Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914270
János Kontos, B. Kránicz, Ágnes Vathy-Fogarassy
Lateral acceleration is a key element of vehicle dynamics. It is consumed by several control, stability and comfort functions of the vehicle. In this paper a neural network-based prediction method is demonstrated for predicting the value of lateral acceleration. The inputs of the method are the most accessible signals in any modern vehicle: wheel speed information, longitudinal acceleration and steering wheel angle. For training, validating and testing the neural network, experimental data was used. The hyperparameters of the neural network were tuned by a hybrid approach. The accuracy of the approach was evaluated by comparing the actual measured values to those predicted by the neural network. Evaluation results convincingly demonstrate the usefulness and reliability of the developed model.
{"title":"Neural Network-Based Prediction for Lateral Acceleration of Vehicles","authors":"János Kontos, B. Kránicz, Ágnes Vathy-Fogarassy","doi":"10.1109/CITDS54976.2022.9914270","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914270","url":null,"abstract":"Lateral acceleration is a key element of vehicle dynamics. It is consumed by several control, stability and comfort functions of the vehicle. In this paper a neural network-based prediction method is demonstrated for predicting the value of lateral acceleration. The inputs of the method are the most accessible signals in any modern vehicle: wheel speed information, longitudinal acceleration and steering wheel angle. For training, validating and testing the neural network, experimental data was used. The hyperparameters of the neural network were tuned by a hybrid approach. The accuracy of the approach was evaluated by comparing the actual measured values to those predicted by the neural network. Evaluation results convincingly demonstrate the usefulness and reliability of the developed model.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914128
Brenton Budler, Ritesh Ajoodha
Detecting network intrusions is an imperative part of the modern cybersecurity landscape. Over the years, researchers have leveraged the ability of Machine Learning to identify and prevent network attacks. Recently there has been an increased interest in the applicability of Deep Learning in the network intrusion detection domain. However, Network Intrusion Detection Systems developed using Deep Learning approaches are being evaluated using the outdated KDD Cup 99 and NSLKDD datasets which are not representative of real-world network traffic. Recent comparisons of these approaches on the more modern CSE-CIC-IDS2018 dataset, fail to address the severe class imbalance in the dataset which leads to significantly biased results. By addressing this class imbalance and performing an experimental evaluation of a Deep Neural Network, Convolutional Neural Network and Long Short-Term Memory Network on the balanced dataset, this research provides deeper insights into the performance of these models in classifying modern network traffic data. The Deep Neural Network demonstrated the best classification performance with the highest accuracy (84.312%) and Fl-Score (83.799%) as well as the lowest False Alarm Rate (2.615%).
检测网络入侵是现代网络安全领域必不可少的一部分。多年来，研究人员利用机器学习的能力来识别和防止网络攻击。最近，人们对深度学习在网络入侵检测领域的适用性越来越感兴趣。然而，使用深度学习方法开发的网络入侵检测系统正在使用过时的KDD Cup 99和NSLKDD数据集进行评估，这些数据集不能代表现实世界的网络流量。最近在更现代的CSE-CIC-IDS2018数据集上对这些方法的比较，未能解决数据集中严重的类别不平衡，导致结果显着偏倚。通过解决这类不平衡问题，并在平衡数据集上对深度神经网络、卷积神经网络和长短期记忆网络进行实验评估，本研究为这些模型在现代网络流量数据分类中的性能提供了更深入的见解。其中，Deep Neural Network的分类准确率最高(84.312%)，Fl-Score最高(83.799%)，虚警率最低(2.615%)。
{"title":"Comparative Analysis of Deep Learning Models for Network Intrusion Detection Systems","authors":"Brenton Budler, Ritesh Ajoodha","doi":"10.1109/CITDS54976.2022.9914128","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914128","url":null,"abstract":"Detecting network intrusions is an imperative part of the modern cybersecurity landscape. Over the years, researchers have leveraged the ability of Machine Learning to identify and prevent network attacks. Recently there has been an increased interest in the applicability of Deep Learning in the network intrusion detection domain. However, Network Intrusion Detection Systems developed using Deep Learning approaches are being evaluated using the outdated KDD Cup 99 and NSLKDD datasets which are not representative of real-world network traffic. Recent comparisons of these approaches on the more modern CSE-CIC-IDS2018 dataset, fail to address the severe class imbalance in the dataset which leads to significantly biased results. By addressing this class imbalance and performing an experimental evaluation of a Deep Neural Network, Convolutional Neural Network and Long Short-Term Memory Network on the balanced dataset, this research provides deeper insights into the performance of these models in classifying modern network traffic data. The Deep Neural Network demonstrated the best classification performance with the highest accuracy (84.312%) and Fl-Score (83.799%) as well as the lowest False Alarm Rate (2.615%).","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122081106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914260
Sofiane Ouaari, Tsegaye Misikir Tashu, Tomáš Horváth
In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme.
{"title":"Multimodal Feature Extraction for Memes Sentiment Classification","authors":"Sofiane Ouaari, Tsegaye Misikir Tashu, Tomáš Horváth","doi":"10.1109/CITDS54976.2022.9914260","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914260","url":null,"abstract":"In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914189
Csaád Bertók, Andrea Huszti, Tamás Kádek, Zsanett Jámbor
We construct a multi-round, secure password hashing scheme that is designed to be resistant against off-line attacks, such as brute force, dictionary and rainbow table attacks. We compare our scheme to the password hashing algorithms used in practice from the point of view of the technical requirements of the Password Hashing Competition. We provide a security analysis, which shows that the proposed algorithm is also collision, hence second pre-image resistant.
{"title":"A multi-round bilinear-map-based secure password hashing scheme","authors":"Csaád Bertók, Andrea Huszti, Tamás Kádek, Zsanett Jámbor","doi":"10.1109/CITDS54976.2022.9914189","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914189","url":null,"abstract":"We construct a multi-round, secure password hashing scheme that is designed to be resistant against off-line attacks, such as brute force, dictionary and rainbow table attacks. We compare our scheme to the password hashing algorithms used in practice from the point of view of the technical requirements of the Password Hashing Competition. We provide a security analysis, which shows that the proposed algorithm is also collision, hence second pre-image resistant.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120861687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914210
Taisiia Morozova, I. Kaj
In this work we consider an uplink cellular network with the focus on a typical cell rather than the whole network. The base stations (BSs) and the users are distributed according to Poisson point processes (PPP) and the signals are transmitted at random power. The BSs’ serving area is formed according to the Voronoi diagram and the users are associated with a serving BS based on the shortest distance. One of the features of the system is that we primarily take into account the interference inside a d-dimensional ball of the average size of a typical Voronoi cell. In this work we mainly focus on the system stability and discuss a necessary stability condition, which is then studied by using stochastic simulation. We also discuss some properties of the network that can affect the stability and appear to be interesting and promising for the performance analysis of the system.
{"title":"Analysis of a typical cell in the uplink cellular network model using stochastic simulation","authors":"Taisiia Morozova, I. Kaj","doi":"10.1109/CITDS54976.2022.9914210","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914210","url":null,"abstract":"In this work we consider an uplink cellular network with the focus on a typical cell rather than the whole network. The base stations (BSs) and the users are distributed according to Poisson point processes (PPP) and the signals are transmitted at random power. The BSs’ serving area is formed according to the Voronoi diagram and the users are associated with a serving BS based on the shortest distance. One of the features of the system is that we primarily take into account the interference inside a d-dimensional ball of the average size of a typical Voronoi cell. In this work we mainly focus on the system stability and discuss a necessary stability condition, which is then studied by using stochastic simulation. We also discuss some properties of the network that can affect the stability and appear to be interesting and promising for the performance analysis of the system.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"22 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914230
A. Alexan, Anca Alexan, S. Oniga
Nowadays, many of us wear multiple devices capable of acquiring and storing data related to our everyday activities. Since the computing power of mobile battery-operated devices slowly increases and the power optimizations allow for more and more continuous use, these devices are capable of not only monitoring our activity but analyzing the activity as well. Of these devices, the smartwatch is probably the most inconspicuous, and due to its widespread use, we have used accelerometer data gathered from a smartwatch to identify common user activities by using image generated plots and image recognition machine learning. By leveraging the.Net ML.NET machine learning framework we have managed to obtain a decent recognition rate.
{"title":"Smart watch activity recognition using plot image analysis","authors":"A. Alexan, Anca Alexan, S. Oniga","doi":"10.1109/CITDS54976.2022.9914230","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914230","url":null,"abstract":"Nowadays, many of us wear multiple devices capable of acquiring and storing data related to our everyday activities. Since the computing power of mobile battery-operated devices slowly increases and the power optimizations allow for more and more continuous use, these devices are capable of not only monitoring our activity but analyzing the activity as well. Of these devices, the smartwatch is probably the most inconspicuous, and due to its widespread use, we have used accelerometer data gathered from a smartwatch to identify common user activities by using image generated plots and image recognition machine learning. By leveraging the.Net ML.NET machine learning framework we have managed to obtain a decent recognition rate.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114802751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914338
Atallo Kassaw Takele, B. Villányi
The industrial internet of things (IIoT) enhances industrial and manufacturing operations by using smart sensors and actuators. However, it is hampered due to the energy efficiency requirements, real time performance requirements in a dynamic environment, and maintaining the security of applications. Security is a serious issue nowadays and is mostly caused by abnormal traffic of some nodes. For detecting those abnormalities, there are two basic machine learning approaches, namely Federated and Centralized Learning. Centralized Learning has better performance, but it has a privacy issue since edge devices send data to the server. On the other hand, Federate Learning obviates privacy issues, but it has less performance due to the resource limitation of edge devices. In this study, a typical hybrid learning based abnormality detection framework has been proposed in which edge devices undertake Federated Learning with a limited number of datasets and the edge server will use the periodically collected aggregated data from edge devices. For security reasons, edge devices share their data after a certain period of time when the time value of the data has declined. We have used Long Short Term Memory (LSTM) Autoencoders with two different datasets (a smaller for edge devices and a larger for the edge server) for the demonstration. The experimental result shows that the size of the dataset affects the predicting performance and resource utilization in an anomaly detection model.
{"title":"Anomaly Detection Using Hybrid Learning for Industrial IoT","authors":"Atallo Kassaw Takele, B. Villányi","doi":"10.1109/CITDS54976.2022.9914338","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914338","url":null,"abstract":"The industrial internet of things (IIoT) enhances industrial and manufacturing operations by using smart sensors and actuators. However, it is hampered due to the energy efficiency requirements, real time performance requirements in a dynamic environment, and maintaining the security of applications. Security is a serious issue nowadays and is mostly caused by abnormal traffic of some nodes. For detecting those abnormalities, there are two basic machine learning approaches, namely Federated and Centralized Learning. Centralized Learning has better performance, but it has a privacy issue since edge devices send data to the server. On the other hand, Federate Learning obviates privacy issues, but it has less performance due to the resource limitation of edge devices. In this study, a typical hybrid learning based abnormality detection framework has been proposed in which edge devices undertake Federated Learning with a limited number of datasets and the edge server will use the periodically collected aggregated data from edge devices. For security reasons, edge devices share their data after a certain period of time when the time value of the data has declined. We have used Long Short Term Memory (LSTM) Autoencoders with two different datasets (a smaller for edge devices and a larger for the edge server) for the demonstration. The experimental result shows that the size of the dataset affects the predicting performance and resource utilization in an anomaly detection model.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116076830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914190
B. Szabó
Simulators for operator training have long been in use for nuclear power plants. These are traditionally full-scope simulators with an expensive physical replica of the control room. Recent advances in virtual reality headsets provide affordable means for presenting a stereoscopic view of a virtual model of the control room. While the commercially available headsets are still not perfect, they offer more realism than flat screens, showing a stereoscopic view. The paper provides some details on how a virtual reality headset has been utilized for viewing the virtual control room of the Paks Nuclear Power Plant, modeled with the Blender Game Engine, with an added autofocus feature based on a pragmatic method. Some aspects of the solution are outlined, and, partially based on the experiences gained in the project, current problems and future trends of virtual reality headsets are discussed.
{"title":"Using a virtual reality headset in the simulation of the control room of a nuclear power plant","authors":"B. Szabó","doi":"10.1109/CITDS54976.2022.9914190","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914190","url":null,"abstract":"Simulators for operator training have long been in use for nuclear power plants. These are traditionally full-scope simulators with an expensive physical replica of the control room. Recent advances in virtual reality headsets provide affordable means for presenting a stereoscopic view of a virtual model of the control room. While the commercially available headsets are still not perfect, they offer more realism than flat screens, showing a stereoscopic view. The paper provides some details on how a virtual reality headset has been utilized for viewing the virtual control room of the Paks Nuclear Power Plant, modeled with the Blender Game Engine, with an added autofocus feature based on a pragmatic method. Some aspects of the solution are outlined, and, partially based on the experiences gained in the project, current problems and future trends of virtual reality headsets are discussed.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914181
S. Szilágyi, László Kovács
A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment.
车辆网络是由配备无线电接口的车辆组成的通信系统，其中端点能够在彼此之间(车对车，V2V)以及与另一个移动网络或固定基础设施(车对基础设施，V2I)交换数据和通信。车辆中使用的众多应用程序通常需要无缝且日益快速可靠的网络连接，这对我们今天所掌握的无线网络技术提出了挑战。德国德布勒森大学(University of Debrecen)开发的MPT-GRE是一种多接口接入技术，它可以提供一种新颖的解决方案，以满足车联网应用中使用的服务需求。考虑到MPT-GRE可以同时使用多个网络接口和ip路由，它有望成为车载网络的有效解决方案。在本文中，我们使用双接口Wi-Fi环境下的自动驾驶汽车模型来检验MPT-GRE的效率。
{"title":"Testing MPT-GRE Multipath Solution in Vehicular Network V2I Communication","authors":"S. Szilágyi, László Kovács","doi":"10.1109/CITDS54976.2022.9914181","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914181","url":null,"abstract":"A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126181314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914052
Carolin Hannusch, Sándor Roland Major
We describe a software package created by the authors that can be used to search for linear binary codes with almost arbitrary conditions. The package is easily extensible and reconfigurable to suit the specific needs of the search. The main function can be used to search for currently unknown linear codes, or to quickly generate examples of known codes.
{"title":"Torch: Software Package For The Search Of Linear Binary Codes","authors":"Carolin Hannusch, Sándor Roland Major","doi":"10.1109/CITDS54976.2022.9914052","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914052","url":null,"abstract":"We describe a software package created by the authors that can be used to search for linear binary codes with almost arbitrary conditions. The package is easily extensible and reconfigurable to suit the specific needs of the search. The main function can be used to search for currently unknown linear codes, or to quickly generate examples of known codes.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126042999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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