Reviewerly/exHarmony huggingface dataset:
Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach

Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized.The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied.The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed.Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.
VectorDefense: Vectorization as a Defense to Adversarial Examples

Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstruct abstract concepts, we attempt to codify the input bitmap image into a set of compact, interpretable elements to avoid being fooled by the adversarial structures. We take the first step in this direction by experimenting with image vectorization as an input transformation step to map the adversarial examples back into the natural manifold of MNIST handwritten digits. We compare our method vs. state-of-the-art input transformations and further discuss the trade-offs between a hand-designed and a learned transformation defense.
Study of Residual Networks for Image Recognition

Deep neural networks have demonstrated a high potential on image classification tasks while presenting new computational challenges to the machine learning community. Due to the complexity and vanishing gradient problem, it normally takes longer time and more computational power to train deeper neural networks. To address some of these issues, deep Residual Networks (ResNets) can expedite the training process and attain more accuracy compared to their equivalent neural networks without the residual connections. ResNets often achieve this improvement by adding a simple skip connection parallel to convolutional layers in neural networks. Although over the past few years, ResNets have proven to be effective in advancing the performance of deep learning models, the best practices and trade-offs regarding adding residual connections to deep networks, and the exact improvement and disadvantages of these connections during the learning process are not well understood. In this project, we designed ResNet models that can perform a simple image classification task on the Tiny ImageNet datasets. For control, we then compare the performance of these ResNet models with their equivalent Convolutional Network (ConvNet) by removing the residual connections. Our findings illustrate that despite their higher accuracy, ResNets are more prone to overfitting, and that may depend on the depth of the network. We show that several methods to prevent overfittings, such as adding dropout layers and stochastic augmentation of the training dataset can be effective in attenuating this problem in ResNets.
DtCraft: A High-Performance Distributed Execution Engine at Scale

Recent years have seen rapid growth in data-driven distributed systems, such as Hadoop MapReduce, Spark, and Dryad. However, the counterparts for high-performance or compute-intensive applications including large-scale optimizations, modeling, and simulations are still nascent. In this paper, we introduce DtCraft, a modern C++ based distributed execution engine to streamline the development of high-performance parallel applications. Users need no understanding of distributed computing and can focus on high-level developments, leaving difficult details, such as concurrency controls, workload distribution, and fault tolerance handled by our system transparently. We have evaluated DtCraft on both micro-benchmarks and large-scale optimization problems, and shown the promising performance from single multicore machines to clusters of computers. In a particular semiconductor design problem, we achieved 30× speedup with 40 nodes and 15× less development efforts over hand-crafted implementation.
Development and Performance Validation of a Cylindrical Buoy for Deep-Ocean Tsunami Monitoring

This paper describes the development and performance validation of a cylindrical surface buoy (CSB) used for monitoring tsunamis in Indian seas. The buoy design with multiple compartments provides damage-tolerance curved top surface profile, the subsurface access ensures vandalism resistance, and the shape provides enough space for the power and control systems for an offshore endurance of two years. The scaled-down CSB is numerically modeled and experimentally validated for its stability in a wave basin. The validated numeric model is extended to a diameter of 2.4 m and the mooring loads are analyzed. The field scale prototype CSB is deployed in the open ocean, where the water depth is 2500 m. The performance of the CSB is validated from one year of failure-free performance and capability to remain within the watch circle under all environmental conditions, and it has detected the water level change events during the operational period.
On constructing alternative benchmark suite for evolutionary algorithms

Benchmark testing offers performance measurement for an evolutionary algorithm before it is put into applications. In this paper, a systematic method to construct a benchmark test suite is proposed. A set of established algorithms are employed. For each algorithm, a uniquely easy problem instance is generated by evolution. The resulting instances consist of a novel benchmark test suite. Each problem instance is favorable (uniquely easy) to one algorithm only. A hierarchical fitness assignment method, which is based on statistical test results, is designed to generate uniquely easy (or hard) problem instances for an algorithm. Experimental results show that each algorithm performs the best robustly on its uniquely favorable problem. The testing results are repeatable. The distribution of algorithm performance in the suite is unbiased (or uniform), which mimics any subset of real-world problems that is uniformly distributed. The resulting suite offers 1) an alternative benchmark suite to evolutionary algorithms; 2) a novel method of assessing novel algorithms; and 3) meaningful training and testing problems for evolutionary algorithm selectors and portfolios.
Triple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation

In this paper, new motion estimation (ME) and motion vector refinement (MVR) methods for motion-compensated frame interpolation (MCFI) are proposed. To estimate motion vectors (MVs) with great reliability, triple-frame-based bi-directional ME (TFBME), which employs three sequential frames, is developed. In TFBME, the middle frame works as a reference to estimate MVs between two end frames, which leads to performance improvement especially when objects move linearly through three sequential frames. In addition, a modified bi-directional ME method, which employs only two successive frames, is combined with TFBME to handle cases in which objects move non-linearly through three sequential frames. In our proposed MVR, artifacts on interpolated frames are first detected by a convolutional neural network and then corrected by weighted vector median filtering. Experimental results show that the proposed MCFI method outperforms conventional methods in both subjective and objective evaluations.
A comparison of mobile form controls for different tasks

Web forms are the design tools that are used for gathering inputs from users in interactive applications. These forms are mainly developed for desktop applications, and transferring them directly to a mobile context did not work well most of the time. Since user experience with mobile devices is different from with desktop computers, the efficiency and accuracy of form controls (buttons, text boxes, drop-down boxes, radio buttons, etc.) in the user interface is essential. In the scope of this research, a user study was conducted to compare the efficiency, effectiveness, and user satisfaction of mobile form controls. In the user study, an experiment was set up to compare four different form controls that were frequently used in mobile web applications, considering variation by task type and size of data set, in the context of two different touch-based smartphones, one running the iOS operating system and the other running the Android operating system. The form controls in the experiment were button, radio button/switch, spinner/picker, and text field. The task types were mutually exclusive selection, non-mutually exclusive selection, and set-a-value. Set sizes were designated as either small set size for less than 5 items or large set size for more than 5 items. There were 30 participants in the study. The task performance, accuracy, and satisfaction levels of the participants, as well as their perceived and actual performances, were recorded. The results showed that the button was fastest for small-set, mutually exclusive tasks while the spinner was fastest for large sets. In addition, it was found that the radio button/switch was the most effective controller for the non-mutually exclusive tasks. Based on the results of the experiment and analysis of the existing literature, five key issues for these form controls were identified to guide application developers in mobile form design: visibility, data set size, task type, users' experience level with a system, and screen layout.
SemaDrift: A hybrid method and visual tools to measure semantic drift in ontologies

Semantic drift is an active field of research, aiming to identify and measure changes in ontologies across versions in time, closely related to several fields such as ontology evolution and versioning. However, practical and widely adopted methods for measuring semantic drift are mostly either not directly applied to Semantic Web formalisms or largely dependent on specific models and domains. This paper presents a novel hybrid method, which combines existing identity-based and morphing-based approaches, and, in turn, introduces further insights such as hybrid chains of concepts across ontology versions and concept stability ranking. Furthermore, it introduces the SemaDrift Application Suite, which integrates both new and existing methods, including structural and text similarity measures, provided as open-source. The applications offer a graphical user interface to calculate and visually explore semantic drift metrics, in order to support and promote their use to Semantic Web and domain experts alike. Specifically, the SemaDrift plugin for the popular Protègè platform is intended for ontology engineers, while SemaDriftFx allows a wider audience of users to visually investigate drift either as numeric output or visually through graphs. Two use case scenarios demonstrate the applicability and usefulness of the methods and tools in the domain of digital preservation and Web Services, with insights previously hard to obtain. Standard scale and concrete end-user evaluation tasks were used to acquire positive preliminary feedback on the usability of the SemaDriftFx tool.
Tidal Current and Level Uncertainty Prediction via Adaptive Linear Programming

Short-term uncertainty prediction modeling of tidal power generation supports power systems in reserve and regulation markets. In tidal power generation via various tidal energy harvesting technologies, tidal current and level are the most influential factors. This paper addresses a nonparametric prediction interval (NPI)-based uncertainty model thereof. The proposed model adapts a bi-level optimization formulation, based on extreme learning machine (ELM) prediction engine and quantile regression (QR). The quantile probabilities are asymmetrically and adaptively chosen in the upper level optimization to make prediction intervals sharper for a specific reliability level (RL). Besides, the training process of ELM is improved by adaptively selecting ELM's hidden neurons via upper level optimization. The lower level optimization finds ELM's output weighting coefficients through linear programming of QR. The heuristic optimization, consisting of gray wolf optimizer and simplex method, is designed to facilitate the NPI with high exploration and exploitation capabilities in upper level optimization. The performance of the proposed NPI is examined using empirical data recorded in three different sites, located in North America. The results of case studies show that the proposed NPI can provide sharper PIs in comparison to the well-tailored rival models whilst a prespecified RL criterion is met.
Privacy Engineering for the Smart Micro-Grid

In developing countries, reliable electricity access is often undermined by the absence of supply from the national power grid and/or load shedding. To alleviate this problem, smart micro-grid (SMG) networks that are small scale distributed electricity provision networks composed of individual electricity providers and consumers, are being increasingly deployed. To ensure the reliable operation of SMGs, monitoring is necessary for data collection and state estimation processes. However, highly calibrated and trustworthy smart meters that are ideally suited to perform such monitoring tasks are often costly and non-ideally suited to SMGs which operate under unreliable communication network infrastructures. As a result, SMGs are an easy target to an adversary who can very easily gain access to private information by monitoring transmission between nodes in the SMG network, and launch inference-based privacy attacks. These attacks lead to electricity theft and grid instability problems in the SMG. The widely popular differential privacy (DP) technique (a rigorous technique in the family of privacy-preserving data publishing (PPDP) techniques to mathematically guarantee the preservation of data privacy) does not address multi-attribute correlations, that are inherently exploited by an adversary in inference attacks. In this paper, we propose HIDE, an oblivious computationally efficient, and rigorous information-theoretic privacy engineering framework for datasets/databases arising in the SMG environments that robustly accounts for multi-attribute correlations while preserving data privacy in a provably optimal fashion. A salient and powerful advantage of HIDE is its ability to generate optimal utility-privacy tradeoffs (computationally efficiently) when the privacy preserving entity in the worst case might have no prior statistical information that links a user's private data with his public data.
An Automated System for Position Monitoring and Correction of Chord-Based Rail Corrugation Measuring Points

Rail corrugation impacts the riding quality and safety of railway systems. The chord-based method is one of the common methods for rail corrugation dynamic measurement. However, during the process of continuous operation, it is hard to ensure that the measuring points on the rail surface can trace the railhead centerline well the whole time. This introduces the position deviations to the system, and leads to the measurement results not reflecting the true rail irregularity state. To solve the problem, an automated system for position monitoring and correction of chord-based measuring points is constructed in this paper. The proposed system comprises the position monitoring subsystem (PMS) and the position correction subsystem (PCS). The PMS monitors the positions of measuring points with given sampling interval, and calculates the position deviations with respect to the railhead centerline. Once the deviation exceeds the preset range, the PCS makes corresponding operations to pull the measuring points back to the effective range. Extensive experiments show that the error of PMS is limited within 0.6 mm, and the PCS can correct the position deviations in a timely manner according to the correction criteria. Furthermore, with 20-cm image sampling interval, our system can run at 6 km/h under our experimental setup, which is higher than that of rail maintenance trains (up to 5 km/h).
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration

Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of $$0.74\pm 0.26$$ mm and highly improved robustness. The success rate is increased from 79.3% to 94.3% and the capture range from 3 mm to 13 mm.
Two classes of permutation trinomials with Niho exponents

In this paper, we consider two classes of permutation trinomials with Niho-type exponents over the finite field F22m, where m is a positive integer. We transform the problem into investigating on some quartic equations (2k-th degree equations) over the subfield F2m in the first class (second class, respectively). We show that these equations have no solutions in F2m. Some sufficient conditions are established to characterize the coefficients in the two classes of permutation polynomials. The numerical result suggests that the sufficient conditions on the coefficients for the case of m odd in the first class, and in the second class, are also necessary.
Comparing performance of graph matching algorithms on huge graphs

Graph matching algorithms are gaining more and more interest in the last years from different scientific communities; indeed, they allow comparing any kind of objects represented using their intrinsic structure, represented in terms of attributed relational graphs. The challenge is to make these algorithms able to provide solutions over huge graphs, with many thousands of nodes, and in a time that is adequate for practical applications; in this paper, we propose a comparison among the best performing algorithms available in the literature on a variety of very large graph databases used for performance assessment. The chosen datasets vary in terms of graph structure, size, density, presence of symmetric or repetitive substructures; this variability makes such datasets very challenging. The aim of this paper is to characterize the performance of the compared algorithms with respect to the typology, the size and other structural properties of the graphs; in this way, the user may consciously select the best suited algorithm for a given purpose. The results of an impressive experimentation that required 556 days of machine time are here presented and extensively discussed.
Learning channel-aware deep regression for object tracking

Deep regression tracker assigns soft Gaussian labels to the dense samples drawn on convolution feature map. It has become a popular tracking framework as its intrinsic end-to-end learning characteristics. In this paper, we analyze the channel-wise information of convolution features in deep regression tracking and propose an effective channel-aware learning algorithm. The proposed algorithm can automatically select the most representative feature channels during the learning of deep regression tracker. We show that this channel selection procedure can be formulated as multi-task learning (MTL) and solved via simple gradient descent. Extensive experiments on OTB-2013 and OTB-2015 datasets validate the effectiveness of the proposed method.
Could emerging fraudulent energy consumption attacks make the cloud infrastructure costs unsustainable?

Cloud paradigm is vulnerable to emerging breeds of fraudulent energy-related threats, which seek to exploit the cloud elasticity and the multi-tenant model. Recently, several sophisticated attacks have been reported by the cloud customers, which induced sustained and prolonged fraudulent resource consumptions, making the cloud costs unsustainable. If properly orchestrated, such attacks can also significantly affect the cloud service providers, forcing a frequent scaling and migration of virtual machines in the cloud. Such attacks aim at exploiting the elasticity and multi-tenacity of the cloud paradigm, in order to compromise the long-term financial viability of operating in the cloud, and thus, inflicting significant energy cost and loss of reputation to the cloud provider. This paper discusses the vulnerabilities associated to such a new breed of attacks, paying special emphasis to the risks for the cloud service providers. Practical experiments and simulations have been used to demonstrate the vulnerability of the cloud resource manager against emerging energy-related threats, named Fraudulent Energy Consumption attacks. Finally, some countermeasures are also discussed.
A substring–substring LCS data structure

For any rectangular grid graph with grid units each potentially having a diagonal edge, we propose a data structure that is constructible in time linear in the number of grid units in the graph and supports linear-time queries of a shortest path between any pair of vertices in the graph. Setting the position of grid units having a diagonal edge appropriately, we can use this data structure to solve a local variant of the longest common subsequence problem or certain related problems in linear time.