Problem In Many Applications Research Articles

ICOPS: insider attack detection in distributed file systems

Distributed file system (DFS) has been widely used in many applications. Insider attacks in DFS is a potential target that can cause problems in many applications. A malicious insider or an outside...

Missing Top Income Recipients

Low response rates among rich households are thought to be a serious problem in many applications using household surveys. The paper discusses the various ways the problem can be dealt with, and makes some recommendations for practice, including in developing countries. Under certain conditions, income-selective non-compliance with an initially randomized assignment can be corrected by reweighting the data. This requires that the surveys pick up at least some top incomes. If not, then income tax records can help, including in estimating distributional national accounts. However, tax data come with their own concerns including tax avoidance/evasion, weak coverage of informal sectors and illicit incomes, and concerns about construct validity, given the limitations of taxable income as a basis for inter-personal comparisons of economic welfare. An appropriately weighted survey-based distribution of an acceptable measure of economic welfare need not be less reliable for most purposes of distributional analysis than income-tax records, including in combination with surveys. The choice will depend on the question to be addressed, and country-specific circumstances. These measurement issues warrant further research across multiple settings. Classification-D63, I32, H31

On the Development of Ionization Waves Preceding Breakdown in a Long Capillary Tube Filled with Helium at Low Pressure

Experiments that revealed the main trends in the development of complete breakdown in a long capillary tube for different electrode configurations are reported. It is demonstrated that, in the presence of an additional external electrode around the tube, a fast stage completing the breakdown is preceded by a slow stage caused by development of a weak-current surface barrier discharge inside the tube. This discharge is realized due to propagation of forward (from the high-voltage electrode) and backward (from the grounded electrode) surface ionization waves. The presence of preliminarily, locally deposited charge of the sign opposite to polarity of the voltage pulse leads to acceleration of propagation of ionization waves in the tube. In the absence of the external electrode, the breakdown occurs much faster but is characterized by a large scatter in the delay time (up to complete lack of breakdown) relative to the applied voltage pulse. Unstable operation of the gas-discharge device determined by development of breakdown represents a much more serious problem in many applications compared to the situation in which it turns on slowly but stably. Therefore, the results demonstrating the possibility of controlling the breakdown are of great scientific and practical value.

A geometric approach to bubbling

Onset of bubbling, i.e., a sudden appearance of high frequency oscillations disrupting the waveform of a slowly oscillating signal in a restricted phase interval, is a serious problem for many applications in the field of power electronics. It has been shown in many publications that the appearance of bubbling is typically associated with a classical (smooth) bifurcation, as for example a pitchfork or a flip, but the mechanism leading to this phenomenon remained unclear. In the present work we focus on bubbling in nonautonomous models given by 1D maps. Using the recently developed cobweb diagrams for such maps, we suggest a novel geometric approach to deal with this phenomenon. As a first step, we explain the onset of bubbling in a model where it is not related to any smooth bifurcation. As a second step, we demonstrate that the provided explanation holds also for models where the onset of bubbling follows such a bifurcation. We show that bubbling does indeed appear soon, but not immediately, after a smooth bifurcation and explain why this fact has been overlooked before.

A Review on Shadow Removal in Images

Shadows are made when light from any source is deterred mostly or totally by a misty item. Presence of shadows creates problems in many applications like gesture identification, object recognition, image segmentation, traffic surveillance, etc. Shadows provide incorrect information about the shape, size and color of the object and make the processing step difficult. At the point when shadow of one article falls on another item, items may seem to blend. So it is an exceptionally critical assignment in numerous PC vision territories like division, following and so on. Many researches and experiments have been conducted for elimination of shadows in pictures and recordings. This paper endeavors to study the different shadow removal methods in an image with their advantages and disadvantages.

Introducing a holistic approach to model and link fouling resistances

Fouling is the unwanted deposition of soils on heat transfer surfaces and is a major challenge for industry and has been s subject to scientific investigations for decades, still being an unsolved problem for many applications. A fouling situation is commonly quantified with the thermal fouling resistance describing the integral fouling behavior of an apparatus. Modeling of this quantity is a permanent subject to research. This contribution presents the basics of an expanded consideration by introducing a holistic approach to model and link fouling resistances based on the extension of previous work in this field. A thermal and a mass based approach to calculate fouling resistances are considered integrally and locally. This will provide a detailed knowledge of the fouling behavior. Various variables are needed for modeling the different fouling resistances. Therefore, both experimental and analytical methods have to be applied to obtain the required data regarding local differences of crystallization deposits within double-pipe heat exchangers. Here the planned experimental and analytical approaches to receive all the required input data are described, also presenting the required test equipment briefly. Core equipment is a test rig equipped with double pipe heat exchangers, which allows the measurement of thermal and fluid flow related values and provides samples for the analysis of the fouling deposits. Furthermore, the aim of the new modeling concept is to link integral and local fouling resistances by taking into account locally varying parameters regarding the fouling layer. In order to allow for that, a recalculation of the thermal fouling resistance into a corrected version by considering heat transfer enhancing effects attempts to correlate with the mass based approach in a first step. In the end, the holistic modelling approach is presented.

A Novel Sampling Technique for Multistart-Based Methods

The problem of locating the global minimum of a function is a challenging one with application in many problems. A common method to tackle this problem is the so-called multistart method, which is the base method for many modern optimization methods. This article proposes a new sampling technique for multistart-based methods, that utilizes an artificial neural network as an approximator of the original objective function. The proposed sampling technique is tested against uniform sampling on a wide set of well-known benchmark optimization problems from the relevant literature and the results are reported.

A review on integrated nutrient management an approach to sustainability

Organic manures are used traditionally to supply plant nutrients. The volume of organic fertilizers is high they are bulky in nature so, there are many problems in application of these fertilizes which forces farmers to use synthetic fertilizers. Using organic fertilizers helps to recycle the organic waste. The end product which is obtained from organic waste can provides nutrients in a efficient manner and also improves conditions of the soil. Because synthetic fertilizers which are currently in the use may not fulfil or maintain the quality of soil needs for sustainable agriculture. By applying all essential nutrients through synthetic fertilizers can degrades the quality and health of the soil which directly responsible for unsustainable yields. So, to improve the nutrient supply in integrated nutrient management system chemical fertilizers and organic manures should be combined together for best outputs. To maintain the soil fertility and proper soil health balanced application of organic and inorganic nutrient is required. Through integrated management sustainability in agricultural productivity should be achived. By using Integrated nutrient management on yield components of crops is very important for guarantee of food security. Integrated nutrient management or supply focus at maintening the fertility of soil and nutrient supply for plants to an optimum level in relation to sustain the desired crop productivity by optimizing the all sources of plant nutrients with the help of integrated nutrient management which can also includes other aspects like; maintainence or enhancement of soil productivity by using chemical fertilizers and organic fertilizers in a balanced manner. Different types of organic materials such as bio compost, FYM, green manures, vermicompost, crop residues, industrial wastes, composts, animal manures.

Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval

The last few years have witnessed the rise of the big data era, in which approximate nearest neighbor search is a fundamental problem in many applications, such as large-scale image retrieval. Recently, many research results demonstrate that hashing can achieve promising performance due to its appealing storage and search efficiency. Since the complex optimization problems for loss functions are difficult to solve, most hashing methods decompose the hash codes learning problem into two steps: projection and quantization. In the quantization step, binary codes are widely used because ranking them by Hamming distance is very efficient. However, the huge information loss produced by the quantization step should be reduced in applications, such as image retrieval where high search accuracy is required. Since many two-step hashing methods produce uneven projected dimensions in the projection step, in this paper, we propose a novel dimension analysis based quantization method (DAQ) on two-step hashing methods for image retrieval. We first perform an importance analysis of the projected dimensions and select a subset of them that are more informative than the others, then we divide the selected projected dimensions into several regions with our quantizer. Every region is quantized with its corresponding codebook. Finally, the similarity between two hash codes is estimated by Manhattan distance between their corresponding codebooks, which is also efficient. We conduct experiments on three public benchmarks containing up to one million descriptors and show that the proposed DAQ method consistently leads to significant accuracy improvements over state-of-the-art quantization methods.

DOA estimation and tracking for FDA-MIMO radar signal

Direction-of-arrival (DOA) estimation is a fundamental problem in many applications of array signal processing. In practice, it is more important to track the DOAs of moving target signals. In this paper, we study the problem of tracking the DOAs of multiple signals in the frequency diversity array (FDA)-MIMO radar system. Aiming at FDA signals, we propose an efficient algorithm for DOA tracking, which combines subspace updating with the DOA estimation of the FDA signal. The proposed algorithm effectively solves the problem of angle-range coupling in the FDA signal. Moreover, it achieves excellent tracking performance and requires low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers

Image-level structural recognition is an important problem for many applications of computer vision such as autonomous vehicle control, scene understanding, and 3D TV. A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, is proposed. The template that reflects the symmetric structure consisting of a number of components represents a stage—a rough structure of an image geometry. The following image features are used: a histogram of oriented gradient (HOG) features showing the overall object shape, colors representing scene information, the parameters of the Weibull distribution features, reflecting relations between image statistics and scene structure, and local binary pattern (LBP) and entropy (E) values representing texture and scene depth information. Each of the individual classifiers learns a discriminative model and their outcomes are fused together using sum rule for recognizing the global structure of an image. The proposed method achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.

Similarity and diversity induced paired projection for cross-modal retrieval

The heterogeneous gap among cross modalities is a critical problem in many applications (e.g., retrieval). Considering that the main purpose of cross-modal learning is to learn a common representation while there also exist specific components across different modalities, a similarity and diversity induced paired projection (SDPP) method is proposed in this paper. SDPP not only extracts the correlation in a common subspace, but also removes the view-specific information which does not contribute to our task. In order to model the specific components, the Hilbert Schmidt Independence Criterion (HSIC) is introduced as a co-regularization to explicitly enforce the diversity. Additionally, different from some existing subspace learning methods which are time consuming in the testing phase, a paired projection strategy is exploited, being capable of obtaining the similar information in a simple but effective way. To optimize the presented approach, an efficient algorithm is designed to update different variables alternatively. Finally, we apply our strategy to the cross-modal retrieval, and experimental results on several real-world datasets substantiate the effectiveness and superiority of our model compared with other state-of-the-art methods.

Anti-Biofouling and Self-Cleaning Surfaces Featured with Magnetic Artificial Cilia.

The fouling of surfaces submerged in a liquid is a serious problem for many applications including lab-on-a-chip devices and marine sensors. Inspired by the versatility of cilia in manipulating fluids and particles, it is experimentally demonstrated that surfaces partially covered with magnetic artificial cilia (MAC) have the capacity to efficiently prevent attachment and adhesion of real biofouling agents—microalgae Scenedesmus sp. Actuation of the MAC resulted in over 99% removal of the algae for two different scenarios: (1) actuating the MAC immediately after injecting the algae into a microfluidic chip, demonstrating antifouling and (2) starting to actuate the MAC 1 week after injecting the algae into the chip and leaving them to grow in static conditions, showing self-cleaning. It is shown that the local and global flows generated by the actuated MAC are substantial, resulting in hydrodynamic shear forces acting on the algae, which are likely to be key to efficient antifouling and self-cleaning. These findings and insights will potentially lead to novel types of self-cleaning and antifouling strategies, which may have a relevant practical impact on different fields and applications including lab-on-a-chip devices and water quality analyzers.

Deep learning for prognostics and health management: State of the art, challenges, and opportunities

Abstract Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This paper provides a systematic review of state-of-the-art deep learning-based PHM frameworks. It emphasizes on the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. In addition, limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research.

Top-[formula omitted] closed co-occurrence patterns mining with differential privacy over multiple streams

The frequent pattern mining over data streams is a very important problem for many applications. However, many researches investigate a single stream in which every transaction is independent and it is not considered that some transactions are generated by the same individual. Some real-world applications involve multiple streams that continuously generate objects, and interesting observations are the objects appearing in many streams, such as emerging topic discovery, e-commerce, web usage pattern mining and location-based services. In this paper, we analyze the privacy problems in mining top-k closed co-occurrence patterns over multiple streams caused by single release of a window and continuous releases in successive windows. To prevent privacy leakage, we propose a differentially private top-k closed co-occurrence patterns mining algorithm across multiple streams with exponential mechanism and Laplace mechanism. The algorithm consists of dissimilarity calculation phase and differentially private mining phase, where differentially private mining phase includes adjusting CP-Graph with splitting transaction, perturbing CP-Graph to obtain the top-k closed co-occurrence patterns candidate set and adding noise to the supports of patterns. Finally, we prove our algorithm satisfies differential privacy and experiment results show the utility and efficiency of our algorithm.

A Vertex-Aligned Model for Packing 4-Hexagonal Clusters in a Regular Hexagonal Container

This paper deals with a problem the packing polyhex clusters in a regular hexagonal container. It is a common problem in many applications with various cluster shapes used, but symmetric polyhex is the most useful in engineering due to its geometrical properties. Hence, we concentrate on mathematical modeling in such an application, where using the “bee” tetrahex is chosen for the new Compact Muon Solenoid (CMS) design upgrade, which is one of four detectors used in Large Hadron Collider (LHC) experiment at European Laboratory for Particle Physics (CERN). We start from the existing hexagonal containers with hexagonal cells packed inside, and uniform clustering applied. We compare the center-aligned (CA) and vertex-aligned (VA) models, analyzing cluster rotations providing the increased packing efficiency. We formally describe the geometrical properties of clustering approaches and show that cluster sharing is inevitable at the container border with uniform clustering. In addition, we propose a new vertex-aligned model decreasing the number of shared clusters in the uniform scenario, but with a smaller number of clusters contained inside the container. Also, we describe a non-uniform tetrahex cluster packing scheme in the proposed container model. With the proposed cluster packing solution, it is accomplished that all clusters are contained inside the container region. Since cluster-sharing is completely avoided at the container border, the maximal packing efficiency is obtained compared to the existing models.

On the Performance of Quickest Detection Spectrum Sensing: The Case of Cumulative Sum

Quickest change detection (QCD) is a fundamental problem in many applications. Given a sequence of measurements that exhibits two different distributions around a certain flipping point, the goal is to detect the change in distribution around the flipping point as quickly as possible. The QCD problem appears in many practical applications, e.g., quality control, power system line outage detection, spectrum reuse, and resource allocation and scheduling. In this letter, we focus on spectrum sensing as our application since it is a critical process for proper functionality of cognitive radio networks. Relying on the cumulative sum (CUSUM), we derive the probability of detection and the probability of false alarm of CUSUM based spectrum sensing. We show the correctness of our derivations using numerical simulations.

Chaotic Atom Search Optimization for Feature Selection

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features.

NestMSA: a new multiple sequence alignment algorithm

Multiple sequence alignment (MSA) is a core problem in many applications. Various optimization algorithms such as genetic algorithm and particle swarm optimization (PSO) have been used to solve this problem, where all of them are adapted to work in the bioinformatics domain. This paper defines the MSA problem, suggests a novel MSA algorithm called ‘NestMSA’ and evaluates it in two domains: Web data extraction and removing different URLs with similar text (DUST). The suggested algorithm is inspired by the PSO optimization algorithm. It is not a generalization of a two-sequence alignment algorithm as it processes all the sequences at the same time. Therefore, it looks globally at the same time on all sequences. Different from other PSO-based alignment algorithms, swarm particles in the proposed NestMSA algorithm are nested inside the sequences and communicated together to align them. Therefore, global maximum is guaranteed in our algorithm. Furthermore, this work suggests a new objective function which both maximizes the number of matched characters and minimizes the number of gaps inserted in the sequences. The running time complexity and the efficiency of NestMSA are addressed in this paper. The experiments show an encouraging result as it outperforms the two approaches DCA and TEX in the Web data extraction domain (95% and 96% of recall and precision, respectively). Furthermore, it gives a high-performance result in the DUST domain (95%, 93% and 92% of recall, precision and SPS score, respectively).

PhaseNet 2.0: Phase Unwrapping of Noisy Data Based on Deep Learning Approach

Phase unwrapping is an ill-posed classical problem in many practical applications of significance such as 3D profiling through fringe projection, synthetic aperture radar and magnetic resonance imaging. Conventional phase unwrapping techniques estimate the phase either by integrating through the confined path (referred to as path-following methods) or by minimizing the energy function between the wrapped phase and the approximated true phase (referred to as minimum-norm approaches). However, these conventional methods have some critical challenges like error accumulation and high computational time and often fail under low SNR conditions. To address these problems, this paper proposes a novel deep learning framework for unwrapping the phase and is referred to as “PhaseNet 2.0”. The phase unwrapping problem is formulated as a dense classification problem and a fully convolutional DenseNet based neural network is trained to predict the wrap-count at each pixel from the wrapped phase maps. To train this network, we simulate arbitrary shapes and propose new loss function that integrates the residues by minimizing the difference of gradients and also uses $L_{1}$ loss to overcome class imbalance problem. The proposed method, unlike our previous approach PhaseNet, does not require post-processing, highly robust to noise, accurately unwraps the phase even at the severe noise level of −5 dB, and can unwrap the phase maps even at relatively high dynamic ranges. Simulation results from the proposed framework are compared with different classes of existing phase unwrapping methods for varying SNR values and discontinuity, and these evaluations demonstrate the advantages of the proposed framework. We also demonstrate the generality of the proposed method on 3D reconstruction of synthetic CAD models that have diverse structures and finer geometric variations. Finally, the proposed method is applied to real-data for 3D profiling of objects using fringe projection technique and digital holographic interferometry. The proposed framework achieves significant improvements over existing methods while being highly efficient with interactive frame-rates on modern GPUs.