Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Code Testing Strategies in DevOps: A Comprehensive Study of Techniques and Tools for Ensuring Code Quality and Reliability

This research study focuses on exploring effective code testing strategies in DevOps for ensuring code quality and reliability. DevOps is an approach that emphasizes the collaboration and integration between software development and IT operations teams to automate the software delivery process and ensure fast and continuous delivery of high-quality software. Code testing is an essential part of the DevOps process that ensures that the software code is working as expected and meets the user’s requirements. This research aims to provide a comprehensive overview of the different techniques and tools used for code testing in DevOps. The study explores various testing techniques, such as unit testing, integration testing, acceptance testing, and automated testing, along with tools such as Pytest, Robot Framework, and Selenium. Additionally, the research examines how code coverage analysis, mutation testing, and load testing can help improve code quality and reliability. The study presents a systematic analysis of the strengths and weaknesses of each testing technique and tool, along with their applications in different stages of the DevOps process. Furthermore, the research provides insights into the challenges and best practices for implementing code testing in a DevOps environment. The results of this study contribute to the existing knowledge of code testing in DevOps and provide guidance for developers, testers, and IT professionals to improve the quality and reliability of their software code.
Battlefield Reconnaissance and Monitoring WSN Transport Protocol based on Network Coding

In the battlefield reconnaissance and monitoring environment, the application of Wireless Sensor Network (WSN) requires high timeliness and reliability of data transmission. To meet the battlefield demand, a transmission protocol is designed in this paper. This protocol combines network coding technology to fully play the function of node collaboration in the transmission process and use the channel broadcast characteristics. The data is transmitted in real-time and reliably through the aggregation node to the command control center, providing a real-time update database for the battlefield commander. Through theoretical and simulation analysis, this protocol can meet the requirements of the battlefield reconnaissance and monitoring environmental log, and the system can still maintain better network performance in the condition of low probability of transmission of battlefield environment.
Iris based ATM Transaction using Machine Learning

The ability of iris recognition systems to establish reliable biometric security measures in a range of high-risk industries, such as aviation, border enforcement, and defence, has been demonstrated. The banking and financial industries must use this system to that dependability and benefits in cost-cutting and process streamlining. The technology was new initially, but because of the pressures on the banking industry engendered by declining earnings, it became crucial. By offering a paperless and secure banking environment, the deployment of biometric ATMs based on iris recognition technology has made significant progress in improving customer experience. A biometric system will automatically identify the individual based on any form of identifying attribute or attribute that the individual has. A biometric will automatically identify each individual user. In the modern world, there are an expanding range of human operations and transactions whereby fast and precise personal identification.
ESR: Optimizing Gene Feature Selection for scRNA-seq Data

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis.
Multi-granularity Autonomous Intelligent Method for Operation Optimization of Integrated Coal Mine Energy Systems

An intelligent optimization algorithm is only valid for solving some problems, which difficultly solves all operation optimization problems of integrated coal mine energy systems with different characteristics. An optimization paradigm usually consists of multiple operators/strategies, each of which is suitable for solving different problems. It is difficult for an operator/strategy to ensure that the population can evolve forward in the evolution process since the state of the population is changing. To this end, a multi-granularity autonomous intelligent optimization method is proposed to optimize the operation of integrated coal mine energy systems with various scenarios. This method automatically determines appropriate optimization paradigms according to problem characteristics and adaptively adjusts optimization operators/strategies based on population states in the evolution process. For the adaptive adjustment of operators/strategies, this paper proposes an adaptive adjustment strategy based on Q-Learning. Taking a coal mine in Shanxi Province, China as the research object, a series of experiments are conducted, and the experimental results show the effectiveness of the proposed algorithm.
Method for multispectral images denoising based on tensor-singular value decomposition

Abstract. We propose a sparsity measure for third-order tensor, called all-dimension tensor nuclear norm (AD-TNN). In specific, we exploit the tensor-singular value decomposition and tensor nuclear norm (TNN), considering TNN along every dimension and then construct our AD-TNN measurement. Based on AD-TNN, we construct a model for multispectral images denoising. We also employ the alternating direction method of multipliers (ADMM) to solve our model. Experimental results show that our method outperforms all the compared methods under comprehensive quantitative performance measures.
Growing Neural Gas based Traversability Clustering for an Autonomous Robot

One of the most important capabilities of an autonomous robot is to recognize a 3D space of the surrounding environment in real-time from a 3D point cloud measured by a 3D distance sensor. The area in which the robot can travel is limited by a robot embodiment such as a mechanism of the robot. Therefore, the traversability estimation method helps the robot to travel safely and reduces the calculation cost of the path planning. This paper proposes Growing Neural Gas (GNG) based traversability estimation method by utilizing a topological structure learned from the 3D point cloud data. However, the conventional GNG cannot preserve the geometric information of the 3D point cloud if the input vector is composed of the multiple properties. Therefore, this paper apply GNG with Different Topologies (GNG-DT) that learn the multiple topological structures according to the number of properties. This paper proposes a GNG-DT based traversability estimation method by redefining the property of the GNG-DT. We conduct several experiments in both simulation and real environment to verify the effectiveness of our proposed method.
Admission Control with Latency Considerations for 5G Mobile Edge Computing

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users’ delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.
Variable Window and Deadline-Aware Sensor Attack Detector for Automotive CPS

Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identifying residual errors that exceed a predetermined threshold. However, this approach means that any sensor attack alert can only be triggered after the time-window has elapsed. The length of the time-window affects the detection delay and the likelihood of false alarms, with a shorter time-window leading to quicker detection but a higher false positive rate, and a longer time-window resulting in slower detection but a lower false positive rate.While researchers aim to choose a fixed time-window that balances a low false positive rate and short detection delay, this goal is difficult to attain due to a trade-off between the two. An alternative solution proposed in this paper is to have a variable time-window that can adapt based on the current state of the CPS. For instance, if the CPS is heading towards an unsafe state, it is more crucial to reduce the detection delay (by decreasing the time-window) rather than reducing the false alarm rate, and vice versa. The paper presents a sensor attack detection framework that dynamically adjusts the time-window, enabling attack alerts to be triggered before the system enters dangerous regions, ensuring timely detection. This framework consists of three components: attack detector, state predictor, and window adaptor. We have evaluated our work using real-world data, and the results demonstrate that our solution improves the usability and timeliness of time-window-based attack detectors.
GLM-Regularized Low-Rank Factorization For Extracting Functional Response From Swept-3D Functional Ultrasound

Functional ultrasound (fUS) is an emerging neuroimaging modality that indirectly measures neural activity by detecting fluctuations in local blood dynamics. fUS acquisitions typically rely on the use of a 1D array transducer, which records hemodynamic activity in a single plane. A new technique named swept-3D fUS imaging obtains a full 3D volume of the brain by continuously moving a 1D array back-and-forth over the volume of interest. The standard procedure in fUS imaging involves filtering and averaging a number of ultrasound frames obtained at a single location to compute power-Doppler images, yet, in case of swept-3D fUS, the location of the recorded slice shifts at each time instant due to probe motion. In this work, we aim at discovering task-relevant components from 3D fUS data while taking into account the spatiotemporal differences in adjacent slices. We propose an alternating optimization scheme with general liner model-based regularization, and validate our method on swept-3D fUS data by identifying active regions and time traces within the mouse brain during a visual experiment.
Design and Development of Compliant Anatomical Palmar Mechanism (CAPM) to Adaptively Reconfigure Precision/Power Grasps

The design of a robotic hand incorporating a critical multi-DOF palm depends on studies of the anatomical structure of the human palm model. Shaping of this large grasping region is simplified to depend on arches formed by relative movement of the phalanges and metacarpal. Compliant Anatomic Palmar Mechanism (CAPM) replaces these arches to form an interconnected compliant structure that can be used in grasping to conform to contacting objects to augment stability. FEM is used to simulate the range of intrinsic movement of the palmar surface. Plotting these results determines the kinematic relationship by relating thumb rotation and metacarpal translation to the deformation of the palmar region.
Blockchain Technology in Improving Transparency and Efficiency in Government Operations

Blockchain technology has garnered significant attention and investment in various industries, including the government and public sector. This research paper explores how blockchain technology can enhance government operations by making them more transparent and efficient. A literature review and case studies of blockchain implementation in government are conducted to identify the key benefits and challenges of this technology. The paper also provides examples of blockchain applications in government, such as voting systems. This paper explores the benefits of using blockchain technology in creating a secure and transparent voting system. The proposed blockchain-based voting system uses smart contracts to automate the voting process and eliminate the need for intermediaries, ensuring the accuracy and integrity of the vote count. The use of blockchain technology also enables the creation of an immutable and transparent ledger of all voting transactions, ensuring that the election results are auditable and verifiable. The paper also discusses the potential challenges associated with implementing a blockchain-based voting system and suggests possible solutions to address these challenges.
An End-to-End Optimization Framework for Autonomous Driving Software

Given the increasing complexity of autonomous driving, it becomes more difficult to test driving functions and to optimize algorithm parameters. One major challenge is that many parameters and software components influence each other, so even small changes in parameters can lead to a high sensitivity in vehicle performance. Many approaches involve real-world and simulation-based testing of predefined scenarios, which is expensive and time-consuming, and manually determining of reliable software parameters is not possible in many applications because parameter variation is non-intuitive. Misconfigurations of the software parameters are detected too late. For that reason, reliable and automated software testing and optimization is an essential component for autonomous driving in the future. This paper presents an end-to-end optimization framework for automatically tuning and optimizing individual parameters for a full-stack autonomous driving software. We will demonstrate our method for optimizing the parameters in a non-deterministic simulation environment by using gradient-free optimization methods. The simulative method we are presenting was applied and deployed at the Indy Autonomous Challenge. This method offers the opinion of building a remote tool chain that efficiently supports testing and optimization under dynamic requirements during the autonomous driving software development process.
Polyphase Implementation for Gaussian 2D FIR Filters with Circular Symmetry

We propose an analytical design procedure for a Gaussian-shaped circular band-pass 2D FIR filter and also a novel, efficient implementation of the designed filter based on a polyphase decomposition approach. This 2D filter is designed in the frequency domain using a specific frequency mapping applied to a band-pass 1D prototype having a Gaussian shape and imposed specifications (peak frequency, bandwidth). Since we start from a 1D prototype with a factored transfer function, the frequency response of the designed 2D filter also results directly in a factored form. This is a major advantage in actual implementation, since in this case the filter can be synthesized in a cascade structure, and the overall filtering can be achieved sequentially. The proposed implementation is described in a simpler case, namely for a smaller size of the filter kernel and also of the input image. Using the polyphase and block filtering approach, a convenient implementation is obtained for the 2D FIR filter at system level, with a relatively low computational complexity, i.e. a minimum number of arithmetic operations.
A 38° Wide Beam-Steerable Compact and Highly Efficient V-band Leaky Wave Antenna with Surface Integrated Waveguide for Vehicle-to-Vehicle Communication

This paper presents a highly efficient single-layer substrate-integrated waveguide (SIW) based leaky-wave antenna (LWA) for the millimeter-wave unmanned aerial vehicle (UAV) communication system. The leaky wave-based radiating part of the unit cell includes a combination of two Y-shaped slots with 46° stretched V etched on the top SIW, resulting in a W-shaped structure. The proposed array achieves a high gain of 13.47 dBi for the frequency range of 56.3 GHz to 63.4 GHz covering the unlicensed band, with a fine matching level below -21 dB. Using the leaky wave antenna's frequency scanning capability, the proposed antenna exhibits a scanning range of 38°. The designed antenna shows a promising solution for the UAV-to-UAV applications due to its low profile and compactness and is well-suited for the single-layer low-cost printed circuit board fabrication process using Rogers RT 5880 as substrate. The radiation pattern for the achieved bandwidth shows an average half-power angular beamwidth of 12.1°, resulting in a radiation efficiency of more than 62% for the elements arranged uniformly at a distance of $\mathbf{0.456}\lambda$. Following an overall low-profile compact size of $6.48\times 4\ \lambda$ corresponding to $3.24\times 0.2\ \text{cm}$ and improved performance, the antenna achieves an elliptical polarization at 60 GHz for an axial ratio equal to 3.5 dBi.
Sparse Gate for Differentiable Architecture Search

Differentiable architecture search is now one of mainstream methods to design the structure of neural networks. It makes the neural architecture search efficient through parameter sharing and differentiable search. However, there are still many significant challenges for designing the optimal architecture, including the phenomenon of skip connection aggregation, excessive memory usage, and large discretization errors. To address these issues, we propose a novel approach called Gate-DARTS where a sparse gating network is introduced to route each input sample to the best $k$ operators. This can reduce the memory requirements of the algorithm and break the residual structure. For the issue of discretization error, we then propose a auxiliary loss to enlarge the difference between different operators. We conduct comprehensive experiments on DARTS-like search space, and Gate-DARTS achieves 97.45% test accuracy on CIFAR10 with 0.23 GPU-days, 83.82% on CIFAR100 with 0.28 GPU-days. Our code has been made available at https://github.com/HandingWangXDGroup/Gate-DARTS.
A Character-level Short Text Classification Model Based On Spiking Neural Networks

Spiking Neural Networks (SNNs), also referred to as the third generation of artificial neural networks, are highly prized for their biological realism, robustness, and low power requirements. SNNs are crucial in fields such as object detection, image recognition, etc. The classification of short text plays an significant role in the development of chatbots and intent detection. It is also an important task that is widely used in many downstream tasks. However, studies applying SNNs to short text classification are limited. This paper provides a new model that uses SNNs to classify short texts. SNNs are difficult to train directly when using deep models and cannot employ large-scale language models to learn good embeddings. To resolve the challenge, we apply the character-level encoding method and convert analog neural networks into SNNs. To begin with, we represent character-level text using a temporal-and-rate joint horizontal encoding method. Then we develop a tailored deep Convolutional Neural Network (CNN) model for classifying texts. At the inference stage, we convert the tailored CNN model into an SNN model. To test the effectiveness of the proposed method, we conduct text encoding experiments on the NAMES dataset and short text classification experiments on both the 20-newsgroups dataset and the emoji-mult dataset. Experiments demonstrate that the proposed method can obtain classification accuracies that are better than or comparable to other methods.
Underwater Image Enhancement with Phase Transfer and Attention

Underwater pictures typically suffer from substantial deterioration due to the refraction and absorption of light by water, including color cast, hazy blur, and limited visibility. Such degradation in visibility eventually reduces the effectiveness of marine applications installed on autonomous underwater vehicles. Hence, an efficient pre-processing step is required for the significant performance of these applications. As a solution, underwater image enhancement (UIE) mainly focuses on enhancing the visibility of degraded images along with restoring crucial details. Existing methods generally utilize (a) complex cascaded architectures, (b) different degradation-prone color spaces, and (c) direct skip connections that pass irrelevant content. In light of this, we propose a lightweight transformer network with 1.7M parameters (1/6th of the existing state-of-the-art method) consisting of the proposed gray-scale attention and phase transformer block for UIE. A gray-scale attention block is proposed for the effective extraction of non-contaminated features. Further, a phase transfer block is proposed for effectively restoring the structural information in the outputs by propagating most relevant and undegraded features from the inputs. A comprehensive evaluation of the proposed method on synthetic (EUVP, UIEB) and real-world (UIEB, UCCS) image datasets as well as extensive ablation studies confirm its effectiveness over existing state-of-the-art approaches. The source code is provided at: https://github.com/Mdraqibkhan/UIEPTA.