Blockchain interoperability is mainly limited by the cost and time required to validate cross-chain transactions. To address the problem, in the existing literature, researchers have proposed the selection of a fixed set of notaries by using their reputation or stake values. However, selecting a fixedsize notary can be inefficient in handling the requirements of various decentralized applications. For instance, high-throughput applications, such as lighting and Raiden networks, may need a smaller set of notaries for better throughput, whereas a larger number of notaries may be required for validating securitycritical transactions, such as Voatz and Polymath. To address the limitation, the paper proposes a Dynamic Notary Selection (DyNoS) framework for selecting a notary subset to validate the cross-chain transactions by formulating the problem as a multileader and multi-follower Stackelberg game. More precisely, in the proposed framework, the followers in the NSB compete to maximize their individual profit by getting selected within the notary subset, while the leaders aim to minimize the total cost charged to dApp users to promote NSB adoption. The effectiveness of the approach is validated using state-of-theart cross-chain testbeds such as Sepolia and Ganache, showing that the cost for validating cross-chain transactions is reduced by$23.5 \sim 27.2 \%$while notary profits increase by$6.1 \sim 9 \%$compared to existing solutions.

Non-uniform sampling selects samples at irregular intervals for concise signal representation. Prior works on non-uniform sampling predominantly focused on maximizing reconstruction accuracy or optimizing sample size. However, a trade-off exists between the two factors, as increasing the sample size can improve the reconstruction accuracy, but it decreases the bandwidth efficiency. The reverse is true for under-sampling. Thus, it is important to balance the two factors. This motivates us to consider CAISOS, a CArdinalIty conStraint nOn-uniform Sampling problem that aims to select at most k sample points of a given time-varying signal such that the regenerated signal has the maximum reconstruction accuracy. Applications of CAISOS include fixed-rate sampling and signal compression in the time domain, such as in robotics and fixed-rate speech encoders. It proposes an integer linear programming model to address CAISOS and shows that the time complexity of the same increases exponentially with increasing signal size. It further proves that CAISOS is an NP-complete problem by showing that it is both NP and NP-hard by using a polynomial-time reduction from the 0/1 knapsack problem. Thus, the paper proposes a polynomial time heuristic based on the least-cost branch-and-bound approximation to solve CAISOS. Finally, it demonstrates the effectiveness of the proposed approaches through simulation by comparing them with the existing counterparts using various time-varying signals available in multiple databases.

Software-defined networks (SDNs) are an emerging technology to improve the scalability and network performance of data-intensive applications with high-volume traffic. Load balancing among controllers with several switch migrations is a major obstacle in a multi-controller SDN, degrading the overall system’s performance. To resolve the above issue, this paper proposes OptiGSM, an optimum greedy-based switch migration approach for load balancing among the controllers. OptiGSM starts the switch migration process from an overloaded controller to an underloaded controller if the mean-square deviation of a load of each controller from the average load of all controllers is greater than a pre-defined threshold. A controller is overloaded if its load is greater than the average load of all controllers and vice-versa. We introduce an algorithm for load balancing and prove that it achieves load balancing using the minimum number of switch migrations. Next, we analyze the time and space complexities of the algorithm. Furthermore, we evaluate OptiGSM using simulation and experimental studies. Numerical results indicate that OptiGSM outperforms conventional approaches in terms of the number of switch migrations, response time, throughput, and delay.

Density-based clustering methods such as DBSCAN are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which are not trivial. In this paper, the user-defined parameters of DBSCAN are evolved using a quantum-inspired genetic algorithm (QGA). The quantum-bit or Q-bit representation of a partition is an improvement over the more popular binary label-based representations and real-coded representation of partition cluster centers. A resulting algorithm called DBSCAN-QGA in the relational data space is proposed, and three different fitness functions are devised to evaluate partitions both in terms of cluster compactness and separation, and the relative number of entities classified as noise. The performance of the proposed algorithm is compared to synthetic and benchmark datasets from the UCI machine learning repository with encouraging results.

Although satellite-based remote sensing imagery has been widely demonstrated to be an effective means of detecting objects and structures, detecting the spatial and temporal bounds of human activity is inherently more complex, and research in this area is nascent. In this paper, we describe the performance metrics established for the IARPA Space-based Machine-Automated Recognition Technique (SMART) to track progress toward meeting this challenge. We also share the latest algorithm performance results from the SMART program.

The non-uniform sampling (NUS) techniques utilize the geometrical or structural characteristics of the signal to gather samples at irregular intervals. Previous works on NUS primarily concentrate on maximizing the reconstruction accuracy or optimizing the sample size. Nonetheless, a trade-off exists between these two factors, as increasing the sample size can improve the reconstruction accuracy but simultaneously decreases the bandwidth efficiency of the network. The reverse is true if the signal is under-sampled. Thus, it is essential to establish a balance between the two factors. This motivates us to consider the problem of determining the set of sample points with cardinality less than equal to a given$k$, such that the regenerated signal has the maximum reconstruction accuracy in terms of the percentage root mean square difference between the input and output signals. It is referred to as “CArdinalIty conStraint non-uniform Sampling (CAISOS)”. The paper presents an integer linear programming model for the CAISOS problem, but its time complexity increases exponentially with increasing signal size. To overcome this, the paper also proposes a polynomial-time heuristic approach based on the least cost (LC) branch-and-bound approach, and provides an extensive performance evaluation of both methods.

Smart contracts in Ethereum blockchain network are self-executable scripts used for managing tokenized assets and access rights between different entities on the blockchain network. They are significant addition to blockchain technology for achieving data transparency and reliability in maintaining the digital relationship between two or more entities. However, the smart contract is in its nascent stage and suffers from various limitations, including immutability and code secrecy. In this paper, we address the inability of smart contracts to access the on-chain data. The smart contract currently fetches the on-chain data by taking external oracle assistance, which can be compromised to influence the customers. To address the issue, we propose a decentralized mechanism for accessing the blockchain data directly from the smart contracts by indexing single or multiple parameters of a transaction in each block using the Merkle-Patricia Trie (MPT). The idea is to increase the data transparency of blockchain applications. The paper provides a sequential search methodology that can retrieve transactions from${N}$blocks, satisfying specific conditions, in${O}$(${N}$). The complexity is further reduced to${O}$(N/k) by partitioning the blockchain data into${k}$disjoint subsets to achieve parallelism in the search procedure. We experimentally verify the effectiveness of the proposed methodology with a case study on the Ethereum blockchain data.

The WHO estimates three million new cases of skin cancer each year. Therefore, there exists a need for prescreening tools that can estimate the biological parameters of human skin, as they can help detect cancers before metastasis. In this paper, we present a novel inverse modeling technique based on Kubelka-Munk theory and machine learning to estimate biological skin parameters from in vivo hyperspec-tral imaging. We use the k-nearest neighbors (k-NN) algorithm in order to estimate skin parameters from their hy-perspectral signatures. We test our methods on 241 hyper-spectral signatures obtained from both genders and three ethnicities, and find encouraging results.

The problem of identifying core patterns with the correct neighborhood parameters is a major challenge for density-based clustering techniques derived from the popular DBSCAN algorithm. An evolutionary approach to optimizing the assignment of core patterns is proposed in this paper. Key ideas presented here include a genetic representation that associates distinct neighborhood parameters with potential core patterns and specialized crossover and mutation operators. The evolutionary framework is based on the multi-objective NSGA-II algorithm, with simplified fitness measures derived from local (neighborhood) information. Clustering experiments on both synthetic and benchmark clustering datasets are presented and results are compared to the original DBSCAN, fuzzy DBSCAN and k-means.

Atmospheric compensation of hyperspectral data is important to a number of critical applications such as spectral unmixing and material identification. Current methods perform well at mitigating effects due to sunlight, skylight, and upwelled radiance, but these methods have no information about the geometric properties of the area being imaged. These geometric properties such as line-of-sight to the sun and sky are typically unknown to the standard atmospheric compensation techniques. This research proposes using Light Detection and Ranging (LIDAR) systems to provide a three-dimensional model of the area being imaged. This geometric model is combined with methods such as ELM to provide an improved atmospheric compensation technique that cannot be done with ELM alone. Results demonstrate the improved estimation of reflectance signatures and their effect on subpixel detection applications.

The inverse problem of parameter estimation for Duffing oscillator, a chaotic dynamical system well known in engineering is solved using quantum-inspired evolutionary algorithm, differential evolution and genetic algorithms. The paper focuses on such combination of parameters that produce periodic responses instead of purely chaotic responses. The feature set used is a set of displacement values of the first five Poincare points, after ignoring transient effects. All approaches correctly identify the target set of parameters as producing the given response; however, depending on the fitness landscape some parameters are more difficult to identify than others especially when using the canonical genetic algorithm. This paper is also the first to investigate the quantum-inspired evolutionary algorithm for such parameter identification problems.

In this paper we propose a new context-sensitive crossover operator for genetic search based clustering applications. The proposed crossover operator compares relevant sub-regions in partitions represented by the two parents selected for mating, passing on to the child only high fitness sub-regions in the partition space. The use of the restricted growth function as the representation for the genotype makes it easier to do a meaningful cluster-wise comparison between two partitions. Clusters are compared using a statistical basis for spatial randomness on the assumption that natural groupings in data are compact and isolated and therefore spatially random within themselves. The proposed crossover operator has good exploitation properties and is heavily biased against an exploratory genetic search because it identifies and necessarily passes good schemas to the child. Preliminary results on two datasets of varying complexity tend to prove this point - when the proposed crossover operator is used with high probability, the search quickly homogenizes and moves as a whole towards high fitness regions of the partition space. We have also presented results of simulations where we have explicitly attempted to balance the exploitation and the exploration aspects of the search by using the crossover operator sparingly during the initial generations, thereby preserving diversity and letting the search branch off towards multiple local optima in the partition space.

Support Vector Data Description (SVDD) methods have been successfully applied to hyperspectral anomaly detection. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse features could be identified for these techniques, the resulting data may improve SVDD performance while enjoying the benefits of decreased processing overhead. Although band selection has been investigated in previous efforts, this work builds on recent research that has resulted in the development of a theoretical framework for signal classification with sparse representation using L1measures.

Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to appropriately sample from the posterior distribution, maintain multiple hypotheses, and alleviate computational costs while preserving tracking accuracy. To address these issues, a novel utilization of the support vector data description (SVDD) density estimation method within the particle filtering framework is presented. The SVDD density estimate can be integrated into a wide range of PFs to realize several benefits. It yields a sparse representation of the posterior density that reduces the computational complexity of the PF. The proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the MAP estimate, and to directly sample from the posterior. We present several experiments that demonstrate the advantages of incorporating a sparse kernel density estimate in a particle filter.

We present a computational model of creative design based on collaborative interactive genetic algorithms. We test our model on floorplanning. We guide the evolution of floorplans based on subjective and objective criteria. The subjective criteria consists of designers picking the floorplan they like the best from a population of floorplans, and the objective criteria consists of coded architectural guidelines. We support collaboration by allowing individual designers to view each others' designs during the evolutionary process and the sharing of designs via case injection. This methodology supports team design, and reflects the view of creativity that collaboration accounts for much of our intelligence and creativity. We present a description of the model and a comparative study of floorplans created individually versus collaboratively. Our results show that floorplans created collaboratively were considered to be more ldquorevolutionaryrdquo and ldquooriginalrdquo than those created individually.

In this paper, we present a new method for estimating the number of endmembers for identification of materials in hyperspectral imagery. Unlike previous approaches, the proposed algorithm utilizes the Neyman-Pearson framework to estimate the number of endmembers that maximizes the probability of detection for a desired false alarm rate. The method can be used for any target signature, hyperspectral image, and detection algorithm. Results on multiple real-world hyperspectral images and material types will be provided showing the usefulness of the algorithm.

In this paper, we discuss distributed algorithms to construct ring-like overlays over a subset of scattered nodes in a static, random wireless ad hoc and sensor network (WASN). A ring-like overlay consists of a unidirectional ring plus side paths or loops, in which the given subset of nodes may appear multiple times. Different from a Hamiltonian cycle, a ring-like overlay is easier to construct and more efficient to operate. Yet, it can support many useful control operations in WASN such as mutual exclusion, clock synchronization, and cluster management. Compared with other topologies, a ring-like overlay allows conflict-free two-way communications, supports node ordering, and provides cost-free status feedbacks of operations. In this paper, we first present a distributed algorithm to construct a proximity-aware ring-like overlay in WASN. We then show optimization techniques to adapt the primitive overlays to meet the various application requirements.

We present IGAP, a peer to peer interactive genetic algorithm which reflects the real world methodology followed in team design. We apply our methodology to floorplanning. Through collaboration users are able to visualize designs done by peers on the network, while using case injection to allow them to bias their populations and the fitness function to adapt to subjective preferences.

This paper presents a recursive clustering scheme that uses a genetic algorithm-based search in a dichotomous partition space. The proposed algorithm makes no assumption on the number of clusters present in the dataset; instead it recursively uncovers subsets in the data until all isolated and separated regions have been classified as clusters. A test of spatial randomness serves as a termination criteria for the recursive process. Within each recursive step, a genetic algorithm searches the partition space for an optimal dichotomy of the dataset. A simple binary representation is used for the genetic algorithm, along with classical selection, crossover and mutation operators. Results of clustering on test cases, ranging from simple datasets in 2-D to large multidimensional datasets compare favorably with state of the art approaches in genetic algorithm-driven clustering.

This paper describes a new approach to finding a global solution for the fuzzy least trimmed squares clustering. The least trimmed squares (LTS) estimator is known to be a high breakdown estimator, in both regression and clustering. From the point of view of implementation, the feasible solution algorithm is one of the few known techniques that guarantees a global solution for the LTS estimator. The feasible solution algorithm divides a noisy data set into two parts -the non-noisy retained set and the noisy trimmed set, by implementing a pairwise swap of datum between the two sets until a least squares estimator provides the best fit on the retained set. We present a novel genetic algorithm-based implementation of the feasible solution algorithm for fuzzy least trimmed squares clustering, and also substantiate the efficacy of our method by three examples.

We present a method for fast anomaly detection in hyperspectral imagery (HSI) based on the support vector data description (SVDD) algorithm. The SVDD is a single class, non-parametric approach for modeling the support of a distribution. A global SVDD anomaly detector is developed that utilizes the SVDD to model the distribution of the spectra of pixels randomly selected from the entire image. Experiments on wide area airborne mine detection (WAAMD) hyperspectral data show improved receiver operating characteristic (ROC) detection performance when compared to the local SVDD detector and other standard anomaly detectors (including RX and GMRF). Furthermore, one-second processing time using desktop computers on several 256 times 256 times 145 datacubes is achieved.

Broadcast in radio-based wireless networks has been a difficult problem. When a node broadcasts, all nodes within its radio coverage will attempt to relay the message by rebroadcasting, causing excessive radio communication in the region that leads to the broadcast storm problem. Most previous solutions assumed a perfect radio condition with a static, circular coverage. However, in real situations, radio signals are very dynamic and irregular in their coverage. This is especially true for wireless sensor networks, which usually employ low quality radio modules to reduce the cost. Assuming no location information is available, we approach the problem in this paper from two aspects: space and time. To solve the irregularity in space in radio coverage, we propose a scheme that is able to find the neighbors on the rim of the irregular radio coverage region to narrow down the choices of the forwarders. To solve the dynamic in radio coverage, we consider an on-demand, stateless strategy to reduce the overhead of maintaining the state information. Through simulation, we compare the resultant algorithm, called RIM, with Double-Covered Broadcast (DCB) [10], one of the best algorithms proposed so far. The results show that under dense deployment and irregular and dynamic radio coverage, our algorithm performs better.

A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based upon the observation that the endmembers form a set of basis vectors for the hyperspectral datacube using the linear mixture model. The result is a fully-automatic method for endmember detection. Experimental results using the Cuprite datacube are presented.

A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

Many applications in wireless ad hoc and sensor networks involve only a subset of nodes in the network. A common practice is to form an overlay, such as a mesh or tree, to link the member nodes together. In this paper, we propose two novel distributed approaches for constructing ring-like overlays for static, random ad hoc and sensor networks. A ring-like overlay is a relaxed, unidirectional ring structure, with side paths or loops. Such a structure may be sufficient for global operations such as reliable file multicast, congestion control, and clock synchronization. We evaluate the resultant ring-like overlays by simulation. To the best of our knowledge, this is the first attempt to construct a ring-like overlay on random ad hoc and sensor networks using a distributed approach

This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery

One strategy to tackle the complexity and scalability issue in large-scale mobile ad hoc networks (MANETs) is to use extra layers of abstraction. A common tactic is to group the nodes in the network into clusters. The clusters and the paths between them constitute an extra layer of overlay abstraction. To maintain the overlay structure, a head node is often elected in each cluster. The head nodes form a backbone to provide, among other things, passages from one cluster to another. Nonetheless, the use of dedicated head nodes also creates problems such as load and power imbalance. In this paper, we investigate the feasibility of building a cluster-based overlay mesh on MANETs without using cluster heads. Without head nodes, the challenge is in maintaining the overlay structure and performing inter-cluster routing. We will examine one possible scheme through simulation.

A novel scheme for cluster validity using a test for random position hypothesis is proposed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by a partitioning algorithm. A test statistic such as the well-known Hopkins statistic could be used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here using two artificially constructed test data sets.

Summary form only given. In mobile agent research, agent-to-agent (A2A) communication often relies on an infrastructure of the discovery server to resolve the location of the agents. This not only induces overhead on managing, maintaining and deploying these discovery servers, but also complicates the development of agent applications. While considering scalability and reliability, such an agent discovery infrastructure becomes very sophisticated. We propose a novel A2A communication system, called Armada, which is autonomic and it does not require a discovery infrastructure. Armada is scalable and reliable, and suitable for a wide-area environment with dynamic entities. It does not pose mobile constrains to the agents. Each agent can be globally and uniquely identified without regarding its location. We evaluate Armada through large-scale simulation and prototype measurement in a cluster environment with 35-node desktop PCs. Results from both accesses presents an efficient design of Armada.

In this paper, a particular type of political game, one involving an attempt to hide a conspiracy, is analyzed. In classical game theory, parties or players have partly or completely conflicting interests. A game is developed, starting from an initial stage where the involved parties might not be in a conflict whatsoever. However with the development of the game, conflicts might arise and interests might diverge. This change can be reflected upon the game in subsequent stages with the introduction of fuzzy factors like global payoffs and motivation. The transition from an initial game to the final game using these fuzzy factors is demonstrated in this paper using the Watergate conspiracy affair as a case study.

A novel approach for detecting microcalcifications in digital mammograms is proposed. The noisy and impulsive nature of normal breast tissue in mammograms leads to high false-positive rates, thereby impairing detection performance. The authors address this issue by first observing that the distribution of pixel values is heavy-tailed. A statistical-physical noise model, based on the generalized central limit theorem and simple models for X-ray attenuation, distribution of breast tissue, and the digitization process, is presented to explain the presence of outliers. The authors use this model to derive an optimal statistical test to detect breast abnormalities in symmetric alpha-stable (S/spl alpha/S) noise. The resulting algorithm yields a constant false-alarm rate (CFAR) S/spl alpha/S microcalcification detector that is robust in impulsive noise environments. Experimental results on mammogram images from the Digital Database for Screening Mammography (DDSM) are provided to demonstrate the usefulness of the proposed approach.

We propose a segmentation method based on Polya's (1931) urn model for contagious phenomena. A preliminary segmentation yields the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. This process is implemented using contagion urn processes and generalizes Polya's scheme by allowing spatial interactions. The composition of the urns is iteratively updated by assuming a spatial Markovian relationship between neighboring pixel labels. The asymptotic behavior of this process is examined and comparisons with simulated annealing and relaxation labeling are presented. Examples of the application of this scheme to the segmentation of synthetic texture images, ultra-wideband synthetic aperture radar (UWB SAR) images and magnetic resonance images (MRI) are provided.

Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that (1) incorporates symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling, (2) constructs a two-dimensional (2-D) site model for deriving local context, and (3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the SalphaS model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.

We propose a segmentation method based on Polya's urn model for contagious phenomena. Initial labeling of the pixel is obtained using a Maximum Likelihood (ML) estimate or the Nearest Mean Classifier (NMC), which are used to determine the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. Examples of the application of this scheme to the segmentation of synthetic texture images, Ultra-Wideband Synthetic Aperture Radar (UWB SAR) images and Magnetic Resonance Images (MRI) are provided.

Target detection in foliage-penetrating (FOPEN), ultrawideband synthetic aperture radar (UWB SAR) images is a challenging problem. Given the low signal-to-clutter ratio, conventional detection algorithms perform poorly in FOPEN SAR images. Using symmetric alpha-stable (S/spl alpha/S) densities to model the impulsive noise, we have developed a region-adaptive automatic target detection (ATD) algorithm. The image is first segmented, and the resulting labeled image is exploited by a region-adaptive target detection algorithm. We evaluate the performance of the algorithm in different frequency bands, and determine which subbands are useful for image segmentation and target detection.