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Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.

This paper examines the average age minimization problem where only a fraction of the network users can transmit simultaneously over unreliable channels. Finding the optimal scheduling scheme, in this case, is known to be challenging. Accordingly, the Whittle’s index policy was proposed in the literature as a low-complexity heuristic to the problem. Although simple to implement, characterizing this policy’s performance is recognized to be a notoriously tricky task. In the sequel, we provide a new mathematical approach to establish its optimality in the many-users regime for specific network settings. Contrary to previous works in the literature that use restrictive mathematical assumptions, which can be challenging to verify, our novel approach is based on intricate techniques and it is free of any strong mathematical assumptions. These findings showcase that the Whittle’s index policy has analytically provable asymptotic optimality for the AoI minimization problem. Finally, we lay out numerical results that corroborate our theoretical findings and demonstrate the policy’s notable performance in the many-users regime.

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps.

Morris (Rev Econ Stud 67:57–78, 2000) defines the $$p$$ -cohesion by a connected subgraph in which every vertex has at least a fraction p of its neighbors in the subgraph, i.e., at most a fraction ( $$1-p$$ ) of its neighbors outside. We can find that a $$p$$ -cohesion ensures not only inner-cohesiveness but also outer-sparseness. The textbook on networks by Easley and Kleinberg (Networks, Crowds, and Markets - Reasoning About a Highly Connected World, Cambridge University Press, 2010) shows that $$p$$ -cohesions are fortress-like cohesive subgraphs which can hamper the entry of the cascade, following the contagion model. Despite the elegant definition and promising properties, to our best knowledge, there is no existing study on $$p$$ -cohesion regarding problem complexity and efficient computing algorithms. In this paper, we fill this gap by conducting a comprehensive theoretical analysis on the complexity of the problem and developing efficient computing algorithms. We focus on the minimal $$p$$ -cohesion because they are elementary units of $$p$$ -cohesions and the combination of multiple minimal $$p$$ -cohesions is a larger $$p$$ -cohesion. We demonstrate that the discovered minimal $$p$$ -cohesions can be utilized to solve the MinSeed problem: finding a smallest set of initial adopters (seeds) such that all the network users are eventually influenced. Extensive experiments on 8 real-life social networks verify the effectiveness of this model and the efficiency of our algorithms.

In many computing applications, there is a need to estimate different features of large data sets, such as the number of distinct elements or the similarity with other data sets. This can be efficiently implemented using probabilistic data structures known as sketches; for example, Hyperloglog is widely used for cardinality estimate and Minhash for similarity estimate. Many of these sketches rely on computing hash functions on the elements and counting the number of consecutive leading zeros (or ones) to identify the minimum (or maximum) hash value for data updating. This functionality usually accounts for the most demanding part of the sketch implementation; an efficient and low power hash computation and leading zero counting are therefore required for high performance. A novel but important observation is exploited in this paper; namely the trailing hash bits are rarely used in these sketches (e.g., the leading zeros counting stops once the first one is found, which usually occurs after a few bits). Based on this feature, a so-called Two-Step Hashing (TSH) scheme is proposed in this paper to significantly reduce the power consumption. Therefore rather than computing all hash bits (as in the traditional scheme), TSH computes a few leading hash bits in a first step and the trailing hash bits in the second step, that is only required when the leading bits are all zeros (or ones). Hence, in most cases only a fraction of the hash bits is computed, so significantly reducing the power dissipation of hash computations when they are used to check the number of consecutive leading zeros, or the minimum value. The use of TSH for sketches with both single and multiple pipeline implementations are considered; evaluation results based on HyperLogLog (HLL) as a sketch with a Cyclic Redundancy Check hash function show that TSH saves up to 75.9 % (78.7 %) power dissipation in a single (multiple) pipeline implementation.

As Probabilistic Safety Assessments (PSAs) are thoroughly conducted for the Site Operating States (SOSs) for a single unit, multi-unit Probabilistic Safety Assessments (MUPSAs) are ongoing worldwide to address new technical challenges or issues. In South Korea, the determination of the site operating states for a single site requires a logical approach with reasonable assumptions due to the fact that there are 4–8 operating units for each site. This paper suggests a simulation model that gives a reasonable expectation of the site operation states using the Monte-Carlo method as a stochastic approach and deterministic aspects such as operational policies. Statistical hypothesis tests were conducted so that the reliance of the simulation results can be guaranteed. In this study, 7 units of the Kori site were analysed as a case study. The result shows that the fraction of full power for all 7 units is nearly 0.45. For situations when more than two units are not in operation, the highest fraction combination was obtained for Plant Operation State (POS) 8, which is the stage of inspection and repairment. By entering various site operation scenarios, the simulation model can be used for the analysis of other site operation states.

Caching systems have long been crucial for improving the performance of a wide variety of network and web based online applications. In such systems, end-to-end application performance heavily depends on the fraction of objects transferred from the cache, also known as the cache hit probability. Many caching policies have been proposed and implemented to improve the hit probability. In this work, we propose a new method to compute an upper bound on hit probability for all non-anticipative caching policies, i.e., for policies that have no knowledge of future requests. Our key insight is to order the objects according to the ratio of their Hazard Rate (HR) function values to their sizes and place in the cache the objects with the largest ratios till the cache capacity is exhausted. Under some statistical assumptions, we prove that our proposed HR to size ratio based ordering model computes the maximum achievable hit probability and serves as an upper bound for all non-anticipative caching policies. We derive closed form expressions for the upper bound under some specific object request arrival processes. We also provide simulation results to validate its correctness and to compare it to the state-of-the-art upper bounds. We find it to be tighter than state-of-the-art upper bounds for a variety of object request arrival processes., IFIP WG 7.3 Performance

The minimum coloring game allows to model economic situations where costly and potentially conflicting tasks have to be executed. The primitives of the game are a graph (describing conflicts between the respective tasks) and a cost function (giving the cost of completing any number of pairwise conflicting tasks). This setting gives rise to a cooperative game where agents have to split the minimum cost of completing all tasks. Our study of the core allows to find a necessary and sufficient condition guaranteeing its non-vacuity; and we describe a subset of allocations that are always in the core (when it is nonempty). These allocations put weights on the incompatible groups with highest cardinality, called maximal cliques. We then propose two cost sharing rules and their axiomatizations. The first rule assigns shares in proportion to the number of maximal cliques an agent belongs to; and the key property in its axiomatization prevents splitting and merging manipulations. The second rule is an extension of the well-known airport rule; and it requires every agent to pay a minimal fraction of the total cost.

The past two decades have witnessed significant advances in high-throughput “omics” technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive “omics” profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR) - a framework that includes a variety of regression methods - in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data.

The First-Come First-Served (FCFS) scheduling policy is the most popular scheduling algorithm used in practice. Furthermore, its usage is theoretically validated: for light-tailed job size distributions, FCFS has weakly optimal asymptotic tail of response time. But what if we don't just care about the asymptotic tail? What if we also care about the 99th percentile of response time, or the fraction of jobs that complete in under one second? Is FCFS still best? Outside of the asymptotic regime, only loose bounds on the tail of FCFS are known, and optimality is completely open. In this paper, we introduce a new policy, Nudge, which is the first policy to provably stochastically improve upon FCFS. We prove that Nudge simultaneously improves upon FCFS at every point along the tail, for light-tailed job size distributions. As a result, Nudge outperforms FCFS for every moment and every percentile of response time. Moreover, Nudge provides a multiplicative improvement over FCFS in the asymptotic tail. This resolves a long-standing open problem by showing that, counter to previous conjecture, FCFS is not strongly asymptotically optimal., Comment: 29 pages, 4 figures. To appear in SIGMETRICS 2021

Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, they still depend on a convenient local filter such as convolution as full 3D attention is expensive to compute and requires a big dataset, which might not be available. Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i.e., self-attention). Unlike a regular Non-Local network, the 3D Axial-Attention network applies the attention operation to each axis separately. Additionally, we solve the invariant position problem of the Non-Local network by proposing to add 3D positional encoding to shared embeddings. Results: We validated the proposed method on 442 benign nodules and 406 malignant nodules, extracted from the public LIDC-IDRI dataset by following a rigorous experimental setup using only nodules annotated by at least three radiologists. Our results show that the 3D Axial-Attention model achieves state-of-the-art performance on all evaluation metrics, including AUC and Accuracy. Conclusions: The proposed model provides full 3D attention, whereby every element (i.e., pixel) in the 3D volume space attends to every other element in the nodule effectively. Thus, the 3D Axial-Attention network can be used in all layers without the need for local filters. The experimental results show the importance of full 3D attention for classifying lung nodules.

Data centers are critical environments that provide support for a wide range of services and applications, and therefore, there is a demand in order to guarantee high availability and reliability required in these environments. This work proposes a strategy based on models, SLA contracts, maintenance policies and optimization techniques for assessing the cost and availability of electrical infrastructures hosted in data centers. The proposed optimization strategy is based on design of experiments (DoE) and uses the availability importance index in order to detect the equipment that most impacts the system’s availability and, thus, to be able to propose improvements. In addition, a hybrid modeling approach that considers the advantages of stochastic Petri nets and reliability block diagrams is adopted to assess availability. To illustrate the applicability of the proposed approach, two case studies were carried out where significant results were obtained. In the first study, where the performance of the proposed strategy was compared with the brute force algorithm, it was possible to obtain results close to the optimum ones in a fraction of the time. For example, brute force demanded more than 100 minutes to be evaluated, while the proposed strategy took only 6 seconds.

With the advancement in information technology, datasets with an enormous amount of data are available. The classification task on these datasets is more time- and memory-consuming as the number of data increases. The support vector machine (SVM), which is arguably the most popular classification technique, has disappointing performance in dealing with large datasets due to its constrained optimization problem. To deal with this challenge, the variant SVM (VSVM) has been utilized which has the fraction $({1}/{2})b^{2}$ in its primal objective function, where $b$ is the bias of the desired hyperplane. The VSVM has been solved with different optimization techniques in more time- and memory-efficient fashion. However, there is no guarantee that its optimal solution is the same as the standard SVM. In this paper, we introduce the generalized VSVM (GVSVM) which has the fraction $({1}/{2t})b^{2}$ in its primal objective function, for a fixed positive scalar $t$ . Further, we present the thorough theoretical insights that indicate the optimal solution of the GVSVM tends to the optimal solution of the standard SVM as $t \rightarrow \infty $ . One vital corollary is to derive a closed-form formula to obtain the bias term in the standard SVM. Such a formula obviates the need of approximating it, which is the modus operandi to date. An efficient neural network is then proposed to solve the GVSVM dual problem, which is asymptotically stable in the sense of Lyapunov and converges globally exponentially to the exact solution of the GVSVM. The proposed neural network has less complexity in architecture and needs fewer computations in each iteration in comparison to the existing neural solutions. Experiments confirm the efficacy of the proposed recurrent neural network and the proximity of the GVSVM and the standard SVM solutions with more significant values of $t$ .

In the past years, most works have focused on designing an indigenous network architecture to advance progress in classification, but another potential opportunity for improvement, that is, research on classification losses, is underdeveloped. Although some new losses have been proposed, most of them either are variants of softmax loss or should combine with softmax loss. Hence, the inherent deficiencies of softmax loss, such as sensitiveness, class-balanced restriction, closed-set limitation, non-scale-invariance and incoordination between the intraclass distance and interclass distance, cannot be completely overcome. In light of this, we pave a new way to design a loss that has no relation to softmax loss and can avoid its weaknesses. We also propose an efficient algorithm to optimize the new loss that can circumvent computing the complicated gradients of a fraction, and the convergence is theoretically ensured. Extensive experimental results on benchmark datasets demonstrate that the new loss is competitive with state-of-the-art losses for classification. Additionally, other specially designed experiments show that the new loss is also effective at handling class-imbalanced problems, is robust in addressing outliers and can discover samples of unseen classes in open-set cases.

We consider the problem of private information retrieval (PIR), where a single user with private side information (PSI) aims to retrieve multiple files from a library stored at some servers. We assume that the side information (SI) at the user includes a subset of files stored privately. Moreover, the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load transmitted from the servers to the user such that the requested files can be decoded with the help of received data and side information. By providing matching lower and upper bounds for certain regimes, we characterize the minimum load imposed on all the servers. Our result shows that the capacity is the same as the capacity of a multi-message PIR problem without PSI, but with a library of reduced size, i.e., the library is equal to the original library size minus the size of SI. Finally, we extend our setup to the case where instead of storing complete files as SI, the user can store a fraction of files. For this scenario, we propose an achievability scheme based on which we discuss the best storing strategies.

A large fraction of textual data available today contains various types of “noise,” such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data/human intervention. The proposed algorithm is applicable to text over different languages and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods.

In every corner of machine learning and statistics, there is a need for estimators that work not just in an idealized model, but even when their assumptions are violated. Unfortunately, in high dimensions, being provably robust and being efficiently computable are often at odds with each other. We give the first efficient algorithm for estimating the parameters of a high-dimensional Gaussian that is able to tolerate a constant fraction of corruptions that is independent of the dimension. Prior to our work, all known estimators either needed time exponential in the dimension to compute or could tolerate only an inverse-polynomial fraction of corruptions. Not only does our algorithm bridge the gap between robustness and algorithms, but also it turns out to be highly practical in a variety of settings.

This paper examines the performance of improvement search as a function of the quality of the starting solution in the planar (or continuous) p-median problem. We show that using optimal solutions of the analogue discrete p-median problem as the starting solution for heuristic improvement algorithms, as recommended in the literature, can actually lead to inferior performance. That is, good starting solutions obtained in the discrete space with a fraction of the effort can actually be better, a counter-intuitive result that illustrates in a different context the less is more principle recently advocated in the literature.

Motion estimation plays a very crucial role in many video applications. Hence, an accurate and fast algorithm is required in real-time applications. Although exhaustive block matching (EBM) algorithm is the best algorithm used for motion estimation in terms of performance, it is computationally very expensive. Several algorithms have been developed in the literature to reduce the computational time required by the EBM algorithm; however, their performance is not comparable to that of EBM. In this paper, we propose an extremely fast technique for motion estimation based on the edge map of only a fraction of the frame. Extensive experiments are carried out, and the results show that the proposed technique provides a performance comparable to that of the EBM algorithm, with a very low computational complexity.

Background: Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are “locked” in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. Methods: We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. Results: We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. Conclusion: Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.

Clustered interval-censored failure time data occur in many areas and many methods have been proposed for their analysis. In practice, in addition to the clustering and interval censoring, such dat...

In this work, an emission-minimizing vehicle routing problem with heterogeneous vehicles and a heterogeneous road and traffic network is considered as it is typical in urban areas. Depending on the load of the vehicle, there exist multiple emission-minimal arcs for traveling between two locations. To solve the vehicle routing problem efficiently, a column generation approach is presented. At the core of the procedure an emission-oriented elementary shortest path problem on a multigraph is solved by a backward labeling algorithm. It is shown that the labeling algorithm can be sped up by adjusting the dual master program and by restricting the number of labels propagated in the sub-problem. The column generation technique is used to setup a fast heuristic as well as a branch-and-price algorithm. Both procedures are evaluated based on test instances with up to 100 customers. It turns out that the heuristic approach is very effective and generates near-optimal solutions with gaps below 0.1% on average while only requiring a fraction of the runtime of the exact approach.

This paper uses the continued fraction technique to construct a nonstationary 4-point ternary interpolatory subdivision scheme, which provides the user with a tension parameter that effectively handles cusps compared with a stationary 4-point ternary interpolatory subdivision scheme. Then, the continuous nonstationary 4-point ternary scheme is analyzed, and the limit curve is at least C 2 -continuous. Furthermore, the monotonicity preservation and convexity preservation are proved.

We study a classic Bayesian mechanism design setting of monopoly problem for an additive buyer in the presence of budgets. In this setting, a monopolist seller with m heterogeneous items faces a single buyer and seeks to maximize her revenue. The buyer has a budget and additive valuations drawn independently for each item from (non-identical) distributions. We show that when the buyer’s budget is publicly known, it is better to sell each item separately; selling the grand bundle extracts a constant fraction of the optimal revenue. When the budget is private, we consider a standard Bayesian setting where buyer’s budget b is drawn from a known distribution B . We show that if b is independent of the valuations (which is necessary) and distribution B satisfies monotone hazard rate condition, then selling items separately or in a grand bundle is still approximately optimal.

In this paper, we present the problem in which a municipal company operating in the waste management sector willing to encourage users to use differentiated waste collection facilities, designs a utility user function to attain such a goal. The problem is modeled in terms of bilevel optimization where the leader is the municipal firm which aims at maximizing the concurrent fraction of user waste demand thrown in recycling facilities, and the follower are users aiming at maximizing the utility function proposed by the leader. The resulting bilevel model is analysed in terms of stability showing that its optimistic solution value equals the pessimistic solution value. A solution approach is presented. Finally, a computational study shows the effectiveness of our proposal.

In this research article, a new idea concerning the application of concept of the continued fractions to the expansion of the geopotential is treated. The perturbed motion of the satellite in the oblate gravitational field of the Earth using a potential of a continued fractions procedure is studied. To compare with the usual perturbation theory, the continued fraction series is truncated beyond the third term. The first oblateness term of the potential is retained. The equations of motion are constructed using the Lagrange planetary equations. The integrals and averages required for solving the problem is outlined as well as the different cases are highlighted. The perturbations in all orbital elements due to considered model of continued fractions are evaluated. The new findings of the manuscript contrast with classical oblate potential field, the semi-major axis, eccentricity, and inclination, nonzero averaged terms in perturbations are obtained. Extra nonzero averaged perturbing terms in the argument of perigee and ascending node are revealed in addition to the classical perturbations.

In this article, an effort has been taken to predict the performance of the proposed tilted wick type solar still with better accuracy. The key parameters for the proposed model is the condensing glass slope angle, its transmittance and interception of solar radiation over the corrugated and spreaded wick surface during the working hours of the system. The evaluation has been made by the concept of solar fraction and the results show that the parameters are found to be significant for the prediction of thermal performance. Numerical results obtained for the investigative solutions were validated with the experimental results for one of the distinctive days in each month and the hypothetical results found promising with the investigational observations. The suggested model is found to be more accurate in the assessment of thermal performance of the system.

Coded computing is a new framework to address fundamental issues in large scale distributed computing, by injecting structured randomness and redundancy. We first provide an overview of coded computing and summarize some recent advances. Then we focus on distributed matrix multiplication and consider a common scenario where each worker is assigned a fraction of the multiplication task. In particular, by partitioning two input matrices into $m$ -by- $p$ and $p$ -by- $n$ subblocks, a single multiplication task can be viewed as computing linear combinations of $pmn$ submatrix products, which can be assigned to $pmn$ workers. Such block-partitioning-based designs have been widely studied under the topics of secure, private, and batch computation, where the state of the arts all require computing at least “cubic” ( $pmn$ ) number of submatrix multiplications. Entangled polynomial codes, first presented for straggler mitigation, provides a powerful method for breaking the cubic barrier. It achieves a subcubic recovery threshold, i.e., recovering the final product from any subset of multiplication results with a size order-wise smaller than $pmn$ . We show that entangled polynomial codes can be further extended to also include these three important settings, providing unified frameworks that order-wise reduce the total computational costs by achieving subcubic recovery thresholds.

The “bee-identification problem” was formally defined by Tandon, Tan and Varshney [ IEEE Trans. Commun. , vol. 67, 2019], and the error exponent was studied. This work extends the results for the “absentee bees” scenario, where a fraction of the bees are absent in the beehive image used for identification. For this setting, we present an exact characterization of the bee-identification error exponent, and show that independent barcode decoding is optimal, i.e., joint decoding of the bee barcodes does not result in a better error exponent relative to independent decoding of each noisy barcode. This is in contrast to the result without absentee bees, where joint barcode decoding results in a significantly higher error exponent than independent barcode decoding. We also define and characterize the ‘capacity’ for the bee-identification problem with absentee bees, and prove a strong converse for the same.

The aim of this work is to deploy and experimentally validate a model-based strategy to estimate unmeasurable variables in a proton exchange membrane (PEM) fuel cell. First, a nonlinear PEM fuel cell dynamical model is implemented and calibrated using an optimisation approach that takes real measurement data as input. Then, an advanced observation approach is developed to retrieve non-measurable data from the fuel cell. Two states are estimated in this work: the fuel cell temperature and the internal liquid water fraction. To achieve this, a model-based high-order sliding mode observer (HOSM) with chattering-free capabilities is deployed. The fuel cell temperature is measured in real time to drive the estimation error to zero in a finite amount of time. Finally, the methodology is validated using experimental data stemming from a laboratory test station, comparing the the HOSM observer with and without chattering-free gain., This work has been partially funded by the Spanish national project DOVELAR (ref. RTI2018-096001- B-C32, MINECO/FEDER), the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656), and AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482).

A new fractional-order Dickson functions are introduced for solving numerically fractional optimal control and variational problems involving Mittag–Leffler nonsingular kernel. The type of fractional derivative in the proposed problems is the Atangana–Baleanu–Caputo fractional derivative. In the process of the method, we use fractional-order Dickson functions and their properties to provide an accurate computational technique for calculating operational matrices, at first. Then, with the help of operational matrices and the Lagrange multiplier method, these problems are reduced to a system of algebraic equations. At last, to demonstrate the effectiveness of the new method, we enforce the proposed algorithm for several examples.

The so-called l0 pseudonorm on Rd counts the number of nonzero components of a vector. It is used in sparse optimization, either as criterion or in the constraints, to obtain solutions with few nonzero entries. For such problems, the Fenchel conjugacy fails to provide relevant analysis: indeed, the Fenchel conjugate of the characteristic function of the level sets of the l0 pseudonorm is minus infinity, and the Fenchel biconjugate of the l0 pseudonorm is zero. In this paper, we display a class of conjugacies that are suitable for the l0 pseudonorm. For this purpose, we suppose given a (source) norm on Rd. With this norm, we define, on the one hand, a sequence of so-called coordinate-k norms and, on the other hand, a coupling between Rd and Rd , called Capra (constant along primal rays). Then, we provide formulas for the Capra-conjugate and biconjugate, and for the Capra subdifferentials, of functions of the l0 pseudonorm (hence, in particular, of the l0 pseudonorm itself and of the characteristic functions of its level sets), in terms of the coordinate-k norms. As an application, we provide a new family of lower bounds for the l0 pseudonorm, as a fraction between two norms, the denominator being any norm.

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or $L_0$ attacks. Effective and fast $L_0$ attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC allows to obtain more powerful attack algorithms that better take into account each input's characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) $L_0$ attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW $L_0$ on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for $L_0$ real-time adversarial testing on datasets such as the ones previously cited. Codes are publicly available through the link https://github.com/probabilistic-jsmas/probabilistic-jsmas., Comment: Journal Machine Learning and Knowledge Extraction

In this paper, the distributed cooperative control problem of heterogeneous nonlinear multi-agent systems (HNMASs) which are of pure-feedback forms coupled with nonaffine dynamics is considered with momentous emphasis on finite-time convergence. Multiple kinds of input constraints are also considered and can be completely unknown. To achieve the dynamic tracking for the heterogeneous followers with unknown input constrains, there are two consensus theorems are proposed, one of which can be utilized to achieve finite-time convergence. Meanwhile, it is the blend of fraction power feedback method and neural network (NN) technique that let the controlled systems have the finite-time characteristic. In the proposed tracking control protocols, only the local position information from neighbors or leader is required. In the end, the effectiveness of two proposed consensus algorithms is confirmed and compared by numerical simulation.

Power flow formulated with a distributed slack bus involves modeling the active-power output of each generator with three elements: a nominal injection modulated by a fraction of the net-load imbalance allocated via a participation factor . This setup acknowledges generator dynamics and system operations, but it has long been plagued by ambiguous and inconsistent interpretations of its constituent elemental quantities. In this paper, we establish that, with the: i) nominal active-power injections set to be the economic dispatch setpoints, ii) participation factors fixed to be the ones used in automatic generation control, and iii) net-load imbalance considered to be the total load and loss unaccounted in economic dispatch, the power flow solution best matches results from a simulation unto steady state of the system differential algebraic equation (DAE) model. Numerical case studies tailored to the New England test system validate the analysis by demonstrating that solutions obtained from a distributed slack formulation offer lower errors (with respect to DAE-model simulations) compared to the exhaustive set of all single slack bus choices.

Association rule mining discovers patterns in large data repositories, and benefits diverse application domains such as healthcare, marketing, etc. However, mining datasets that contain data about individuals may cause significant privacy breaches. Recent research addresses the privacy threats that arise when mining sensitive data, and several techniques allow data mining with differential privacy guarantees. However, existing methods only discover rules that have very large support, i.e., occur in a large fraction of the dataset transactions (typically, more than 50 percent). This is a serious limitation, as numerous high-quality rules do not reach such high frequencies (e.g., rules about rare diseases, or luxury merchandise). We propose a method that focuses on mining high-quality association rules with moderate and low frequencies. We employ a novel technique for rule extraction that combines the exponential mechanism of differential privacy with reservoir sampling. The proposed algorithm allows us to directly mine association rules, without the need to compute noisy supports for large numbers of itemsets. Our experimental evaluation shows that our technique is able to sample low- and moderate-support rules with good precision, clearly outperforming existing solutions.

Scientifically and accurately forecasting of future shale gas output tends is very important in making energy policies, especially for China whose historical data of shale gas output is very limited. The existing grey shale gas output prediction model does not perform well in prediction due to its defects. To overcome these shortcoming, this paper, based on the principle of “new information priority”, combined with the contradiction between model prediction results and qualitative data analysis conclusions, designs a grey prediction model combining new initial conditions and original data reprocessing. Then the new model’s accumulative order is optimized by fraction accumulation generation operation and its properties is discussed. Finally, the new model is used to simulate and forecast shale gas output in China from 2012 to 2018 and compared it with the existing shale gas prediction model. The comparison results show that the new model reduces by 84.34%, 68.96% and 75.60% of the mean relative simulation percentage error (MRSPE), mean relative prediction percentage error (MRPPE) and comprehensive mean relative percentage error (CMPPE) respectively. This paper not only has theoretical innovations but also provides a good mathematical method for predicting shale gas output.

This paper addresses the sampled-data state estimation problem for complex networks using partial nodes’ measurements. A hybrid observer network with partial control is developed to estimate the state information. The key point of the hybrid observer network is that the state observer network is continuous-time by introducing an output predictor. Besides, the hybrid observer only requires a fraction of nodes’ sampled measurements with partial control technique. It reduces the state estimation cost and improves the estimation effectiveness. Some criteria are developed to guarantee that the proposed observer network is an exponential observer. Finally, simulation example validates the proposed approach.

This paper investigates the effect of incidents on freeway segment capacity. Currently, the Highway Capacity Manual (HCM) provides a look-up table linking the remaining segment capacity fraction during an incident to the total and closed number of lanes on the segment. In reality, segment capacity during an incident will tend to vary over time, with the most severe effects felt early on before any type of response is initiated, with congestion progressively improving as the appropriate incident management actions are implemented. By applying a genetic algorithm calibration method on each incident day and calibrating the incident capacity adjustment factors (CAFs), optimal time-dependent CAFs were derived that best represented the effect of incidents on the freeway segment capacity. By analyzing the optimal CAFs, the strongest relationship was revealed to be between the optimal time-dependent CAF and the temporal progression of the incident. A regression model was developed to represent this behavior. This was formulated in a manner that can directly adjust the current HCM’s fixed CAF values (for a specific lane closure configuration) for modeling incidents both in single day, seed file application, or for an entire year reliability analysis. A portion of WB I-540 in Raleigh, NC was selected as the study area in which the proposed method was tested. Between January 2014 and December 2018, the team identified 22 isolated incidents (away from the recurring congestion period) that closed one or two lanes of traffic, creating a distinct congestion pattern.

Given a relational specification between Boolean inputs and outputs, Boolean functional synthesis seeks to synthesize each output as a function of the inputs such that the specification is met. Despite significant algorithmic advances in Boolean functional synthesis over the past few years, there are relatively small specifications that have remained beyond the reach of all state-of-the-art tools. In trying to understand this behaviour, we show that unless some hard conjectures in complexity theory are falsified, Boolean functional synthesis must generate large Skolem functions in the worst-case. Given this inherent hardness, what does one do to solve the problem? We present a two-phase algorithm, where the first phase is efficient in practice both in terms of time and size of synthesized functions, and solves a large fraction of our benchmarks. This phase is also guaranteed to solve the problem when the representation of the input specification satisfies some structural requirements. For those cases where the first phase doesn’t suffice, we present a second phase of our synthesis algorithm that uses a special class of algorithms, called expansion-based algorithms, to generate correct Skolem functions. This may require exponential time and generate exponential-sized Skolem functions in the worst-case. Detailed experimental evaluation shows that our overall synthesis algorithm performs better than other techniques for a large number of benchmarks.

A general theory of online power control for discrete-time battery limited energy harvesting communications is developed, which leads to, among other things, an explicit characterization of a maximin optimal policy. This policy only requires the knowledge of the (effective) mean of the energy arrival process and maximizes the minimum asymptotic expected average reward (with the minimization taken over all energy arrival distributions of a given (effective) mean). Moreover, it is universally near optimal and has a strictly better worst-case performance as well as a strictly improved lower multiplicative factor in comparison with the fixed fraction policy proposed by Shaviv and \"{O}zg\"{u}r when the objective is to maximize the throughput over an additive white Gaussian noise channel. The competitiveness of this maximin optimal policy is also demonstrated via numerical examples., Comment: 13 pages, 5 figures

Shadow fractions can be overestimated because of topographic shadows, which can occupy a significant area on aerial photographs of mountainous terrain. In this study, we first used high-dynamic-range (HDR) image analysis techniques to extract the original canopy shadow from the topographic shadows on aerial photographs. Subsequently, we applied the shadow fraction method to estimate selected forest attributes (stand height, basal area, and stem volume). In this paper, we discuss the effects of tree shadow fraction normalization, auxiliary spectral information, and forest type on forest attribute estimation. HDR image analysis successfully extracted canopy shadow information from topographic shadows. The tree shadow fraction normalization method had no obvious effect. The shadow fraction enhanced spectral information to estimate stand attributes. Using shadow fractions resulted in better estimates of stand height for mixed-hardwood forest ([Formula: see text] = 0.45), basal area for mixed-hardwood forest ([Formula: see text] = 0.50), and stem volume for conifer–hardwood forest ([Formula: see text] = 0.43). This difference in estimated results is related to the shade patterns produced by stand structures in the different forest types.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Graphical abstract.

In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov-Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example.

As cooling and heating energy consumptions of buildings are closely related to outdoor climate variations, the reliability of building energy simulation results is significantly influenced by the accuracy of weather data being used. We intend to construct a new typical meteorological year (TMY) for Taipei. As no beam or diffuse solar irradiance data have been recorded at local weather stations, a preliminary study on the influences of currently available hourly solar diffuse fraction models (DFMs) to the building cooling loads was performed. A 2.30%–5.18% range of annual cooling load variation was observed, which drove a need for searching suitable DFMs. To this end, the observed diffuse irradiance data of an in-situ experiment was compared to the DFM modeled values to identify the suitable DFM. It was found that Kuo’s model, which has its coefficient been adapted to the local weather and further uses solar altitude, the daily clearness index as predicting variables, performed best and was used herein. The representativeness against the long-term climate of the three antiquated TMYs and the new one was discussed with a simulation-based comparison from 12 existing buildings. The reliability and accuracy of the new TMY in representing the local climate conditions are much improved.

Communication in engineering organizations affects the performance of the complex systems they design. Mis communication occurs when communication results in a “deficiency” or “problem” that hinders parties from fulfilling their individual or collective values. A recent study demonstrated widespread miscommunication in a Fortune 500 engineering firm about the definition of “an estimate” in a complex system design context. Building on that work, this article used a Monte Carlo simulation (8800 runs) of an agent-based model to demonstrate how systemic design process miscommunication may affect complex system performance. Each run of the simulation created a unique 1000-artifact system using a network generation algorithm and converged its design through optimization. Systems where estimates communicated “current” designs outperformed systems where estimates communicated “future” projections of their designs instead. Varying the fraction of the population, which uses each definition of an estimate varied system performance and uncertainty. Whether related to estimate definitions or more generally, this article demonstrates that miscommunication may affect system performance.

Online multi-information detection of mineral properties and composition plays a vital role in the realization of digital mining and digital concentrating mill, and the way of machine vision technology is put forward as a cost-effective and safe approach at present. This paper presents an exploratory study employing a bench-scale approach to detect the multi-information of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content. Firstly, we adopt a Finite-Erosion-and-Exact-Dilation (FEED) algorithm and a particle-on-edge region segmentation algorithm to segment overlapped particles and ensure the full analysis of target regions. Moreover, twenty-nine features are extracted and optimized to enable the particle mass estimation model, particle size characterization, classification model of density fraction, and prediction model of ash content to be implemented. Finally, an experimental study shows the merits of the proposed approach, and the average prediction errors of size distribution, density distribution, and ash content of each density fraction are 1.85%, 2.57%, 3.36%, respectively. The total ash content error is 2.54%. Results derived using the proposed approach reveal that it has the potential to be applied to the coal processing industry.

This paper addresses the bipartite output consensus problem of heterogeneous multi-agent systems, where the agents have different dynamics. Under the assumption that the communication topology has a spanning tree and the information of the leader is accessible only to a fraction of followers, an adaptive distributed bipartite observer is designed for every follower to estimate some information of the leader. By combining the output regulator equation and reduced-order observer design, an output feedback control law is proposed for solving the bipartite output consensus problem. At last, a simulation example is provided to show the effectiveness of the theoretical results.

Our investigations are motivated by distributive logical connectives and their generalizations used in fuzzy set theory and focused also by Klement in the Linz Seminar 2000 closing session. This paper is mainly devoted to solving the functional equations of conditional distributivity of $U_1$ over $U_2$ , where both $U_1$ and $U_2$ are uninorms with continuous underlying operators. Then, the almost complete characterization of such a pair $(U_1,U_2)$ of uninorms is given except for only a small fraction of the unit square. Next, we give the complete characterization of the previous pair under some appropriate restriction, that is, the second operator $U_2\in \mathbf {U}_{\max }\cup \mathbf {U}_{\min }$ . Finally, we also construct a counterexample to illustrate that the obtained results are necessary but not sufficient for general cases with unlimited condition.

In the Distributed Storage Systems (DSSs), the encoded fraction of information is stored in a distributed fashion on different chunk servers. Recently a new paradigm of Fractional Repetition (FR) codes has been introduced, in which, encoded data information is stored on distributed servers using an encoding involving a Maximum Distance Separable (MDS) code and a Repetition code. In this work, we have considered FR codes with asymmetric parameters known as Generalized Fractional Repetition (GFR) codes. We have shown that any GFR code is equivalent to a hypergraph. Using the correspondence, the properties and the bounds of a hypergraph are directly mapped to the associated GFR code. In general, using the correspondence, the necessary and sufficient conditions for the existence of a GFR code is obtained. It is also shown that any GFR code associated with a linear hypergraph is universally good, and any locally repairable GFR code is associated with a class of non-linear hypergraph.