Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyber Attacks Detection Based on Generative Adversarial Networks

The development of the Internet of things (IoT) and other modern technologies brings not only great convenience and intelligence to society but also the severe problem of cyber security. In recent years, a large number of network attacks, especially botnet and distributed denial of service (DDoS) attacks which are difficult to detect, have seriously affected the normal operation of social work. Solutions and methods based on artificial intelligence techniques are therefore proposed to help detect cyberattacks. In this paper, the application of GAN in the field of network security is discussed. GAN has been one of the most significant deep learning models in recent years. It consists of a generative model and a discriminative model which can be combined to produce a dynamic game system. GAN has been applied in various fields such as image generation, speech processing, data enhancement, and cyberattack detection. This paper demonstrates the basic working principle and infrastructure of GAN, and also focuses on how the GAN model assists with cyber intrusion detection.
An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance

Multiple Unmanned Aerial Vehicles (UAVs) have a greater potential to be widely used in civil and military applications. Swarm of UAVs can be deployed in a multitude of 24/7 security and surveillance. The network management and pattern formation are crucial for multi-UAV formation control mechanisms while cautiously navigating the surveillance areas. A Deep Reinforcement Learning (DRL) based Formation Flight Control for Navigation (FFCN) is used to efficiently build the UAV swarm, which decreases networking load by minimizing communication and processing involved in pattern formation. Moreover, through the leader-follower navigation, the network management of the swarm is substantially simplified. The leader-follower approach in FFCN is efficient for multi-UAV as the navigation system needs to find only the leader's trajectory. However, the failure of the leader due to actuator faults decreases the efficiency of the system. The proposed FFCN addresses the above by including a fault-tolerance mechanism, thus improving the system's reliability. Simulation results show that the FFCN model achieves faster convergence in less time with a lower collision rate. The model's usage reduced the collision rate to 3.4% in successful formation without colliding with other UAVs.
Leg-Shared Fault Tolerant Predictive Control of PMSM Drive Systems Fed by Three-Level Back-to-Back Converters

Reliability is one of the most important characteristics of motor drive system, especially after power semiconductor device open-circuit fault occurs. Effective fault tolerant control methods are considered crucial to increase their reliability and availability levels. In this manuscript, a leg-shared fault tolerant control strategy within the model predictive control framework is proposed. In the fault tolerant topology, three TRAICs (TRIod AC switches) are adopted to connect three legs on the machine-side with one shared-leg on the grid-side. This topology makes full use of the feature that both sides of back-to-back converter are active legs, and has little adverse effect on DC-link capacitor voltage. Meanwhile, in order to not increase the complexity of fault-tolerant control algorithm, more flexible model predictive control (MPC) method is adopted and the calculation efficiency is improved. Simulation results validate the feasibility and effectiveness of the proposed leg-shared fault tolerant control methods.
SISO Decoding of U-UV Codes

U-UV structural coding with algebraic component codes can provide competent error-correction performance in the short-to-medium length regime. Constituted by BCH component codes and its ordered statistics decoding (OSD), the successive cancellation list (SCL) decoding of U-UV codes can outperform that of polar codes. However, the current SCL decoding is a soft-in hard-out (SIHO) process. Exploiting its list decoding feature, this paper proposes a soft-in soft-out (SISO) decoding for U-UV codes, providing the key technique for the codes to be further engaged in an iterative system. The proposal is designed based on the recursive structure of U-UV codes and the list decoding feature for both the component and the structural codes. Both the decoding complexity and its soft information transfer characteristics are also shown.
A Hybrid MLC and BICM Coded-Modulation Framework for 6G

To meet requirements of higher spectral efficiency and higher throughput in future 6G communications, we propose a hybrid multilevel coding (MLC) and bit-interleaved coded modulation (BICM) scheme based on the Ungerboeck set partitioning. Then an optimized signal mapper is designed to improve the error-correcting performance. Simulation results show that, when 256-QAM is used, our scheme gains up to 2.5 dB at a BER of 10−5 for the spectral efficiency of 7 bits/s/Hz over AWGN channels compared with the conventional BICM scheme. And the gap may increase further with the use of larger constellations. With the aid of an outer code, our scheme performs 0.5 dB better than the BICM scheme over independent Rayleigh fading channels.
Development of a Game to Foster Programming Thinking for Learning through Reading Program

In this study, we propose a digital game that fosters programming-thinking skills. The ability to learn concepts such as conditional branching, iteration, problem-solving, and event analysis through programming is referred to as programming-thinking skills. These skills can be developed through existing programming learning games. However, there are some issues. Many programming-learning games are like puzzles. They are also games that allow one to program with codes such as “move forward. ” The skills that can be developed in these games are limited. To solve this problem, we proposed a game that can effectively develop programming-thinking skills through reading and programming of programs. In this study, we present the design of our game and the results of our preliminary research, as well as discuss its usefulness in fostering programming-thinking skills.
Digital Transformation of the Construction Industry: A Review

The construction industry is hailed as the driver for the economic development of every country to a great extent due to its dynamic nature, yet it needs digital transformation to align with the fast-paced digital world economy. Hence, to evaluate the potential for the digital transformation of various segments of the construction industry, a systematic review was conducted in this study. English research articles were extracted from the Scopus database by using keywords of “Digital Transformation” AND “Construction Industry” correlated to the engineering field. After a detailed screening process as per the PRISMA statement, only 26 articles were selected for further assessment from a total of 67 extracted articles. The extracted articles were focusing on digital technologies e.g., Building Information Modeling (BIM), Smart Devices, Internet of things (IoT), Drones, Project studio (PS), and digital twins and their implication in the digital transformation of the construction industry. It was evident that digital transformation enhanced the productivity and efficiency of construction projects with better control over cost, time, and risk throughout the project life cycle. Therefore, it is recommended to establish top management interest for digital partnering with trusted organizations enabling the realization of digital technologies in the construction industry.
Aircraft Numerical “Twin”: A Time Series Regression Competition

This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft “numerical twins”, which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.
UAV-assisted data offloading for smart container in offshore maritime communications

Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention. However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station (BS). In this paper, we investigate the unmanned aerial vehicle (UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment, offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio. Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.
Optimal Configuration of Converter and Energy Storage Capacity in DC Distribution System Based on Overload Risk

DC distribution system can more effectively undertake DC load, photovoltaic components and energy storage. Because of the access of charging piles and the penetration of renewable energy, the size of load will be more and more uncertain. While the application of energy storage can smooth load fluctuation. And how to optimize the allocation of power source, energy storage capacity and power is a challenge. In this paper, the time series probability model is applied to represent the uncertainty of load. Based on the probability model, two types risk of overload are analyzed, one is caused by the power shortage, another is caused by energy shortage. Taking the maximization of DC system power supply income as the goal, considering the investment and operation cost of converter and energy storage, the DC system income objective function is established. Then the gradient descent method is employed to solve the function. Finally, several examples of the optimal allocation are given.
Overview of Derating Factor Calculation Methods for Renewable Energy Capacity Mechanisms

Ensuring sufficient power generation adequacy is a requirement for the electricity system security and reliability. Many foreign power markets have introduced different types of capacity mechanisms such as centralized capacity markets and decentralized capacity obligations. The derating factor is one of the major design elements of the capacity mechanism, representing the percentage of the installed capacity of the unit to characterize its effective capacity. Conventional power systems have a large number of thermal power plants and small system fluctuations, so the derating factor can be calculated easily based on historical data. With high penetration of renewable energy, renewable power generation contributes can significantly affect system adequacy and thus, should participate in the capacity mechanism. However, due to its stochastic characteristics, the calculation method of its derating factor needs to be studied again. This paper summarizes the relevant practical experience and academic research from the US and Europe in terms of renewable energy derating calculation, consisting of two major categories, i.e. historical data method and probability analysis method for comparative analysis. In addition, it analyzes the influence of the synergy effect caused by renewable energy on the derating factor, including the synergy effect between different power generation technologies, renewable energy and load. Finally, based on the status quo of the power market and carbon emission planning in China, feasible recommendations are put forward for the calculation of derating factors in China's adaptation to high-proportion renewable energy systems.
DWH-DIM: A Blockchain Based Decentralized Integrity Verification Model for Data Warehouses

Data manipulation is often considered a serious problem in industrial applications as data tampering can lead to inaccurate financial reporting or even a corporate security crisis. A correct representation of company data is essential for the companies' core business processes and is requested by governments and investors. However, the current solution, third-party auditing, is expensive and cannot be fully trusted. In this paper, we present the Data Warehouse Decentralized Integrity Model (DWH-DIM) to validate the integrity of the data warehouse and replace the current process. To address the challenge that the existing distributed integrity verification models cannot handle GDPR and are limited by scalability, our model uses a distributed file system to store attributes that can be used for the integrity verification task. The blockchain further confirms the authenticity of the files. Based on the proposed model, we present a detailed implementation of the DWH-DIM tool. The implementation is tested with a use case and several benchmarks. Experimental results demonstrate that our proposed model is feasible and meets the requirement for certificate warehouse data.
Relationship Between Active and Passive Microwave Signals Over Vegetated Surfaces

The NASA Soil Moisture Active Passive (SMAP) satellite mission aims to produce enhanced resolution surface soil moisture products by combining coincident but multiresolution L-band active and passive microwave measurements. Since the SMAP radar ceased operations early in the mission, Copernicus Sentinel-1 C-band radar observations are used in the combined product. The synergy is built on two basic foundations: first, active and passive signals covary in a known and systematic fashion, and second, measurements are available at multiple resolutions. In this study, we perform numerical simulations and assess global satellite observations to test the first foundation (covariation). Specific focus lies on the role of the vegetation canopy in modulating the active-passive relationship. We use a discrete radiative transfer model to simulate the slope β and coefficient of determination R² of the relationship between active and passive signals, considering three vegetation types for which the model has been extensively assessed in previous experimental studies. We find that a linear relationship between backscatter and emissivity can be established over a range of vegetation conditions. The coupling between active and passive signals decreases with increasing vegetation water content, such that moderate or higher correlations (nonzero slopes) are retained up to 4 kg/m² (6.3 kg/m²) for L-band/L-band and 1.5 kg/m² (2 kg/m²) for the C-band/L-band configuration. We decompose the effects of different soil-vegetation scattering mechanisms, such as double-bounce, and different measurement error levels on the active-passive relationship. Comparisons with satellite data confirm that our simulations capture magnitudes and major trends found across global vegetated land masses.
A Location-based and Hierarchical Framework for Fast Consensus in Blockchain Networks

Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating the existing consensus protocols in many IoT systems limits scalability and leads to high computational cost and network latency. We propose a hierar-chical and location-aware consensus protocol for IoI-blockchain applications inspired by the original Raft protocol to address these limitations. The proposed consensus protocol generates the consensus candidate groups based on nodes' individual reputation and distance information to elect the leader in each sub-layer blockchain and uses our threshold signature scheme to reach global consensus. Experimental results show that the proposed consensus protocol is scalable for large IoT applications and significantly reduces the communication cost, network latency, and agreement time by more than 50% compared with the Raft protocol for consensus processing.
Comparative Analytical Survey on SBST Challenges from the Perspective of the Test Techniques

—Despite several decades of working on software quality assurance methods, they still require further investigation. It is paramount to detect a process detecting possible software errors with a minimum budget and effort. Search-based software testing (SBST) is an approach to automated software testing that aims to find a suitable alternative to manual testing. The SBST is to transform the test problem into an optimization problem and obtain better solutions by searching the problem space. Search-based testing has some disadvantages and advantages. The novelty of this paper is that, besides representing the significance and efficiency of SBST in software testing, the search-based test challenges were detected and described from the perspective of the test techniques. Our work is to extract challenges from reliable sources and research and their classification based on test techniques. For this purpose, we considered this framework: 1) A systematic introduction to the most critical metaheuristic optimization algorithms. 2) classifying the test techniques and explaining their advantages and disadvantages. 3) proposing a suitable classification for the challenges of the search-based test area based on the technique used. Our motivation to do this research was to provide complete knowledge about search-based software testing challenges so that new researchers could choose their research fields with prior knowledge and provide a way to improve existing methods. Finally, the results of this paper can be used to compare the existing test techniques used in SBST , select the best one, and represent the challenges of each technique.
Enhancing Lexical Representation of Test Coverage for Failure Clustering

Failure clustering aims to group multiple test failures based on shared root causes, helping developers to comprehend and debug each root cause (i.e., the underlying fault) in isolation. Clustering of failing test executions requires distances between those executions, for which distance measures between coverage vectors are widely used. Lexical representation of coverage has been suggested as an alternative, representing each structural element covered by a failing execution with the lexical tokens in the element. This paper investigates whether the granularity of the lexical representation affects the effectiveness of the failure clustering. We evaluate varying levels of tokenisation granularity by using them for clustering coexisting real-world test failures in Defects4J benchmark. Our results show that the traditionally adopted subtokenisation can actually deconstruct larger meaningful semantic token units, resulting in suboptimal clustering.
IoT-enabled model for Digital Twin Of Mental Stress (DTMS)

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time.
Towards Architecture-aware Hierarchical Communication Trees on Modern HPC Systems

Modern HPC systems built with emerging multi-/many -core architectures have high core-counts and deep memory hierarchies. It is challenging to design communication libraries on these systems with the conventional wisdom of using OS processes as the basic building block to build communication algorithms. Instead, the next generation of communication libraries should treat hardware as the “first-class citizen” and utilize the underlying topology as the basic building block. Driven by this overarching principle, we present a framework for Optimized Shared Memory Processing (OSMP) and communication for these platforms. An abstract representation of the underlying hardware topology is maintained by OSMP in the form of a topology tree, which is later exploited by runtime libraries to execute communication operations in a topology-aware manner. This can be done by simply traversing the topology tree with an existing communication primitive as the base-case. OSMP does not mandate any changes to the original communication algorithm. We focus on collective operations such as barrier, reduction, and broadcast as candidate communication patterns. We demonstrate the efficacy of OSMP by decoupling the implementation of collective algorithms and system topology and evaluate it on four state-of-the-art multi-tmany-core architectures: Intel Cascade Lake, AMD Rome, ARM A64fx and IBM POWER9. Results show that even the basic algorithms can be made topology-aware by exploiting OSMP. This provides significant benefits over state-of-the-art algorithm implementations for intra-node communication. Using various micro-benchmarks and applications, we demonstrate that our proposed designs can achieve up to 7.8× improvements at the micro-benchmark level, and 15% for applications over state-of-the-art intra-node collective communication designs employed by production MPI libraries.
Adaptive User Interface for Smart Programming Exercise

An adaptive user interface for smart programming exercise and its platform is presented. The proposed adaptive user interface is oriented to repetitive exercises with many pro-gramming tasks through different learning phases. The learning phases include searching, reading, coding, testing, debugging, and refactoring, and the learner can receive smart assistance in each phase. The content of the smart assistance can be adjusted by modes predefined for each phase. The configuration and contents of the user interface are controlled by the system according to the transition of the learner's state and activities. The smart assistance is realized by different types of materials and automatic assessment systems for program codes as well as by machine learning models for the recommendation, code completion, bug highlighting, program repairing, and program transformation. In this paper, the state transition graph to organize the adaptive user interface and its smart assistant modes for each learning phase are presented. The prototype of the user interface as well as the architecture of its platform including the automatic assessment system, different machine learning models, and Iogging ecosystem are also demonstrated.