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Design of a General Hardware in the Loop Underwater Communication Emulation System

IEEE ACCESS 2021年 9卷 37685-37696页

作者： Li, Yun Zhou, Zhaojia Su, Yishan Guangxi Univ Finance & Econ Sch Informat & Stat Nanning 530003 Peoples R China Guilin Univ Aerosp Technol Coll Elect Informat & Automat Guilin 541004 Peoples R China Tianjin Univ Dept Elect & Informat Engn Tianjin 300072 Peoples R China

Underwater acoustic communication plays an important role in underwater wireless sensor network. Different from terrestrial radio channel, modeling of underwater acoustic channel is very challenging due to its unique and dynamic characteristics. In order to verify the proposed acoustic communication algorithms, a general emulation platform which can reduce the verification cost has a wide application prospect. In this paper, we design a general hardware in the loop underwater communication emulation system based on the LabVIEW and MATLAB software and real acoustic transducer. In the emulation system, LabVIEW calls MATLAB script to achieve algorithm implementation, and then uses DAQmx to send and receive data through the acquisition card. The proposed system supports a variety of modulation and demodulation methods, such as multiple frequency-shift-keying(MFSK), multi-carrier frequency-shift-keying(MCFSK), orthogonal frequency division multiplexing(OFDM), etc. In addition, in order to detect frame efficiently, we propose a light-weight frame synchronous algorithm. To verify the function of the systems, lake test and sea test are conducted for different algorithms.

关键词： Matlab Underwater acoustics Modulation Hardware Modems software algorithms Demodulation Underwater acoustic communication LabVIEW MATLAB suitable platform

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Fairness-aware Configuration of Machine Learning Libraries 22

Fairness-aware Configuration of Machine Learning Libraries

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ACM/IEEE 44th International Conference on software Engineering (ICSE)

作者： Tizpaz-Niari, Saeid Kumar, Ashish Tan, Gang Trivedi, Ashutosh Univ Texas El Paso El Paso TX 79968 USA Penn State Univ University Pk PA 16802 USA Univ Colorado Boulder Boulder CO USA

ISBN: (纸本)9781450392211

This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Datadriven software is increasingly applied in social-critical applications where ensuring fairness is of paramount importance. The existing approaches focus on addressing fairness bugs by either modifying the input dataset or modifying the learning algorithms. On the other hand, the selection of hyperparameters, which provide finer controls of ML algorithms, may enable a less intrusive approach to influence the fairness. Can hyperparameters amplify or suppress discrimination present in the input dataset? How can we help programmers in detecting, understanding, and exploiting the role of hyperparameters to improve the fairness? We design three search-based software testing algorithms to uncover the precision-fairness frontier of the hyperparameter space. We complement these algorithms with statistical debugging to explain the role of these parameters in improving fairness. We implement the proposed approaches in the tool Parfait-ML (PARameter FAIrness Testing for ML Libraries) and show its effectiveness and utility over five mature ML algorithms as used in six social-critical applications. In these applications, our approach successfully identified hyperparameters that significantly improve (vis-a-vis the state-of-the-art techniques) the fairness without sacrificing precision. Surprisingly, for some algorithms (e.g., random forest), our approach showed that certain configuration of hyperparameters (e.g., restricting the search space of attributes) can amplify biases across applications. Upon further investigation, we found intuitive explanations of these phenomena, and the results corroborate similar observations from the literature.

关键词： software testing Machine learning algorithms software algorithms Computer bugs Libraries software Space exploration

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Comparative Analysis of Machine Learning Methods Application for Financial Fraud Detection 32

Comparative Analysis of Machine Learning Methods Application...

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32nd Conference of Open-Innovations-Association-FRUCT

作者： Menshchikov, Alexander Perfilev, Vladislav Roenko, Denis Zykin, Maksim Fedosenko, Maksim ITMO Univ St Petersburg Russia

ISBN: (数字)9789526924489

ISBN: (纸本)9789526924489

This paper addresses the fraud detection problem in the context of Big Data used in remote banking systems. The paper aims to propose a new algorithm for automatic detection of fraudulent transactions using machine learning with a performance that allows to apply it in big data systems. The article identifies promising directions for optimizing the operation of methods for fraudulent transactions detection in anti-fraud systems. Architectural approaches to the operation of anti-fraud systems have been studied. Based on this, an architecture for illegal actions prediction in a near real-time mode was proposed. The research task of the article is to find the most suitable machine learning algorithm, with the least training and prediction time, demonstrating high classification performance. To achieve this goal, an analysis of the supervised and ensemble machine learning algorithms was made. The dataset was preprocessed for the experiment with SMOTE resampling and robust scaling techniques. The chosen methods were compared using different metrics: F1 score, AUC and time consumption for training and classification. As a result of a metrics comparison, it was found that multilayer perceptron (MLP) and boosting methods (Adaptive, Gradient, XGBoost) has the highest classification, but MLP outperforms boosting methods in terms of time consumption for classification. Thus, MLP was selected as the most appropriate algorithm for further integration to proposed Big Data architecture. Based on the data obtained during the experiments, the degree of their implementation in fraud detection systems was assessed and architecture for the anti-fraud detection system for big data was proposed.

关键词： Training Measurement Machine learning algorithms software algorithms Computer architecture Big Data Boosting

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Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

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Journal of Cyber Security and Mobility 2025年第3期12卷 275-296页

作者： Fadhil Mohammed Salman Ahssan Ahmmed Mohammed Ahmed Fakhir Mutar Ministry of Education Babylon Education Directorate Iraq Department of Computer Technology Engineering Al-Mustaqbal University College Iraq

Wireless Sensor Network (WSN) is a group of small, intelligent sensors with limited resources. WSN has limited energy restrictions, so, the network lifetime is the major challenge that directly affect the efficiency of the network. This work presents an energy-saving clustering hierarchical algorithm for WSNs; it is an improvement of Low-Energy adaptive Clustering Hierarchy (LEACH) algorithm. The aim of this algorithm is to minimize power consumption by the appropriate election of new cluster heads in every data transfer round and avoid network collisions. This goal achieved by using an efficient function to select the best cluster heads nodes in each round, which takes into account the current energy in the sensors. The proposed algorithm improves the cluster formation process by relying on the shorter distance to the base station. The Time Division Multiple Access (TDMA) mechanism also utilized to schedule the transmission of data packets to cluster heads nodes and to avoid data packet collisions at the base station. Experiments conducted in MATLAB R (2020a) software showed that the suggested algorithm extended the network lifetime by 14.5%, and improved the network throughput by 16.8% compared to the LEACH algorithm. That means, the proposed energy-saving clustering hierarchy algorithm has improved the performance of the LEACH algorithm in term of enhancing network lifetime and increasing network throughput.

关键词： Wireless sensor networks Time division multiple access Base stations Schedules Voting software algorithms Clustering algorithms Throughput software MATLAB

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Local Differential Privacy-Based Federated Learning for Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2021年第11期8卷 8836-8853页

作者： Zhao, Yang Zhao, Jun Yang, Mengmeng Wang, Teng Wang, Ning Lyu, Lingjuan Niyato, Dusit Lam, Kwok-Yan Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Xian Univ Posts Telecommun Sch Cyberspace Secur Xian 710121 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266102 Peoples R China Natl Univ Singapore Dept Comp Sci Singapore Singapore

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

关键词： Privacy Internet of Things Differential privacy Servers Crowdsourcing Cloud computing software algorithms Federated learning Internet of Things local differential privacy

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Application of Machine Learning in software Quality: a Mini-review

Application of Machine Learning in Software Quality: a Mini-...

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Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD), IEEE Multi-conference on

作者： Ozias Bombiri Pasteur Poda T. Frédéric Ouedraogo Laboratoire LAMDI Université Nazi BONI Bobo Dioulasso Burkina Faso UFR Sciences et Technologies Université Norbert ZONGO Koudougou Burkina Faso

The digital transformation underway in Sa-helian countries is accompanied by a growing interest in software engineering products in both the formal and informal sectors. The developed software mostly meet the needs of the customers, but it is not excluded that they suffer from compliance to the software quality standard. Thus, having intelligent systems that can assess whether important quality attributes are verified may be helpful. In this paper, we propose a mini-survey of different research works focusing on the improvement of software quality based on Machine Learning (ML) methods. The lessons learnt show that software quality management through ML methods is an active research field. software defect prediction and maintainability prediction are problems that had been treated by ML methods. Datasets, software metrics and ability of ML algorithms to build such predictive models have been the main target of the studies.

关键词： Machine learning algorithms software algorithms software quality Machine learning Predictive models Maintenance engineering Prediction algorithms

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software Informatization Security Platform in Big Data Environment

Software Informatization Security Platform in Big Data Envir...

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Computer Simulation and Modeling, Information Security (CSMIS), International Conference on

作者： Cunwei Zhao EUNCET Business School Polytechnic University of Catalonia Catalonia Spain

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

With the rapid development and mutual penetration of information technology and communication technology, people's daily life has undergone tremendous changes, and the era when communication basically relied on roaring has passed. The development of smartphones reflects the great convenience brought by technology. The development trend of smart phones reminds us that smart phones play an increasingly important role in the fields of mobile communication, mobile computing, mobile work, and mobile life. Research on the status and trends of mobile application platforms shows that smartphones will occupy a larger market. However, a lot of attention needs to be paid to software security. For this reason, this paper conducts design research on software information security platform in the big data environment. In this paper, the structure of the open software information security platform in the big data environment can be divided into service layer, application standard and protocol layer, data layer and hardware layer. Various systems, services and information are combined to establish a unified comprehensive service portal. Experiments show that the accuracy of all feature selection algorithms is above 80%, and the Relief algorithm has the best effect, with a maximum accuracy of 89%. Secondly, the better effect is the TF -IDF algorithm, and the worst effect is the chi-square test.

关键词： Wireless communication Computer viruses Social networking (online) software algorithms Information security Big Data Market research

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LoRay: AoA Estimation System for Long Range Communication Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第3期20卷 2005-2018页

作者： BniLam, Noori Joosens, Dennis Aernouts, Michiel Steckel, Jan Weyn, Maarten Univ Antwerp Fac Appl Engn IDLab Imec B-2000 Antwerp Belgium Univ Antwerp Cosys Lab Res Grp B-2000 Antwerp Belgium Flanders Make Strateg Res Ctr B-3920 Lommel Belgium

In this paper, we introduce a comprehensive angle of arrival (AoA) estimation solution for the long range (LoRa) communication network. Termed the LoRa array (LoRay), the proposed system constitutes hardware and software solutions to estimate the AoA of the received signals in real life urban environments. The hardware solution is based on converting multiple individual software defined radios (SDR) into a single SDR that consists of multiple RF-channels. The proposed hardware is cost effective, flexible and generic. The software solution, on the other hand, utilizes the space alternating generalized expectation-maximization (SAGE) algorithm to estimate the AoA of highly correlated received signals. The proposed software exploits few samples of the received signal to estimate the AoA of the direct and reflected paths in an intensive multipath environment. The LoRay system has been validated in outdoor urban environments. The experimental results show that the proposed system provides stable and accurate AoA estimates for both the line-of-sight (LoS) and the non-line-of-sight (NLoS) conditions. The AoA of 80% of the received signals have been estimated within an estimation error below 2 degrees and 10 degrees for the LoS and the NLoS locations, respectively.

关键词： Antenna arrays Estimation Hardware software algorithms software Receiving antennas Urban areas Angle of arrival AoA direction of arrival DoA direction finding DF LoRa LoRaWAN LPWAN outdoor AoA estimation IoT localization

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Hybrid Population Intelligence and Deep Learning Research for software Defect Prediction

Hybrid Population Intelligence and Deep Learning Research fo...

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Intelligent Computing, Communication & Convergence (ICI3C), International Conference on

作者： Guohong Pei Zhijiong Lu Jiaxing Jin Rongqing Feng Zhuhai College of Science and Technology China

ISBN: (数字)9798350343922

ISBN: (纸本)9798350343939

The goal of this project is to explore the topic of software defect prediction and provide a novel method for enhancing prediction model performance by fusing hybrid crowd intelligence with deep learning. In the study, we first use deep learning autoencoder for data dimensionality reduction, and achieve supervised pre-training of high-dimensional data by learning the intrinsic features of the data. Then, a deep neural network-based software defect prediction model was constructed, in which a hybrid population intelligence algorithm was used for hyperparameter optimisation. The hybrid algorithm combines the advantages of particle swarm optimisation and wolf pack algorithms for better global exploration and local optimisation when searching the hyperparameter space. In our experiments, we used a software defect dataset containing 58 features and compared the performance of five hyperparameter optimisation methods such as grid algorithm, stochastic algorithm, particle swarm algorithm, wolf pack algorithm and hybrid wolf pack algorithm. The experimental results show that the model using the hybrid wolfpack algorithm exhibits significant advantages in four evaluation metrics, including precision, recall, F-measure and AUC, and achieves more robust software defect prediction compared to other methods. This study not only promotes the application of deep learning in the field of software engineering, but also provides new empirical support for the effectiveness of group intelligence algorithms in software defect prediction.

关键词： Deep learning Dimensionality reduction software algorithms Optimization methods Stochastic processes Predictive models Prediction algorithms software Particle swarm optimization software engineering

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On the Performance of Mean-Based Sort for Large Data Sets

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IEEE ACCESS 2021年 9卷 37418-37430页

作者： Moghaddam, Shahriar Shirvani Moghaddam, Kiaksar Shirvani Shahid Rajaee Teacher Training Univ Fac Elect Engn Tehran *** Iran Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran

Computer and communication systems and networks deal with many cases that require rearrangement of data either in descending or ascending order. This operation is called sorting, and the purpose of an efficient sorting algorithm is to reduce the computational complexity and time taken to perform the comparison, swapping, and assignment operations. In this article, we propose an efficient mean-based sorting algorithm that sorts integer/non-integer data by making approximately the same length independent quasi-sorted subarrays. It gradually finds sorted data and checks if the elements are partially sorted or have similar values. The elapsed time, the number of divisions and swaps, and the difference between the locations of the sorted and unsorted data in different samples demonstrate the superiority of the proposed algorithm to the Merge, Quick, Heap, and conventional mean-based sorts for both integer and non-integer large data sets which are random or partially/entirely sorted. Numerical analyses indicate that the mean-based pivot is appropriate for making subarrays with approximately similar lengths. Also, the complexity study shows that the proposed mean-based sorting algorithm offers a memory complexity same as the Quick-sort and a time complexity better than the Merge, Heap, and Quick sorts in the best-case. It is similar to the Merge and Heap sorts in view of the time complexity of the worst-case much better than the Quick-sort while these algorithms experience identical complexity in the average-case. In addition to finding part by part incremental (or decremental) sorted data before reaching the end, it can be implemented by parallel processing the sections running at the same time faster than the other conventional algorithms due to having independent subarrays with similar lengths.

关键词： Sorting Approximation algorithms Arrays Time complexity software algorithms Parallel processing Licenses Ascending descending divide-and-conquer heap integer large data set merge non-integer quick sorting

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