Shuffling Process Research Articles

New Colorful Image Encryption Method Using Triple Chaotic Maps and Grey Wolf Optimization (GWO)

A novel image encryption algorithm employing triple chaotic maps has been developed to address the shortcomings of existing methods in terms of security and efficiency. The algorithm leverages the interconnectivity of color channels in images, using distinct keys to disrupt pixel correlations within each channel. The three chaotic maps utilized URUK, WAM, and Nahrain to generate two sets of keys. The first set is used to shuffle pixel positions, creating scrambled channels. Subsequently, the second set is applied to diffuse these scrambled channels independently. A gray wolf optimization (GWO) algorithm is then employed to further optimize the shuffling process, minimizing pixel correlations and enhancing security. The triple chaotic maps of varying orders contribute to the unpredictability and robustness of the cipher image. A comprehensive security analysis, including entropy, correlation coefficients, and attack resistance, demonstrates the superior performance of the proposed method compared to existing image encryption algorithms

First-principles analysis of the effects of oxygen, vacancies, and their complexes on the screw dislocation motion in body-centered cubic Nb

Some solute atoms induce strengthening and embrittlement in body-centered cubic refractory metals. Especially, interstitial oxygen produces remarkable strengthening effects in Nb, wherein the yield stress of oxygen-doped Nb alloys is more than twice that of pure Nb. Conventional mechanisms cannot explain this oxygen-induced dramatic strengthening because the interaction between dislocations and oxygen atoms is not so significant. In a previous study, we found that the formation of vacancy–oxygen pairs enhances the attractive interaction with a screw dislocation and increases the energy barrier for dislocation motion associated with cross-kink nucleation in Nb–O alloys. However, the strengthening effect could not be described by the pinning model for dislocation motion. Herein, we focused on the atomic-level analysis of the fundamental process related to the dislocation motion around a vacancy, an oxygen atom, and a vacancy–oxygen pair. First-principles calculations revealed that the vacancy–oxygen pairs increase the energy barrier with respect to the dislocation motion more substantially than vacancies and oxygen interstitials owing to a unique oxygen-induced mechanism; an octahedral–tetrahedral shuffling process of oxygen is necessary for dislocation passing through vacancy–oxygen obstacles. Such event almost never happens in the real metallic materials. Instead, cross-kink nucleation occurs frequently to overcome the widely distributed vacancy–oxygen obstacles, which contributes to the dramatic strengthening.

Shuffled ARSH-FATI: A Novel Meta-Heuristic for Lifetime Maximization of Range-Adjustable Wireless Sensor Networks

Wireless Sensor Network (WSN) is an essential component of the Internet-of-Things (IoT), enabling the creation of a vast network of interconnected devices and systems that can interact with each other and with the physical world. WSNs are widely used for a variety of applications, including environmental monitoring, healthcare, industry 4.0, agriculture, transportation, and surveillance. WSN consists of devices with limited residual energy therefore, the lifetime improvement of WSN is critical for achieving the full potential of IoT and enabling the creation of more robust and reliable connected systems. This paper explores the problem of Lifetime Maximization of Range Adjustable Sensors (LM-RAS). The goal is to optimize the lifetime of the WSN consisting of range-adjustable sensors while satisfying two constraints. Firstly, all targets in the network must be monitored simultaneously. Secondly, the activation time of each sensor should be limited by its battery energy. A novel meta-heuristic called Shuffled ARSH-FATI has been proposed that embeds ARSH-FATI into a multi-population method. Shuffled ARSH-FATI solves the LM-RAS problem by splitting it into two sub-problem. The first sub-problem deals with the creation of energy-efficient coverage schemes and the second sub-problem deal with the schedule of these schemes. Shuffled ARSH-FATI utilizes a Linear Programming (LP) model to generate an optimal schedule for the coverage schemes but its performance greatly depends on the quality of the coverage schemes. Shuffled ARSH-FATI simultaneously explores several regions of the search space by partitioning the coverage schemes in population into several sub-populations. It evolves each sub-population (in this problem a sub-set of coverage schemes) independently using ARSH-FATI. It incorporates the shuffling process via Merge, Shuffle, and Partition (MSP) algorithm that merges the sub-populations, shuffles, and then partitions the merged population into new sub-populations. The shuffling process along with the strategy to fine-tune the coverage scheme into energy-efficient coverage schemes maintains population diversity throughout the optimization. The diverse high-quality coverage scheme improves the performance of the LP model.

Capacitor Mismatch Calibration of a 16-Bit SAR ADC Using Optimized Segmentation and Shuffling Scheme

This brief presents a 16-bit successive approximation register (SAR) analog-to-digital converter (ADC) with inputsignal-independent background calibration. A serial double conversion (SDC) method with second MSB decisions skipped is proposed to perform A/D conversion and background calibration simultaneously, with only one ADC and little extra time. Deep and input-signal-independent calibration is achieved with the proposed optimized segmentation and shuffling (OSAS) scheme, which uses customized shuffling and paired-swapping to inject only the weight error of DAC capacitors into the residue, including the bridge capacitor. In addition, by making the shuffling process more flexible, the OSAS scheme speeds up the convergence of calibration. The proposed calibration is experimentally validated on a prototype 16-bit SAR ADC fabricated in 180-nm CMOS technology, demonstrating an SFDR and SNDR improvement of 30.2 dB and 25.3 dB at 1-MS/s, respectively, and the calibration converged after only 3,000 samples.

Pengenalan Bendera Negara Dengan Fisher Yates- Shuffle Pada Game Edukasi Android Menggunakan Metode GDLC

This research aims to address the negative impact of less educational games by creating an interactive educational game for Android that combines learning about national flags and memorizing flag images, thus enhancing logical thinking skills in children. It is developed using the Flutter framework as the development platform and follows the GDLC method, with alpha testing conducted using the blackbox method. The Fisher-Yates Shuffle algorithm is implemented for shuffling the flag images and the order of elements in the card list. The testing method involves using the Debug Console in Visual Studio Code to verify the success of the shuffling process. The conducted testing, performed six times, demonstrates the algorithm's success in shuffling the card order for each level. In the easy level, the randomized order is 6, 10, 8, 5, 3, 4, 11, 2, 7, 1, 12, 9. In the normal level, the randomized order is 6, 11, 20, 16, 1, 13, 19, 17, 9, 2, 15, 4, 10, 12, 14, 8, 5, 7, 2, 18. In the hard level, the randomized order is 2, 14, 1, 21, 7, 18, 24, 22, 10, 23, 6, 11, 15, 9, 17, 4, 8, 3, 5, 13, 16, 12, 19, 20. The Fisher-Yates Shuffle algorithm successfully generates diverse shuffling of flag images and avoids repeating the random order of cards each time the game starts. Thus, this game can provide stimulation to the brain and encourage children to effectively enhance their object memory skills.

Android-Based Muhammadiyah Organization Introduction Application Using the Linear Congruent Generator Method

The aim of the research is to build an android-based application for introducing the Muhammadiyah organization using a Linear Congruent Generator, to introduce Muhammadiyah teachings to many people more easily and to provide knowledge about Muhammadiyah worship procedures. its function in the development of Islam in Indonesia, which so far has been carried out through branches and branches throughout the archipelago. The solution to this problem is to introduce Muhammadiyah teachings, it requires the help of technology that is currently developing. By using Android, we can introduce Muhammadiyah to more people because it can be used anywhere and anytime. In the application, it will be displayed starting from the history of the founding of Muhammadiyah, Muhammadiyah teachings and Muhammadiyah practical worship. It is hoped that with this application in Test results The nature of the Linear Congruent Method (LCM) method is that repetition occurs at a certain time period or after a certain number of times of generation so that there are no repetitions in the problem, the choice of constant values ​​must be correct and the existence of a shuffling process, helps overcome the problem of Linear Congruential Generator (LCG).

Optimization of the Join between Large Tables in the Spark Distributed Framework

The Join task between Spark large tables takes a long time to run and produces a lot of disk I/O, network I/O and disk occupation in the Shuffle process. This paper proposes a lightweight distributed data filtering model that combines broadcast variables and accumulators using RoaringBitmap. When the data in the two tables are not exactly matched, the dimension table Key is collected through the accumulator, compressed by RoaringBitmap and distributed to each node using broadcast variables. The distributed fact table data can be pre-filtered on the local server, which effectively reduces the data transmission and disk reading and writing in the Shuffle phase. Experimental results show that this optimization method can reduce disk usage, shorten the running time and reduce network I/O and disk I/O for Spark Join tasks in the case of massive data, and the effect is more obvious when the two tables have a higher incomplete matching degree or a fixed matching degree but a larger amount of data. This optimization scheme has the advantages of being easy to use, being easy to maintain and having an obvious effect, and it can be applied to many development scenarios.

Towards Accelerating Data Intensive Application's Shuffle Process Using SmartNICs

The wide adoption of the emerging SmartNIC technology creates new opportunities to offload application-level computation into the networking layer, which frees the burden of host CPUs, leading to performance improvement. Shuffle, the all-to-all data exchange process, is a critical building block for network communication in distributed data-intensive applications and can potentially benefit from SmartNICs. In this paper, we develop SmartShuffle, which accelerates the data-intensive application's shuffle process by offloading various computation tasks into the SmartNIC devices. SmartShuffle supports offloading both low-level network functions, including data partitioning and network transport, and high-level computation tasks, including filtering, aggregation, and sorting. SmartShuffle adopts a coordinated offload architecture to make sender-side and receiver-side SmartNICs jointly contribute to the benefits of shuffle computation offload. SmartShuffle carefully manages the tight and time-varying computation and memory constraints on the device. We propose a liquid offloading approach, which dynamically migrates operators between the host CPU and the SmartNIC at runtime such that resources in both devices are fully utilized. We prototype SmartShuffle on the Stingray SoC SmartNICs and plug it into Spark. Our evaluation shows that SmartShuffle improves host CPU efficiency and I/O efficiency with lower job completion time. SmartShuffle outperforms Spark, and Spark RDMA by up to 40% on TPC-H.

Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset

Efforts have been made to improve the risk stratification model for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using machine learning models with iterated cross validation (CV) method. A total of 122 patients with pathologically confirmed DLBCL and receiving rituximab-containing chemotherapy were enrolled. Contributions of clinical, laboratory, and metabolic imaging parameters from fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scans to the prognosis were evaluated using five regression models, namely logistic regression, random forest, support vector classifier (SVC), deep neural network (DNN), and fuzzy neural network models. Binary classification predictions for 3-year progression free survival (PFS) and 3-year overall survival (OS) were conducted. The 10-iterated fivefold CV with shuffling process was conducted to predict the capability of learning machines. The median PFS and OS were 41.0 and 43.6 months, respectively. Two indicators were found to be independent predictors for prognosis: international prognostic index and total metabolic tumor volume (MTVsum) from FDG PET/CT. For PFS, SVC and DNN (both with accuracy 71%) have the best predictive results, of which outperformed other algorithms. For OS, the DNN has the best predictive result (accuracy 76%). Using clinical and metabolic parameters as input variables, the machine learning methods with iterated CV method add the predictive values for PFS and OS evaluation in DLBCL patients.

A Utility-Based Distributed Pattern Mining Algorithm With Reduced Shuffle Overhead

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability.

Multi-level encryption for 3D mesh model based on 3D Lorenz chaotic map and random number generator

<span lang="EN-US">The increasing 3D model applications in various areas of life and widespread use like industry leads to 3D models being stolen and attacked by hackers; therefore, 3D model protection is a fundamental matter nowadays. In this paper, the proposed scheme will provide stringent security for the 3D models by implementing multiple levels of security with preserving the original dimensionality of the 3D model using the weight factor (w). The first level of security is achieved by applying a shuffling process for the vertices based on a key from random number generator (RNG), which provides good confusion. The second level is implemented by modifying the vertices values based on 3D keys from 3D Lorenz chaotic map, which provides good diffusion. The proposed scheme was applied on different 3D models varying in the vertices and faces number. The results illustrate that the proposed scheme deforms the entire 3D model based on Hausdorff distance (HD) approximately 100 after the encryption process, making it resist statistical attack. The scheme provides high security against brute force attack because it has a large key space equal to 10,105 and high security against deferential attack through secret key sensitivity using number of pixels change rate (NPCR) near to 99:6% and unified average changing intensity (UACI) near to 33:4%.</span>

STSIIML: Study on token shuffling under incomplete information based on machine learning

Transaction data on the public chain is open and transparent to all participants, which poses a potential threat to the privacy of participants. Some privacy-conscious token holders want to employ obfuscation methods to protect the origin and destination of their tokens, and the need for token shuffling services (TSS) arose at a historical moment. The prevailing token shuffling policies rely too heavily on blacklists in the token shuffling process, which is contrary to the idea of decentralization. Therefore, weakening or even eliminating the usage of blacklisting mechanisms in TSSs, which is an urgent issue to be addressed. In this paper, we adopt the idea of machine learning and propose a general framework for TSSs, which replaces the natural role under incomplete information with machine learning, so as to achieve the goal of eliminating the blacklist mechanism in TSSs. Then, the token shuffling process is constructed as an extended game under incomplete information on the basis of different token shuffling policies, and this incomplete information game is analyzed on the basis of poison policy, haircut policy, and suicide policy respectively. Finally, the sequential equilibrium under different games is investigated through simulations. The simulation results show that in the incomplete information game based on the poison policy, the participation of two players in the TSS is a sequential equilibrium, while in the incomplete information game based on the haircut policy, the players do not participate in the TSS is an sequential equilibrium.

Shuffling between Cladocopium and Durusdinium extensively modifies the physiology of each symbiont without stressing the coral host

As sea surface temperatures increase, many coral species that used to harbour symbionts of the genus Cladocopium have become colonized with the thermally tolerant genus, Durusdinium. Here, we asked how gene expression in the symbionts of one genus changes depending on the abundance of another symbiont genus within the same coral host, and what effect this interaction has on the host. Symbiont gene expression was overwhelmingly driven by whether the genus was the minority or the majority within the host, which affected 79% (Durusdinium) and 96% (Cladocopium) of all genes. Particularly strong effects in both genera were observed for photosynthesis components (upregulated in the minority state) and proteins putatively associated with cell motility (upregulated in the majority state). Importantly, there was no distinct gene expression signature associated with the mixed symbiosis state when both genera were represented in comparable proportions within the host, which could lead to more intense competition. The mixed symbiosis was also not associated with elevated host stress: in fact, after heat treatment, stress signatures were the lowest in mixed-symbiosis corals compared to both Cladocopium- and Durusdinium-dominated corals. In conclusion, during shuffling between Cladocopium and Durusdinium both symbiont genera go through extensive and largely reciprocal physiological transitions, but there is no evidence of intensifying antagonistic interactions that are detrimental to the host. Unless the mixed-symbiosis corals in this study are not representative of the typical transition between Cladocopium and Durusdinium, the process of shuffling from one symbiont genus to another appears to be cost-free for the coral host, and even appears to be associated with lower stress susceptibility. This raises optimism for the future corals, which will probably have to rely on symbiont shuffling more and more to withstand environmental challenges.

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation.

Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

Security analysis of an audio data encryption scheme based on key chaining and DNA encoding

Fairly recently, a new audio encryption scheme has been proposed. The cryptosystem is based on a substitution-permutation algorithm using DNA encoding. The key-generation of this proposed scheme is based on a key chaining mode, that generates a new key block for every plain block using the chaotic logistic map. After several statistical tests, handled by the authors of the scheme, they claimed that their cryptosystem is robust. In this paper, we scrutinize the cryptosystem from a cryptanalytic perspective, and we handle several security attacks to evaluate the immunity of the system, and to assess its possible adoption in real-world applications. We demonstrate two successful conventional attacks on the scheme, which are: the chosen ciphertext and chosen-plaintext attacks. The cryptosystem’s shuffling process design is scrutinized as well, and a cycle attack is described using the drawn results. Lessons learned from this cryptanalytic paper, are then outlined in order to be considered in further designs and proposals.

Efficient shuffle management for DAG computing frameworks based on the FRQ model

In large-scale data-parallel analytics, shuffle, namely the cross-network read and the aggregation of partitioned data between tasks with data dependencies, usually bring in large overhead. To reduce shuffle overhead, we present SCache, an open-source plug-in system that particularly focuses on shuffle optimization. SCache adopts heuristic pre-scheduling combining with shuffle size prediction to pre-fetch shuffle data and balance load on each node. Meanwhile, SCache takes full advantage of the system memory to accelerate the shuffle process. We also propose a new performance model called Framework Resources Quantification (FRQ) model to analyze DAG frameworks and evaluate the SCache shuffle optimization. The FRQ model quantifies the utilization of resources and predicts the execution time of each phase of DAG jobs. We have implemented SCache on both Spark and Hadoop MapReduce. The performance of SCache has been evaluated with both simulations and testbed experiments on a 50-node Amazon EC2 cluster. Those evaluations have demonstrated that, by incorporating SCache, the shuffle overhead of Spark can be reduced by nearly 89%, and the overall completion time of TPC-DS queries improves 40% on average. On Apache Hadoop MapReduce, SCache optimizes end-to-end Terasort completion time by 15%.

Analysis of Complex DNA Rearrangements during Early Stages of HAC Formation.

Human artificial chromosomes (HACs) are important tools for epigenetic engineering, for measuring chromosome instability (CIN), and for possible gene therapy. However, their use in the latter is potentially limited because the input HAC-seeding DNA can undergo an unpredictable series of rearrangements during HAC formation. As a result, after transfection and HAC formation, each cell clone contains a HAC with a unique structure that cannot be precisely predicted from the structure of the HAC-seeding DNA. Although it has been reported that these rearrangements can happen, the timing and mechanism of their formation has yet to be described. Here we synthesized a HAC-seeding DNA with two distinct structural domains and introduced it into HT1080 cells. We characterized a number of HAC-containing clones and subclones to track DNA rearrangements during HAC establishment. We demonstrated that rearrangements can occur early during HAC formation. Subsequently, the established HAC genomic organization is stably maintained across many cell generations. Thus, early stages in HAC formation appear to at least occasionally involve a process of DNA shredding and shuffling that resembles chromothripsis, an important hallmark of many cancer types. Understanding these events during HAC formation has critical implications for future efforts aimed at synthesizing and exploiting synthetic human chromosomes.

Modular Cyclopentenone Synthesis through the Catalytic Molecular Shuffling of Unsaturated Acid Chlorides and Alkynes.

We describe a general strategy for the intermolecular synthesis of polysubstituted cyclopentenones using palladium catalysis. Overall, this reaction is achieved via a molecular shuffling process involving an alkyne, an α,β-unsaturated acid chloride, which serves as both the alkene and carbon monoxide source, and a hydrosilane to create three new C-C bonds. This new carbon monoxide-free pathway delivers the products with excellent yields. Furthermore, the regioselectivity is complementary to conventional methods for cyclopentenone synthesis. In addition, a set of regio- and chemodivergent reactions are presented to emphasize the synthetic potential of this novel strategy.

Improved Shuffled Jaya algorithm for sizing optimization of skeletal structures with discrete variables

Jaya algorithm is a simple and efficient population-based metaheuristic algorithm. Besides its simplicity, it has free from any algorithm-specific parameters. Although it has these advantages, the Jaya algorithm suffers from some shortcomings including unwanted premature convergence and the possibility of being trapped in local minima due to insufficient population diversity. To alleviate these handicaps, this paper proposes an Improved Shuffled based Jaya (IS-Jaya) algorithm. The proposed optimization method uses the concept of shuffling process to gain superior exploration capability in the search mechanism. A mechanism that causes to escape from local minima is also incorporated into the original Jaya algorithm. The efficiency of the IS-Jaya algorithm is tested on discrete optimization problems and compared to those of Jaya algorithm, self-adaptive multi-population-based Jaya (SAMP-Jaya), and some other state-of-art optimization methods. Optimization results show that the proposed optimization method can be an effective tool for solving discrete size optimization of skeletal structures.

Secure multi-gigabit optical link design for high energy physics experiment with acceleration of more secure RC4 variant in reconfigurable platform

Many cryptographic attacks are capable to break RC4. Still RC4 becomes alive in crypto community with several variants. Insufficient key schedule is the most important weakness of RC4 where the first bytes of output reveals information about the key. The loop-holes can be corrected by discarding initial portion of the output stream [1] which is known as RC4-dropN (N is a multiple of 256, such as 768 or 1024). Here we shows RC4 can be made more secured if an additional RC4-like Post-KSA Random Shuffling (PKRS) process is introduced between KSA and PRGA. The PKRS process is tuned to form as many S-boxes as required by particular design architectures involving multiple coprocessors, each one undertaking byte-by-byte processing. Following a recent idea [2, 3] the speed of execution of each processor is also enhanced by another fold if the byte-by-byte processing is replaced by a scheme of processing two consecutive bytes together. Adopting some new innovative concepts, three hardware design architectures are proposed in a suitable FPGA embedded system involving 1, 2 and 4 coprocessors functioning in parallel and a study is made on accelerating RC4 by processing bytes in byte-by-byte mode achieving throughputs from 1-byte-in-1-clock to 4-bytes-in-1-clock. The hardware designs are appropriately upgraded to accelerate RC4 further by processing 2 consecutive RC4 bytes together and it has been possible to achieve a maximum throughput of 8-bytes per clock in Xilinx Virtex-5 LX110t FPGA [4] architecture followed by secured communication High Energy Physics (HEP) application that preserves the DC-balancing of the link and permits forward error correction (FEC) along with encryption.