Reviewerly/exHarmony huggingface dataset:
A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain

(1) Background: Cancer is a leading cause of death worldwide and each year, approximately 400,000 children develop cancer. Early detection of cancer greatly increases the chances for successful treatment, while screening aims to identify individuals with findings suggestive of specific cancer or pre-cancer before they have developed symptoms. Precise detection, however, often mainly relies on human experience and this could suffer from human error and error with a visual inspection. (2) Methods: The research of statistical approaches to analyze the complex structure of data is increasing. In this work, an entropy-based fuzzy clustering technique for interval-valued data (EFC-ID) for cancer detection is suggested. (3) Results: The application on the Breast dataset shows that EFC-ID performs better than the conventional FKM in terms of AUC value (EFC-ID = 0.96, FKM = 0.88), sensitivity (EFC-ID = 0.90, FKM = 0.64), and specificity (EFC-ID = 0.93, FKM = 0.92). Furthermore, the application on the Multiple Myeloma data shows that EFC-ID performs better than the conventional FKM in terms of Chi-squared (EFC-ID = 91.64, FKM = 88.26), Accuracy rate (EFC-ID = 0.71, FKM = 0.60), and Adjusted Rand Index (EFC-ID = 0.33, FKM = 0.21). (4) Conclusions: In all cases, the proposed approach has shown good performance in identifying the natural partition and the advantages of the use of EFC-ID have been detailed illustrated.
An efficient differential analysis method based on deep learning

Differential analysis is a vital tool for evaluating the security of cryptography algorithms. There has been a growing interest in the differential distinguisher based on deep learning. Various neural network models have been created to increase the accuracy of distinguishing between ciphertext and random sequences. However, few studies have focused on differential analysis at the design stage of cryptographic algorithms. This paper presents an appropriate model for differential analysis of block ciphers. The model is similar to multilayer perceptron (MLP) models in simplicity and clarity. It also introduces a shortcut connection that enables one to learn more information about the differential analysis dataset. The model is used to predict the minimum number of active S-boxes (AS), linking differential analysis results to algorithm features. This model and two classical neural network models are compared under fair experimental conditions. The findings indicate that our model predicts the AS values with an accuracy of 97%. It can effectively predict the results of differential analysis. In addition, the differential analysis dataset is constructed for SPN structure cryptographic algorithms. It can be used for further differential analysis studies based on deep learning.
ParaLiNGAM: Parallel causal structure learning for linear non-Gaussian acyclic models

One of the key objectives in many fields in machine learning is to discover causal relationships among a set of variables from observational data. In linear non-Gaussian acyclic models (LiNGAM), it can be shown that the true underlying causal structure can be identified uniquely from merely observational data. The DirectLiNGAM algorithm is a well-known solution to learn the true causal structure in a high dimensional setting. DirectLiNGAM algorithm executes in a sequence of iterations and it performs a set of comparisons between pairs of variables in each iteration. Unfortunately, the runtime of this algorithm grows significantly as the number of variables increases. In this paper, we propose a parallel algorithm, called ParaLiNGAM, to learn casual structures based on DirectLiNGAM algorithm. We propose a threshold mechanism that can reduce the number of comparisons remarkably compared with the sequential solution. Moreover, in order to further reduce runtime, we employ a messaging mechanism between workers. We also present an implementation of ParaLiNGAM on GPU, considering hardware constraints. Experimental results on synthetic and real data show that our proposed solution outperforms DirectLiNGAM by a factor up to 4788X, and by a median of 2344X.
Single-stage Multi-scale Receptive Field Improvement Lightweight Object Detection Network Based on MobileNetV3

The computing power of edge devices is difficult to keep up with the development of modern computer technology, and the computing power is not improved enough. In practical application environments, so there is the birth of lightweight models. Lightweight models specifically refer to some models with simple model architecture and low computational load. Although the lightweight model is fast and the model is relatively simple, the detection effect is not very good. This paper proposes a parallel convolution module, performs feature fusion through parallel processing of multi-scale convolution kernels, and then integrates the spatial channel attention mechanism into the module to implement a multi-scale target detection module on a single feature layer. Model fusion proposes anchor boxes to generate heads and become a single-stage object detection model.
Identification of Conflicts in User Story Requirements Using The Clustering Algorithm

In a software development life cycle, a critical phase that must be an important concern is the phase of exploring user requirement. In Agile Software Development, user requirements are written in a user story. User stories are written in Natural Language Processing with the format “As I, I want to, So that. There can be conflicts between user story. Grouping user stories will make it easier and faster for software developers to evaluate potentially conflicting or conflicting user stories. The steps are taken starting from preprocessing for syntax checking. These attributes are well formed, atomic, minimal, and unique. The clustering algorithm chosen Single Linkage, and K means. From 31 sentences of user stories passed syntax 115. User stories pass this syntax are then clustered. The results of the evaluation of these two algorithms using the best silhouette values were obtained for single linkage 6 clusters, and k means 6 clusters.
Cloud Infrastructure Self Service Delivery System using Infrastructure as Code

To succeed in cloud adoption and thrive in multi-cloud environments, most organizations develop centralized platform teams. The primary function of these centralized platform teams popularly known as Cloud Center of Excellence (CCoE) teams is to industrialize application delivery. The platform teams which are made up of individuals who provision, run and manage shared infrastructure in cloud environments across teams, act on the requests generated by developers. This activity is time-consuming and prone to manual errors. This paper defines a system that enables developers to access platform capabilities on-demand using templates via a self-service mechanism. Application delivery in cloud environments may evolve into proven repeated patterns useful for standardizing how people function and what tools they consume and which process they follow. These standardized steps can be converted into a template made with infrastructure as Code. Using this system, the developers can launch their required resources using templates without intervention from platform teams while adhering to internal compliance of the organization. These templates built using Infrastructure as Code (IaC) by the platform teams make it easy to create new resources in the cloud and release new capabilities more quickly; ensuring that desired configurations are set by Infrastructure, Network, Security, and Operations Engineers.
Continuous-variable measurement-device-independent quantum key distribution with passive state in oceanic turbulence

Quantum communication, as an absolutely secure communication method, has been widely studied in recent years. While CVQKD is easier to realize and has efficient information transmission, has attracted more attention than DVQKD. And there is another kind of detection strategy which places the detector on a third party called continuous-variable measurement device independent quantum key distribution(CV -MDI), can lower the cost of implementation because it don't need such a high precision Gaussian modulator and thus can be commercialized easily. We apply a CV -MDI with passive-state preparation protocol to a oceanic turbulence model, and then we analyze the secret key rate and other performance of the protocol in the particular scenario.
Multiple Feature Selection Frameworks Based on Evolutionary Computing and Ensemble Learning for Software Defect Prediction

Software defects may cause severe crashes in the system, leading to the software's high maintenance costs. Early identification of these defects would lead to high-quality software, thus saving time and money. This study proposes five feature selection approaches based on evolutionary computing algorithms, each coupled with a majority voting ensemble for Software defect prediction. The objective is to improve the existing process by targeting the metric selection stage. The study was conducted on thirty open-source defect datasets. The proposed feature selection techniques were applied on a within-project defect prediction model and a heterogeneous defect prediction model. The Friedman and the Wilcoxon Signed-rank test concluded that the proposed techniques were promising and generated results comparable to some other state-of-the-art feature selection methodologies.
Load Balanced Web Server on AWS Cloud

Cloud computing is the large interest accessibility of the architecture, provides benefits and especially computing power and information capacity (distributed storage), without direct dynamic client administration. Enormous Frameworks and Information ordinarily convey their capacities across a few areas, every one of which is a server farm. Cloud computing depends on resource sharing to accomplish productivity and ordinarily uses a “pay-as-you-go” model, which can assist with reducing capital costs but yet may likewise abbreviate surprising working costs for clients. Web Server load balancing is a way for servers to manage high-volume traffic and avoid decreased load times and transparency issues. By properly and consistently appropriating organization and web traffic passed beyond more than one server, associations can additionally develop throughput and application reaction times. The bigger part of the organizations staying aware of their web server on their on-premise, which prompts expanded support, workload, and increased cost. As an alternative, cloud innovations can be used to design a web server that can perform efficiently and effectively. Subsequently, we use AWS cloud computing technologies. This exploratory article builds a web server utilizing the cloud development approach of cloud Formation Templates (CFTs). By following this process, we may get ready to put our framework together using Yet Another Markup Language (YAML) code. For attaining these important cloud goals, tools including Elastic Cloud Compute (Ec2) Instances, application load balancers for changing client-provided load, and auto-scaling groups are used in load-balanced web servers in the AWS cloud computing.
Image Dehazing Algorithm Based on Particle Swarm Optimization for Sky Region Segmentation

The well-known Dark Channel Prior (DCP) dehazing algorithm does not work well in the sky area, thus, we propose an image dehazing method for segmented sky regions based on Particle Swarm Optimization (PSO). First, we apply PSO to the Otsu segmentation method to accurately segment sky and non-sky regions in the hazy image. Second, the transmission of non-sky regions is derived by DCP. A transmission compensation method is proposed to obtain the transmission for the sky regions. The transmission is then refined by gradient-domain guided filtering. Finally, the restored haze-free image is obtained through the Atmospheric Scattering Model (ASM). Quantitative and qualitative experiments show that the proposed method has a better dehazing effect, especially for hazy images with the sky areas.
Application of Improved Genetic Algorithm in Cruise Missile Route Planning

To address the shortcomings of the traditional genetic algorithm in cruise missile route planning, which is prone to “premature maturation” and premature convergence to a local optimal solution, an improved genetic algorithm is proposed that introduces an adaptive operator and a variation ratio strategy. The algorithm processes the individual fitness values in the population by ranking ratio technique, and then adopts a selection strategy combining elite selection and roulette algorithm to add the feasible routes with the best fitness values directly to the children at each evolution, and then roulette selects the remaining feasible routes, which improves the global optimal search performance of the algorithm in the trajectory planning. Meanwhile, an adaptive crossover operator is used to dynamically select the crossover probability based on the individual fitness values of the parents. Finally, the two algorithms are applied to the established map model for route planning separately, and the simulation results show that the path solved by the improved genetic algorithm reduces three planning waypoints and 10.74 % of the range compared with the traditional genetic algorithm, and the global search performance of the route planning process applying the improved genetic algorithm is significantly better than that of the traditional genetic algorithm.
Sentiment Classification of Financial Online Reviews Based on Machine Learning Algorithm

Countries or governments can learn people's opinions or views on the economic situation by referring to financial online reviews, and the sentiment classification of reviews can also help them make financial decisions, which is conducive to mining a large amount of financial information with commercial value. Effective information is of great significance to the implementation of financial policies. This paper analyzes the sentiment tendency of financial online reviews into negative reviews, positive reviews and neutral reviews, analyzes the application of support vector machine and naive Bayes classifier in review sentiment classification, and compares the performance of these two machine learning algorithms. Classification accuracy, the experimental results show that the machine learning algorithm has better classification effect on negative and positive reviews, and the classification effect on neutral reviews is average.
P<sup>3</sup>: A task migration policy for optimal resource utilization and energy consumption

Theevolution in modern technologies like artificial intelligence, machine learning, cloud computing, edge computing, data science, etc, focuses on user perspectives like accuracy, response-time, and timeliness but at the same time consumes heavy energy due to large and fast data processing. From the system perspective, resource utilization and energy consumption are also significant design considerations. This work proposes a task migration policy for optimal core utilization and energy savings. The time taken by data analytical tasks to process the data varies, due to variations in the amount of data it analyzes in unit time. This creates variation in the core utilization due to which there exist small inactive intervals in the schedule, consuming energy. If the inactive state is known to continue for a longer duration, the core can be put into a shutdown state which effectively reduces overall energy consumption. Dynamic Procrastination (DP) is a commonly used technique to increase the inactive duration by postponing the tasks whenever possible. To further increase the inactive duration to qualify for shutting down the core, in a homogeneous multi-core (HMC) system, the jobs can be migrated to other cores. This effectively improves core utilization and reduces overall system energy without negatively affecting performance. Combining the DP and migration techniques introduces challenges like meeting deadlines, deciding upon push/pull migration, finding the number of tasks and suitable core for migration, and computation of energy consumption parameters. This paper proposes P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (Push-Procrastinate-Pull) migration policy for the HMC system. The experimental evaluation with synthetically generated benchmark program suites shows that on an average P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> reduces the overall energy by 1.2% and reduces the shutdown duration over the idle period by 2.22% over DP without migration.
An Efficient Particle Swarm Optimization with Fusion of Figurate Opposition-based learning and Grey Wolf Optimizer

Efficient metaheuristics are required to solve real-world optimization problems and find an optimal solution while maintaining the Exploration-Exploitation balance. This paper presents a novel hybrid algorithm called Opp-PSOGWO, which combines the exploitation capabilities of particle swarm optimization (PSO) and the exploration capabilities of grey wolf optimization (GWO). Figurate opposition-based Learning (OBL) is added to the hybrid to improve performance. The dimensions of particles that fall in the Fibonacci sequence positions are generated in opposite directions to achieve an optimal initial population with increased population diversity. In this way, the hybrid algorithm can avoid the local optima trap, and Figurate opposition avoids unnecessary exploration and gives the optimal solution quickly. To evaluate the performance and the solution quality, Opp-PSOGWO is tested on 9 classical benchmark functions (unimodal and multimodal). The experimental results demonstrate that, in terms of performance metrics, the proposed algorithm outperforms the state-of-art metaheuristic algorithm.
Research on Online Simulation Training Teaching of Onshore Marine Engine Room

In response to the requirements of the Ministry of Education to improve the undergraduate teaching quality, implement education digitization strategy, and deepen the education reform, this paper analyzes the teaching mode of the onshore engine room training course based on our university's offline physical engine room laboratory and teaching reform project of onshore engine room. With the help of a new generation of information technology, it develops and applies an online simulation training teaching platform to assist in offline training teaching. The results show that the application of the online simulation training teaching platform indeed helps improve the offline training teaching quality. The teaching method of using online platforms to assist offline teaching provides a reference for peer colleges to build education public service platforms, improve the practical training quality and promote education digitization and teaching modernization.
Secured Authentication by Single Sign On (SSO): A Big Picture

Single Sign On (SSO) is a tool used for single login credentials to authorise the user to access all software or applications independent of each other, causing users to log in repeatedly. With the help of an administrator, it minimizes the security risk of managing the user. SSO allows users to access the various applications after one login. It does not mean that the Single Sign On system merges many different user account information for all the different services as well as the many applications and systems. SSO safely stores multiple user login information into a single account that, with the help of these credentials, users need to login. With the help of login credentials, the SSO system will generate authentication information that is accepted by the different applications and systems. In the present research article, many different types of technologies that operationalize SSO are collated. Various implementation types of SSO are explained. It also discusses the protocols used in SSO, SSO architecture, advantages of SSO, implementation of SSO in detail and challenges faced by SSO. An overall broader picture of SSO is discussed in this regard.
Outlier and Trend Detection Using Approximate Median and Median Absolute Deviation

In this modern era of technologies of scale, vast amounts of data are generated both by users and machines every day. This data comes as streams that may contain outliers. Detecting those outliers can be helpful in many ways, such as machine failures due to overload. Similarly, trends in social media posts are also outliers, and detecting them at different levels has great benefits. The current paper proposes an algorithm to approximate median and median absolute deviation from a stream of numerical values. The algorithm takes a fixed number of memory spaces and linear to the size of the memory. The median and median absolute deviation are then used to detect outliers and multi-level trends without being prone to noise in the data. Experimental results with CPU usage benchmark data and Twitter post data show the effectiveness of the proposed algorithms.
Land Registry and Title Management using Blockchain and Smart Contracts

In India, land registry and title management is a time-consuming process involving many middlemen, which increases the risk of fraud. Different departments in the land administration system hold their copies of documents, resulting in document falsification and record mismanagement. In this research, we have proposed how blockchain and smart contracts may be used to create a decentralized application for land registry and title management systems in India that is efficient, safe, and scalable. Our proposed system aims on providing a practical approach for digitizing land records so that they can be stored and transacted upon in a blockchain, thereby inheriting the biggest advantage of blockchain which is an immutable, permanent, transparent, and verifiable ledger of modifications in land records, meanwhile mimicking the distributed nature of different departments involved in land administration through a permissioned blockchain network realized through Hyperledger fabric framework.
V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning

Long short-term memory (LSTM) has been widely adopted in tasks with sequence data, such as speech recognition and language modeling. LSTM brought significant accuracy improvement by introducing additional parameters to recurrent neural network (RNN). However, increasing number of parameters and computations also led to inefficiency in computing LSTM on edge devices with limited on-chip memory size and DRAM bandwidth. In order to reduce the latency and energy of LSTM computations, there has been a pressing need for model compression schemes and suitable hardware accelerators. In this article, we first propose the Fixed Nonzero-ratio Viterbi-based Pruning, which can reduce the memory footprint of LSTM models by 96% with negligible accuracy loss. By applying additional constraints on the distribution of surviving weights in Viterbi-based Pruning, the proposed pruning scheme mitigates the load-imbalance problem and thereby increases the processing engine utilization rate. Then, we propose the V-LSTM, an efficient sparse LSTM accelerator based on the proposed pruning scheme. High compression ratio of the proposed pruning scheme allows the proposed accelerator to achieve 24.9% lower per-sample latency than that of state-of-the-art accelerators. The proposed accelerator is implemented on Xilinx VC-709 FPGA evaluation board running at 200 MHz for evaluation.