our method synthesizes better comic images than the state-of-the-art method.
Public-key cryptography schemes are widely used due to their high level of security. As a very efficient one among public-key cryptosystems, elliptic curve cryptography (ECC) has been studied for years. Researchers used to improve the efficiency of ECC through point multiplication, which is the most important and complex operation of ECC. In our research, we use special families of curves and prime fields which have special properties. After that, we introduce the instruction set architecture (ISA) extension method to accelerate this algorithm (192-bit private key) and build an ECC\_ASIP model with six new ECC custom instructions. Finally, the ECC\_ASIP model is implemented in a field-programmable gate array (FPGA) platform. The persuasive experiments have been conducted to evaluate the performance of our new model in the aspects of the performance, the code storage space and hardware resources. Experimental results show that our processor improves 69.6% in the execution efficiency and requires only 6.2% more hardware resources.
In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.
We investigate minimization of tree pattern queries that use the child relation, descendant relation, node labels, and wildcards. We prove that minimization for such tree patterns is Sigma2P-complete and thus solve a problem first attacked by Flesca, Furfaro, and Masciari in 2003. We first provide an example that shows that tree patterns cannot be minimized by deleting nodes. This example shows that the M-NR conjecture, which states that minimality of tree patterns is equivalent to their nonredundancy, is false. We then show how the example can be turned into a gadget that allows us to prove Sigma2P-completeness.
This paper formulates a power system related optimal control problem, motivated by potential cyber-attacks on grid control systems, and ensuing defensive response to such attacks. The problem is formulated as a standard nonlinear program in the GAMS optimization environment, with system dynamics discretized over a short time horizon providing constraint equations, which are then treated via waveform relaxation. Selection of objective function and additional decision variables is explored first for identifying grid vulnerability to cyber-attacks that act by modifying feedback control system parameters. The resulting decisions for the attacker are then fixed, and the optimization problem is modified with a new objective function and decision variables, to explore a defender's possible response to such attacks.
We present an optimization approach that can be employed to calculate the globally optimal segmentation of a 2-D magnetic system into uniformly magnetized pieces. For each segment, the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam-focusing quadrupole magnet for particle accelerators, and a rotary device for magnetic refrigeration.
The mainstream approach to protecting the privacy of mobile users in location-based services (LBSs) is to alter (e.g., perturb, hide, and so on) the users’ actual locations in order to reduce exposed sensitive information. In order to be effective, a location-privacy preserving mechanism must consider both the privacy and utility requirements of each user, as well as the user’s overall exposed locations (which contribute to the adversary’s background knowledge). In this article, we propose a methodology that enables the design of optimal user-centric location obfuscation mechanisms respecting each individual user’s service quality requirements, while maximizing the expected error that the optimal adversary incurs in reconstructing the user’s actual trace. A key advantage of a user-centric mechanism is that it does not depend on third-party proxies or anonymizers; thus, it can be directly integrated in the mobile devices that users employ to access LBSs. Our methodology is based on the mutual optimization of user/adversary objectives (maximizing location privacy versus minimizing localization error) formalized as a Stackelberg Bayesian game. This formalization makes our solution robust against any location inference attack, that is, the adversary cannot decrease the user’s privacy by designing a better inference algorithm as long as the obfuscation mechanism is designed according to our privacy games. We develop two linear programs that solve the location privacy game and output the optimal obfuscation strategy and its corresponding optimal inference attack. These linear programs are used to design location privacy–preserving mechanisms that consider the correlation between past, current, and future locations of the user, thus can be tuned to protect different privacy objectives along the user’s location trace. We illustrate the efficacy of the optimal location privacy–preserving mechanisms obtained with our approach against real location traces, showing their performance in protecting users’ different location privacy objectives.
An Egyptian statue on display at the Manchester Museum mysteriously spins on its axis every day; it is eventually discovered that this is due to anisotropic friction forces, and that the motile power comes from imperceptible mechanical waves caused by visitors' footsteps and nearby traffic. This phenomena involves microscopic ratchets, and is pervasive in the microscopic world - this is basically how muscles contract. It was the source of inspiration to think about everyday objects that move by harvesting external vibration rather than using mechanical traction and steering wheels. We propose here a strategy for displacing objects by attaching relatively small vibration sources. After learning how several random bursts of vibration affect its pose, an optimization algorithm discovers the optimal sequence of vibration patterns required to (slowly but surely) move the object to a very different specified position. We describe and demonstrate two application scenarios, namely assisted transportation of heavy objects with little effort on the part of the human and self arranging furniture, useful for instance to clean classrooms or restaurants during vacant hours.