Shuffling Method Research Articles

Sorting by Shuffling Methods and a Queue

We study sorting by queues that can rearrange their content by applying permutations from a predefined set. These new sorting devices are called shuffle queues and we investigate those of them corresponding to sets of permutations defining some well-known shuffling methods. If $\mathbb{Q}_{\Sigma}$ is the shuffle queue corresponding to the shuffling method $\Sigma$, then we find a number of surprising results related to two natural variations of shuffle queues denoted by $\mathbb{Q}_{\Sigma}^{\prime}$ and $\mathbb{Q}_{\Sigma}^{\textsf{pop}}$. These require the entire content of the device to be unloaded after a permutation is applied or unloaded by each pop operation, respectively.
 First, we show that sorting by a deque is equivalent to sorting by a shuffle queue that can reverse its content. Next, we focus on sorting by cuts. We prove that the set of permutations that one can sort by using $\mathbb{Q}_{\text{cuts}}^{\prime}$ is the set of the $321$-avoiding separable permutations. We give lower and upper bounds to the maximum number of times the device must be used to sort a permutation. Furthermore, we give a formula for the number of $n$-permutations, $p_{n}(\mathbb{Q}_{\Sigma}^{\prime})$, that one can sort by using $\mathbb{Q}_{\Sigma}^{\prime}$, for any shuffling method $\Sigma$, corresponding to a set of irreducible permutations.
 We also show that $p_{n}(\mathbb{Q}_{\Sigma}^{\textsf{pop}})$ is given by the odd indexed Fibonacci numbers $F_{2n-1}$, for any shuffling method $\Sigma$ having a specific \say{back-front} property. The rest of the work is dedicated to a surprising conjecture inspired by Diaconis and Graham, which states that one can sort the same number of permutations of any given size by using the devices $\mathbb{Q}_{\text{In-sh}}^{\textsf{pop}}$ and $\mathbb{Q}_{\text{Monge}}^{\textsf{pop}}$, corresponding to the popular \emph{In-shuffle} and \emph{Monge} shuffling methods.

BiSuT: A NoC-Based Bit-Shuffling Technique for Multiple Permanent Faults Mitigation

Since several decades, fault tolerance has become a major research field due to transistor shrinking and core number increasing in system-on-chip (SoC). Especially, faults occurring to the network-on-chips (NoCs) of those systems have a significant impact, due to the high amount of data, crossing the NoC, for the communication among intellectual properties (IPs). Furthermore, existing fault-tolerant approaches cannot efficiently deal with several permanent faults, which occur in NoC routers. To address these limitations, we propose the bit shuffling method (BiSuT) for fault-tolerant NoCs that reduces the impact of faults on data communications. To achieve that, the proposed approach exploits, at runtime, the position of permanent faults and changes the order of bits inside a flit. Our method reduces, as much as possible, the impact of faults by transferring the faults on least significant bits (LSBs), instead of keeping them on most significant bits (MSBs). The results obtained by extensive evaluations show that BiSuT can reduce the impact of multiple permanent faults, with low hardware costs, compared to the existing approaches, like the Hamming code.

Improving pseudo-optimal Kalman-gain localization using the random shuffle method

In present days, Iterative ensemble smoothers (IES) are among the main methods to perform ensemble-based history matching in petroleum reservoirs. Generally, some localization technique is applied to the IES to prevent ensemble collapse, which is the consequence of an excessive reduction of the posterior ensemble variance. When the standard distance-based localization is applied, the assimilation of non-local parameters is difficult, and besides that, this kind of methodology has also several intrinsic parameters that need to be defined before the assimilation. In contrast, adaptive localization methods aim to overcome the noticed problems of distance-based localization, by using some statistical method to define the localization. This article proposes a novel adaptive localization scheme, on top of two preexisting techniques: pseudo-optimal and correlation-based localizations. The motivation here is to further improve the adaptive localization scheme, by combining the strengths of these two preexisting techniques. The efficacy of the proposed localization scheme is tested in one 2D and one 3D case studies, whereas the latter case study involves a field-scale reservoir model with both local and non-local parameters, which often impose challenges on the conventional localization schemes. In comparison to other evaluated localization schemes, our results indicate that the proposed adaptive localization scheme achieves improved history matching performance.

DIESEL+: Accelerating Distributed Deep Learning Tasks on Image Datasets

We observe that data access and processing takes a significant amount of time in large-scale deep learning training tasks (DLTs) on image datasets. Three factors contribute to this problem: (1) the massive and recurrent accesses to large numbers of small files; (2) the repeated, expensive decoding computation on each image, and (3) the frequent communication between computation nodes and storage nodes. Existing work has addressed some aspects of these problems; however, no end-to-end solutions have been proposed. In this article, we propose DIESEL+, an all-in-one system which accelerates the entire I/O pipeline of deep learning training tasks. DIESEL+ contains several components: (1) local metadata snapshot; (2) per-task distributed caching; (3) chunk-wise shuffling; (4) GPU-assisted image decoding and (5) online region-of-interest (ROI) decoding. The metadata snapshot removes the bottleneck on metadata access in frequent reading of large numbers of files. The per-task distributed cache across the worker nodes of a DLT task to reduce the I/O pressure on the underlying storage. The chunk-based shuffle method converts small file reads into large chunk reads, so that the performance is improved without sacrificing the training accuracy. The GPU-assisted image decoding and the online ROI method minimize the image decoding workloads and reduce the cost of data movement between nodes. These techniques are seamlessly integrated into the system. In our experiments, DIESEL+ outperforms existing systems by a factor of two to three times on the overall training time.

Arnold Transform Based Medical Image Scrambling and Reconstruction Technique with Improved PSNR Parameter for Increasing the Robustness of Digital Watermarking Algorithms in Comparison with Random Shuffling Method

In this work, a new transform-based technique to secure the data in watermarking algorithm is proposed. Materials and methods: In this research, Arnold transform-based image scrambling method developed and compared with random shuffling method and 30 is the sample size of each group. All medical images are collected from standard medical database. Results: The execution of image scrambling algorithm is calculated using peak signal to noise ratio (PSNR) and universal index quality measure (UIQI) parameters. Low values of PSNR and UIQI represent higher scrambling. Arnold transform provides mean PSNR value of 7.1314db and UIQI is 69.0437%. Random shuffling provides mean PSNR value is 9.2890db, and UIQI is 75.3633%. Conclusion: Based on the experimental and the statistical analysis by using the independent sample T test, the Arnold transform-based image scrambling method performs significantly better than random shuffling method with PSNR (P=0.01) and UIQI (P=0.21).

A Contribution to Enhance the Panoramic Image Security via Cryptography and Shuffled Methods

Major changes have been accounted in the field’s of information technology(IT), the information that may in the form of audio, video and text, can be transferred anytime, from source to destination or vice versa that may located in anywhere in the World. The evolutions that have been made in the communication sectors, such wire and wireless networks, brought many merits for the end-users prospects, but at the same time, several vulnerabilities and threads have also been linked simultaneously with, and during the information exchanges. To keep the information secure before transmits from the source and receives at the destination, two generic security solutions are proposed which provide a significance security for the information that being travelled in the unsecure networks. First solution uses the cryptography mechanisms while the second solution employs a novel shuffling method, which deployed to enhance the security of information while travelling to the unsecured networks. In this study, the random sized panoramic images are considered as information that required to be secured over internet.

Analyzing the Function of Essential Genes by Plasmid Shuffling.

Investigations on gene essentiality have important implications in several fields of basic and applied research. A variety of strategies have been developed over the years to identify essential genes. Here, we describe an implemented plasmid shuffling method useful to assess the essentiality of overlapped genes under very stringent conditions. A host strain harboring the chromosomal deletion of the genes of interest is complemented by a thermosensitive plasmid carrying the copy of gene 1, gene 2, and rpsL allele, conferring streptomycin sensitivity to an otherwise resistant strain. A compatible plasmid harboring a different selectable marker and the copy of gene 2 only is transformed into the host strain, resulting in the coexistence of two plasmids. These cells are grown at high temperatures in a medium containing streptomycin. Under such conditions, viable cells are expected to contain only the incoming plasmid and to carry suppressor mutation(s) that bypass the loss of the essential gene 1. The system may thus represent a valuable tool to identify interactions between essential proteins and cell pathways.

Research on the Basketball Goal Recognition Method Based on Improved MobileNet

Moving target detection is involved in many engineering projects, but it is difficult because of the strong time-varying speed and uncertain path. Goal recognition is the key technology of the basketball goal automatic test. Also, accurate and timely judgment of basketball goals has important practical value. Therefore, a basketball goal recognition method based on an improved lightweight deep learning network model (L-MobileNet) is proposed. First of all, the basket detection is carried out by the Hough circle transform algorithm. Then, in order to further improve the detection speed of basketball goals, based on the lightweight network MobileNet, an improved lightweight network (L-MobileNet) is proposed. First of all, for deeply separable convolution, channel compression and block convolution reduce the parameters and computational complexity of the module. At the same time, because block convolution will hinder the information exchange between characteristic channels, an improved channel shuffling method, IShuffle, is introduced. Then, combined with the residual structure to improve the generalization ability of the network, the RLDWS module is constructed. Finally, a more lightweight network L-MobileNet is constructed by using the RLDWS module. The experimental results show that the proposed method can effectively realize the judgment of basketball goals, and the judgment accuracy is improved by 8.35%. At the same time, the amount of parameters and computation is only 29.7% and 53.2% of the original, and it also has certain advantages compared with other lightweight networks.

Transfer Learning Applied to Characteristic Prediction of Injection Molded Products.

This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.

Reduced-complexity decoding implementation of QC-LDPC codes with modified shuffling

Layered decoding (LD) facilitates a partially parallel architecture for performing belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes. Such a schedule for LDPC codes has, in general, reduced implementation complexity compared to a fully parallel architecture and higher convergence rate compared to both serial and parallel architectures, regardless of the codeword length or code-rate. In this paper, we introduce a modified shuffling method which shuffles the rows of the parity-check matrix (PCM) of a quasi-cyclic LDPC (QC-LDPC) code, yielding a PCM in which each layer can be produced by the circulation of its above layer one symbol to the right. The proposed shuffling scheme additionally guarantees the columns of a layer of the shuffled PCM to be either zero weight or single weight. This condition has a key role in further decreasing LD complexity. We show that due to these two properties, the number of occupied look-up tables (LUTs) on a field programmable gate array (FPGA) reduces by about 93% and consumed on-chip power by nearly 80%, while the bit error rate (BER) performance is maintained. The only drawback of the shuffling is the degradation of decoding throughput, which is negligible for low values of E_b/N_0 until the BER of 1e−6.

Optimization of well field management to mitigate groundwater contamination using a simulation model and evolutionary algorithm

Groundwater represents the most important available freshwater reserves and is of critical importance to global water and food security. Old environmental burdens that have led to the spread of contaminants in groundwater limit its use, thus interventions to mitigate contamination must often be carried out to ensure a safe drinking water supply. This study presents the optimization of well field management designs to reduce the desethylatrazine (DEA) concentration in the deep wells of the Brest Water Works (Central Slovenia). It investigates artificial recharge by injection wells using water from the nearby river and elaborates five well field management scenarios prioritizing different objectives. A multi-objective simulation-optimization framework was developed. A transient groundwater flow and solute transport model was applied to simulate the effects of the proposed recharge and pumping regimes. The shuffled complex evolution method was used to identify optimal values of well field management variables (location of injection well(s), minimum required injection rate, maximum pumping rate from production well) in the proposed scenarios. Model simulations showed that optimized well field management designs can significantly reduce DEA concentration in production wells (below 0.05 μg/L), assure compliance with water quality standards with (26%) reduced injection rate, and, with the implementation of two injection wells, achieve lower DEA concentration and higher pumping rate (up to 27 L/s). The optimization solutions depend on the defined well field management priorities and reveal a trade-off between the objectives (reduction of DEA concentration, increase of pumping rate, and reduction of injection rate). The impact of management variables on mitigation efficiency is not uniform and largely depends on the location of the injection well(s), which increases the complexity of mitigation design. The study has shown that the presented approach can be efficiently used for finding optimal mitigation designs and supporting water managers with information for planning mitigation measures.

Similarity Analysis of Alarm Sequences by a Shuffling Method

Modern telecommunication systems produce large amounts of alarm messages, and alarm management is vital for telecommunication systems’ high-quality performance. Building functional networks by observing the pair similarity between time series is a useful way to filter and reduce alarm messages. Because of the coexistence of positive and negative correlations among telecommunication devices, most of the similarity measures have troubles in computing the complex correlations. In this paper, we propose an index of measuring how much two-alarm series deviate from the uncorrelated situation to detect the correlation of both sides. Synthetic sequences verify our method. Furthermore, we apply our method to analyze telecommunication devices’ alarm correlation in a province of China. Our index of pair similarities is capable of measuring other discrete event data.

Channel–spatial attention-based pan-sharpening of very high-resolution satellite images

The pan-sharpening process aims to generate a new synthetic output image preserving the spatial details of panchromatic and spectral details of the multi-spectral image inputs. Recently, deep learning-based methods show substantial success in the remote sensing field mostly with the application of traditional Convolutional Neural Networks (CNNs). Most of the traditional CNN-based approaches treat all the channels equitably and cannot learn the correlation. Attention mechanism which can learn the correlations among the channels has been proven to be effective in super-resolution and object detection tasks. In this research, we introduced a novel deep learning framework, channel–spatial attention-based method for pan-sharpening (CSAPAN), by designing a Densely residual attention module (RAM). Besides, we train our model in the high-frequency domain and up-sample the low-resolution multispectral images by using the pixel shuffle method before stacking with the panchromatic images for further feature extraction. We evaluated our proposed CSAPAN along with traditional methods and CNN-based methods in reduced and full resolution and obtained satisfactory quantitative and qualitative results on Pleiades, Worldview-2, and QuickBird-2 satellite image datasets.

Improving spatial estimation of hydrologic attributes via optimized moving search strategies

The anisotropy and directional variation of spatial attributes is one of the most important challenges in spatial estimation of environmental features. This problem received a suitable response in the realm of stochastic modeling using directional variography; however, there is not any specific solution in the field of deterministic methods. In this research, Moving Least Square (MLS) and Moving Inverse Distance Weighting (MIDW) have been used to assess a moving domain as the remedy; although, there has been no research on the spatial estimation with the MLS method. The methods are evaluated in three case studies, (I) using anisotropic piezometric head data from the Wolfcamp aquifer in Texas, (II) long-term annual average precipitation measurements in Namak Lake Watershed (NLW) located in the central part of Iran, and (III) estimation of annual precipitation with remote-sensing products, TRMM, in NLW as well. To achieve the best performance of the methods, parameters have been optimized using the modified version of shuffled complex evolutionary method. Assessment of statistical metrics, Taylor Diagram, and numerical results showed that spatial interpolation using MIDW and MLS revealed better results, with an improvement of 45.3% on average in terms of Root Mean Square Error (RMSE), in comparison with original IDW as the benchmark evaluation approach. This shows the power of moving data partitioning on the performance of deterministic estimators. Additionally, MLS was more effective than MIDW with an average of 0.55, 10.53, and 17.19% reduction in RMSE of case I, II, and III, respectively.

Three-Phase AC–AC Converter With Diode Rectifier for Induction Heating Application With Improved Input Current Quality and Coil Modeling

In this article, a three-phase ac-ac resonant converter for an induction heating application is proposed. The system employs three coils in a coaxial arrangement, which is characterized using an resistive-inductive-capacitive (RLC) passive network. For obtaining the parameters of this network, a nonlinear regression from the experimental data was executed using the shuffled complex evolution optimization method, which stands out for its rapid convergence and for providing a well-structured and systematic search for the cost function minimization. The operation stages of the resonant converter are discussed in detail allowing to observe a zero voltage switching (ZVS) behavior for the resonant load inductive region. In addition, the converter operates at unitary power factor because of the smooth dc-link bus voltage and the input current filter. Finally, a 10-kW prototype was built to verify the theoretical analysis and the simulations. As from this comparison was established that the mutual inductance and stray capacitive effects were modeled accurately allowing to reproduce the induced voltages in the coils. Besides, it was corroborated that the coils arrangement influences in the distortion of the input currents, but this distortion can be minimized through the adequate selection of the switching phase angles.

Efficient Implementation of NIST LWC ESTATE Algorithm Using OpenCL and Web Assembly for Secure Communication in Edge Computing Environment.

In edge computing service, edge devices collect data from a number of embedded devices, like sensors, CCTVs (Closed-circuit Television), and so on, and communicate with application servers. Since a large portion of communication in edge computing services are conducted in wireless, the transmitted data needs to be properly encrypted. Furthermore, the application servers (resp. edge devices) are responsible for encrypting or decrypting a large amount of data from edge devices (resp. terminal devices), the cryptographic operation needs to be optimized on both server side and edge device side. Actually, the confidentiality and integrity of data are essential for secure communication. In this paper, we present two versions of security software which can be used on edge device side and server side for secure communication between them in edge computing environment. Our softwares are basically web-based application because of its universality where the softwares can be executed on any web browsers. Our softwares make use of ESTATE (Energy efficient and Single-state Tweakable block cipher based MAC-Then-Encrypt)algorithm, which is a promising candidate of NIST LWC (National Institute of Standards and Technology LightWeight Cryptography) competition and it provides not only data confidentiality but also data authentication. It also implements the ESTATE algorithm using Web Assembly for efficient use on edge devices, and optimizes the performance of the algorithm using the properties of the underlying block cipher. Several methods are applied to efficiently operate the ESTATE algorithm. We use conditional statements to XOR the extended tweak values during the operation of the ESTATE algorithm. To eliminate this unnecessary process, we use a method of expanding and storing the tweak value through pre-computation. The measured results of the ESTATE algorithm implemented with Web Assembly and the reference C/C++ ESTATE algorithm are compared. ESTATE implemented as Web Assembly is measured in web browsers Chrome, FireFox, and Microsoft Edge. For efficiency on server side, we make use of OpenCL which is parallel computing framework in order to process a number of data simultaneously. In addition, when implementing with OpenCL, using conditional statements causes performance degradation. We eliminated the conditional statement using the loop unrolling method to eliminate the performance degradation. In addition, OpenCL operates by moving the data to be encrypted to the local memory because the local memory has a high operation speed. TweAES-128 and TweAES-128-6, which have the same structure as AES algorithm, can apply the previously existing studied T-table method. In addition, the input value 16-byte is processed in parallel and calculated. In addition, since it may be vulnerable to cache-timing attack, it is safely operated by applying the previously existing studied T-table shuffling method. Our softwares cover the necessary security service from edge devices to servers in edge computing services and they can be easily used for various types of edge computing devices because they are all web-based applications.

Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands

The foliage density $(u_l)$ and the leaf angle distribution (LAD) are important properties that impact radiation transmission, interception, absorption and, therefore, photosynthesis. Their estimation in a forested scene is a challenging task due to their interdependence in addition to the large variability in the forest structure and the heterogeneity of the vegetation. In this work, we propose to jointly estimate both of them using terrestrial laser scanner (TLS) point cloud for different forest stands. Our approach is based on direct/inverse radiative transfer modeling. The direct model was developed to simulate TLS shots within a vegetation scene having known foliage properties (i.e., $u_l$ and LAD) resulting in a 3-D point cloud of the observed scene. Then, the inverse model was developed to jointly estimate $u_l$ and LAD decomposing the 3-D point cloud into voxels. The problem turns out to a high-dimensional cost function to optimize. To do it, the shuffled complex evolution method has been adopted. Our approach is validated with results derived from several simulated homogeneous and heterogeneous vegetation canopies as well as from actual TLS point cloud acquired from Estonian Birch, Pine, and Spruce stands. Our findings revealed that our estimates were considerably close to the actual $u_l$ and leaf inclination distribution function (LIDF) values with ( $\text{Biais}_{u_l} \in [0.001 \; 0.006]$ , $\text{RMSE}_{u_l} \in [0.019 \; 0.045]$ , $\text{RMSE}_{\text{LIDF}} \in [ 0.019 \; 0.038]$ ) for homogeneous dataset and ( $\text{Biais}_{u_l} \in [0.001 \; 0.045]$ , $\text{RMSE}_{u_l} \in [0.023 \; 0.078]$ , $\text{RMSE}_{\text{LIDF}} \in [ 0.011 \; 0.018]$ ) for heterogeneous dataset with different tree crown geometries (i.e., conical and elliptical). In the actual case (Birch, Pine, and Spruce stands), our approach with the traditional and novel techniques, $\text{RMSE}_{\text{LAI}}$ are 0.526 and 0.105, respectively. The results outperform those of the baseline technique (i.e., assuming spherical LAD) with $\text{RMSE}_{\text{LAI}}=2.651$ .

A Random Shuffle Method to Expand a Narrow Dataset and Overcome the Associated Challenges in a Clinical Study: A Heart Failure Cohort Example.

Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcome the issue of a narrow dataset cardinality (in a clinical dataset, the cardinality is the number of patients in that dataset), population-enhancing algorithms are therefore crucial. The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate, without the need of specific hypotheses and regression models. The cardinality enhancement was validated against an established random repeated-measures method with regard to the correctness in predicting clinical conditions and endpoints. In particular, machine learning and regression models were employed to highlight the benefits of the enhanced datasets. The proposed random shuffle method was able to enhance the HF dataset cardinality (711 patients before dataset preprocessing) circa 10 times and circa 21 times when followed by a random repeated-measures approach. We believe that the random shuffle method could be used in the cardiovascular field and in other data science problems when missing data and the narrow dataset cardinality represent an issue.

Deterministic Secure Quantum Communication on the BB84 System.

In this paper, we propose a deterministic secure quantum communication (DSQC) protocol based on the BB84 system. We developed this protocol to include quantum entity authentication in the DSQC procedure. By first performing quantum entity authentication, it was possible to prevent third-party intervention. We demonstrate the security of the proposed protocol against the intercept-and-re-send attack and the entanglement-and-measure attack. Implementation of this protocol was demonstrated for quantum channels of various lengths. Especially, we propose the use of the multiple generation and shuffling method to prevent a loss of message in the experiment.

Cost-effective moving target defense against DDoS attacks using trilateral game and multi-objective Markov decision processes

Moving Target Defense (MTD) has emerged as a game changer to reverse the asymmetric situation between attackers and defenders, and as one of the most effective countermeasures to mitigate DDoS attacks, shuffling-based MTD has gained ever-growing attention in cyber security. Despite the increased security, frequent shuffles would significantly bring heavy burden to the system. Moreover, most existing work has not adequately considered the impact of MTD techniques on the defender, and especially ignored that on legitimate users. Due to the lack of cost-effective shuffling methods, it is difficult to reach the optimal balance between the performance and overhead associated with the MTD deployment. Building on our preliminary work in this field, we propose a novel cost-effective shuffling method, which involves common users as a trilateral game for strategy generation and resists DDoS attacks with several MTD mechanisms. The novel game model extends our previous work to further describe the interaction among the attacker, the defender and users in detail, and we exploit Multi-Objective Markov Decision Processes to find the optimal MTD strategy by solving the trade-off problem between the effectiveness and cost of shuffling. By designing a trilateral game cost-effective shuffling algorithm, we capture the best MTD strategy and reach a balance between them in a given shuffling scenario. Simulation and experiments on an experimental software-defined network (SDN) indicate that our approach can effectively mitigate DDoS attacks with an acceptable overload, and exhibit better performance than other related and state of the art approaches.