Pub Date : 2022-05-16DOI: 10.1109/CITDS54976.2022.9914236
Djamila Talbi, Mohamed Amine Korteby, Zoltán Gál
Pico-cell based very high-speed wireless technologies require new medium access control mechanisms to provide top efficiency in the control plane. Beyond 5G and 6G wireless services are studied currently with synthetic data generated with special simulators. In this paper we used NS3 TeraSim tool to evaluate upload communication cases from mobile terminals to unique base station in different population and topology scenarios during 10 ms simulated time interval. Fractal based wavelet analysis is used to extract features of channel access in different simulation cases and classify them with recurrent neural network. The methodology utilized performs stable and unstable phases of the new IEEE pre-standard mechanism called Adaptive Directional Antenna Protocol for THz.
{"title":"Neural Network Based Analysis of Terahertz Frequency Signal Propagation for B5G/6G Wireless Networks","authors":"Djamila Talbi, Mohamed Amine Korteby, Zoltán Gál","doi":"10.1109/CITDS54976.2022.9914236","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914236","url":null,"abstract":"Pico-cell based very high-speed wireless technologies require new medium access control mechanisms to provide top efficiency in the control plane. Beyond 5G and 6G wireless services are studied currently with synthetic data generated with special simulators. In this paper we used NS3 TeraSim tool to evaluate upload communication cases from mobile terminals to unique base station in different population and topology scenarios during 10 ms simulated time interval. Fractal based wavelet analysis is used to extract features of channel access in different simulation cases and classify them with recurrent neural network. The methodology utilized performs stable and unstable phases of the new IEEE pre-standard mechanism called Adaptive Directional Antenna Protocol for THz.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"13 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":"124434528","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/CITDS54976.2022.9914051
Imre Gera, András London, András Pluhár
We propose an edge-based community detection algorithm that finds nested communities of a given graph. The communities are defined as the subgraphs induced by the edges of the same label and these edges together fulfill the property of network nestedness. Our method compares only possibly nested pairs of nodes and assigns all their edges to either common or different communities, realizing nested subgraphs. Finally, the algorithm removes superfluous communities in a post-processing step. We inspect the algorithm’s performance on a set of host-parasite networks and show the correlation between mean community size and the discrepancy nestedness measure. Since the algorithm’s performance is adjustable through a threshold parameter, we also investigate the effects of the parameter on the number of iterations and the obtained community structure.
{"title":"Greedy algorithm for edge-based nested community detection","authors":"Imre Gera, András London, András Pluhár","doi":"10.1109/CITDS54976.2022.9914051","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914051","url":null,"abstract":"We propose an edge-based community detection algorithm that finds nested communities of a given graph. The communities are defined as the subgraphs induced by the edges of the same label and these edges together fulfill the property of network nestedness. Our method compares only possibly nested pairs of nodes and assigns all their edges to either common or different communities, realizing nested subgraphs. Finally, the algorithm removes superfluous communities in a post-processing step. We inspect the algorithm’s performance on a set of host-parasite networks and show the correlation between mean community size and the discrepancy nestedness measure. Since the algorithm’s performance is adjustable through a threshold parameter, we also investigate the effects of the parameter on the number of iterations and the obtained community structure.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"759 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":"116124106","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/CITDS54976.2022.9914383
Mohamed Amine Korteby, Djamila Talbi, Zoltán Gál
Wireless Sensor Networks(WSNs) have made significant strides in recent years owing to their growth in terms of equipment and cost reduction. Several protocols have been designed, based on the application and network architecture. The LEACH (Low Energy Adaptive Clustering Hierarchy) mechanism is one of the most energy-efficient solutions in WSN environments. This hierarchical protocol aggregates and forwards data from cluster members to a fixed sink node using the cluster head feature of the nodes. We propose a new family of routing mechanisms called CB-LEACH by introducing movement possibility to the sink node and balancing the cluster head election decision based on the distances between the nodes and on the remaining energy of the potential cluster head candidates. We introduced the Hausdorff dimension, memory exponent, and common metric of fractality metrics to characterize the new routing mechanism It is proven that these metrics can highlight the most important features of the newly proposed CB-LEACH system
{"title":"Fractals and Wavelets Based Energy Analysis of Cost-Balanced LEACH Sensor Network","authors":"Mohamed Amine Korteby, Djamila Talbi, Zoltán Gál","doi":"10.1109/CITDS54976.2022.9914383","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914383","url":null,"abstract":"Wireless Sensor Networks(WSNs) have made significant strides in recent years owing to their growth in terms of equipment and cost reduction. Several protocols have been designed, based on the application and network architecture. The LEACH (Low Energy Adaptive Clustering Hierarchy) mechanism is one of the most energy-efficient solutions in WSN environments. This hierarchical protocol aggregates and forwards data from cluster members to a fixed sink node using the cluster head feature of the nodes. We propose a new family of routing mechanisms called CB-LEACH by introducing movement possibility to the sink node and balancing the cluster head election decision based on the distances between the nodes and on the remaining energy of the potential cluster head candidates. We introduced the Hausdorff dimension, memory exponent, and common metric of fractality metrics to characterize the new routing mechanism It is proven that these metrics can highlight the most important features of the newly proposed CB-LEACH system","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"26 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":"121103232","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/CITDS54976.2022.9914358
O. Pop, C. Rusu
In this paper we shall present some results about the operation of an acoustic sensor previously proposed. Our intention for the acoustic sensor is to be used in a remote geographic monitoring system. The sensor consists of four microphones arranged in four directions, aligned after the four cardinal points. It has previously been shown that the mechanical and acoustic structure allows the determination of the angle of arrival of sound waves generated by a sound source and in some cases the identification of the sound source after a specific signal processing. We shall discuss the processing and storage of audio data in a cloud and estimate the operation of the sensor in depth in natural areas.
{"title":"Acoustic sensor with four microphones for a network used in monitoring","authors":"O. Pop, C. Rusu","doi":"10.1109/CITDS54976.2022.9914358","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914358","url":null,"abstract":"In this paper we shall present some results about the operation of an acoustic sensor previously proposed. Our intention for the acoustic sensor is to be used in a remote geographic monitoring system. The sensor consists of four microphones arranged in four directions, aligned after the four cardinal points. It has previously been shown that the mechanical and acoustic structure allows the determination of the angle of arrival of sound waves generated by a sound source and in some cases the identification of the sound source after a specific signal processing. We shall discuss the processing and storage of audio data in a cloud and estimate the operation of the sensor in depth in natural areas.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"1 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":"128360146","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/CITDS54976.2022.9914092
Tibor Péter Kapusi, Laszlo Kovacs, A. Hajdu
Autonomous driving and self-driven vehicles have become among the most pursued research areas in recent years. Nowadays, various driving tasks can be solved by applying the newest machine learning techniques such as line tracking, traffic sign recognition, automated speed adjustment, and parking. However, difficult visual conditions and anomalies can cause problems in selected algorithms, which may occur unexcepted and failure operations in these cases. It is also expected not just very expensive to do such kinds of experiments, but these problematic conditions are also lead to dangerous traffic situations at the same time. We made an effort to put these kinds of studies into a cost-effective and safe model-scale environment. This paper introduces an anomaly detection method capable of recognizing abnormal and burnt-out objects in image scenes. Our proposed method is based on a fast neural network architecture using YOLO layers to detect regions. Our experiments demonstrate the capabilities and detection accuracy of the designed neural network, called anomalyNet, with the complete training and evaluation process. In the study, we work with publicly available datasets, but our model-sized track and DAVE (University of Debrecen Autonomous VehiclE) play an important role also.
{"title":"Deep learning-based anomaly detection for imaging in autonomous vehicles","authors":"Tibor Péter Kapusi, Laszlo Kovacs, A. Hajdu","doi":"10.1109/CITDS54976.2022.9914092","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914092","url":null,"abstract":"Autonomous driving and self-driven vehicles have become among the most pursued research areas in recent years. Nowadays, various driving tasks can be solved by applying the newest machine learning techniques such as line tracking, traffic sign recognition, automated speed adjustment, and parking. However, difficult visual conditions and anomalies can cause problems in selected algorithms, which may occur unexcepted and failure operations in these cases. It is also expected not just very expensive to do such kinds of experiments, but these problematic conditions are also lead to dangerous traffic situations at the same time. We made an effort to put these kinds of studies into a cost-effective and safe model-scale environment. This paper introduces an anomaly detection method capable of recognizing abnormal and burnt-out objects in image scenes. Our proposed method is based on a fast neural network architecture using YOLO layers to detect regions. Our experiments demonstrate the capabilities and detection accuracy of the designed neural network, called anomalyNet, with the complete training and evaluation process. In the study, we work with publicly available datasets, but our model-sized track and DAVE (University of Debrecen Autonomous VehiclE) play an important role also.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"5 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":"126217099","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/CITDS54976.2022.9914158
Róbert Lakatos, G. Bogacsovics, A. Hajdu
In this paper, we present a natural text processing model for predicting the price of exchange-traded products based on machine learning and general statistics. With the help of our model, we are forecasting the trend of one of the most important energy, the oil prices daily basis from tweets. The backbone of our model consists of transformer-based techniques in a recurrent neural network framework with corresponding hyperparameter optimization. The essence of our solution is to use the sentiment characteristics and vocabulary that can be extracted from the tweeter news. We have found that some of the news sources have better correlated to the oil price change which observation was used to refine the training corpus. Furthermore, we have applied noise filtering by removing the insignificant words from the textual information. In this way, we have generated a data source from which the sentiment values showed a high-precision correlation of 84.08% with the true direction of the oil price.
{"title":"Predicting the direction of the oil price trend using sentiment analysis","authors":"Róbert Lakatos, G. Bogacsovics, A. Hajdu","doi":"10.1109/CITDS54976.2022.9914158","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914158","url":null,"abstract":"In this paper, we present a natural text processing model for predicting the price of exchange-traded products based on machine learning and general statistics. With the help of our model, we are forecasting the trend of one of the most important energy, the oil prices daily basis from tweets. The backbone of our model consists of transformer-based techniques in a recurrent neural network framework with corresponding hyperparameter optimization. The essence of our solution is to use the sentiment characteristics and vocabulary that can be extracted from the tweeter news. We have found that some of the news sources have better correlated to the oil price change which observation was used to refine the training corpus. Furthermore, we have applied noise filtering by removing the insignificant words from the textual information. In this way, we have generated a data source from which the sentiment values showed a high-precision correlation of 84.08% with the true direction of the oil price.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"4 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":"125247897","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/CITDS54976.2022.9914037
László Kovács, Dávid Baranyai, Tamás Girászi, T. Majoros, Ádám Kovács, Máté Vágner, Dénes Palkovics, T. Bérczes
Autonomous vehicles use several different kinds of sensors to get information about the surrounding area. With sensors and artificial intelligence, the autonomous vehicle tries to find the optimal decision as close as possible to the appropriate behavior. Because of the huge amount of data, the usage of modern machine learning and data-driven approaches is necessary. Although computing big data is not easily handled especially onboard a vehicle, the critical mass of the diverse data generated from different sources is essential. In the field of autonomous vehicles, there have not been standards yet, but the range of applied sensors is well-known. Most systems use a combination of cameras, radar, and LIDAR (Light Detection and Ranging) sensors that transmit data to a central computer that detects the environment around the car. Self-driving development could be supported with model-sized self-driving vehicles because of the complexity of the area. The development of autonomous vehicles consists of security, communication, and data processing issues. Mistakes are increasing the risks of potential accidents. The realistic environment which can be simulated or built makes it possible that the learned behavior can be carried across the platforms while the differences in the sizes are not playing an important role in the matter of learning. The previous reason causes the model-size self-driving development to be more cost-effective. In our work, we developed a self-driving model car with different types of sensors. Measurement data from them can be used to improve the self-driving capabilities of the vehicle.
{"title":"Sensor design and integration into small sized autonomous vehicle","authors":"László Kovács, Dávid Baranyai, Tamás Girászi, T. Majoros, Ádám Kovács, Máté Vágner, Dénes Palkovics, T. Bérczes","doi":"10.1109/CITDS54976.2022.9914037","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914037","url":null,"abstract":"Autonomous vehicles use several different kinds of sensors to get information about the surrounding area. With sensors and artificial intelligence, the autonomous vehicle tries to find the optimal decision as close as possible to the appropriate behavior. Because of the huge amount of data, the usage of modern machine learning and data-driven approaches is necessary. Although computing big data is not easily handled especially onboard a vehicle, the critical mass of the diverse data generated from different sources is essential. In the field of autonomous vehicles, there have not been standards yet, but the range of applied sensors is well-known. Most systems use a combination of cameras, radar, and LIDAR (Light Detection and Ranging) sensors that transmit data to a central computer that detects the environment around the car. Self-driving development could be supported with model-sized self-driving vehicles because of the complexity of the area. The development of autonomous vehicles consists of security, communication, and data processing issues. Mistakes are increasing the risks of potential accidents. The realistic environment which can be simulated or built makes it possible that the learned behavior can be carried across the platforms while the differences in the sizes are not playing an important role in the matter of learning. The previous reason causes the model-size self-driving development to be more cost-effective. In our work, we developed a self-driving model car with different types of sensors. Measurement data from them can be used to improve the self-driving capabilities of the vehicle.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"1 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":"131280884","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/CITDS54976.2022.9914116
Arpad Pandy, Dávid-Gyula Kun, Laszlo Kovacs, Gábor Vasváry, Zoltán Pánti, A. Hajdu
Autonomous driving is an emerging field of research. The related industry is one of the most expensive areas nowadays. The core of these complex controlling systems is the perception of the environment and the usage of actuators for changing supported by sensors to give obvious feedback about the change of state. Steering controlling is such a subsystem. In the real-sized modern car, there are several methods for implementing feedback loops for it, such as torque and angle sensors. In this paper, we concentrate on extending our model-based research and development autonomous vehicle platform – DAVE to be able to study the hard conditions safely and cost-effectively. This work presents a method for a new sensor signal integrated into our CAN-BUS system to give feedback about the steering movement of the wheels to a digital steering controller using a rear-view camera. The advantage of using a rear-view blind-spot camera is that it is already in place, and no additional hardware is needed to use it as a pseudo angle sensor.
{"title":"Image sensor based steering signal for a digital actuator system","authors":"Arpad Pandy, Dávid-Gyula Kun, Laszlo Kovacs, Gábor Vasváry, Zoltán Pánti, A. Hajdu","doi":"10.1109/CITDS54976.2022.9914116","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914116","url":null,"abstract":"Autonomous driving is an emerging field of research. The related industry is one of the most expensive areas nowadays. The core of these complex controlling systems is the perception of the environment and the usage of actuators for changing supported by sensors to give obvious feedback about the change of state. Steering controlling is such a subsystem. In the real-sized modern car, there are several methods for implementing feedback loops for it, such as torque and angle sensors. In this paper, we concentrate on extending our model-based research and development autonomous vehicle platform – DAVE to be able to study the hard conditions safely and cost-effectively. This work presents a method for a new sensor signal integrated into our CAN-BUS system to give feedback about the steering movement of the wheels to a digital steering controller using a rear-view camera. The advantage of using a rear-view blind-spot camera is that it is already in place, and no additional hardware is needed to use it as a pseudo angle sensor.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"6 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":"114241275","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/CITDS54976.2022.9914271
Péter Vörös, Dávid Kis, P. Hudoba, Gergely Pongrácz, S. Laki
Software-defined networking and data-plane programmability have opened up the possibilities for switches to be used for novel applications that are different than simple packet forwarding. Various tasks from low-level robot control to signal and data processing can be offloaded to network devices. In the past years, solutions exploiting programmable switching ASIC, FPGA or the combination of both have emerged. In this paper, we propose a GPU-accelerated switch design for supporting payload processing tasks in the network. The proposed design combines the processing capabilities of GPUs and the kernel-bypass library DPDK. We define different image processing use cases that can benefit from in-network computing, allowing execution without the need for an external server. The proposed method cannot only make the overall system performance better, but also reduce the power consumption since it requires less hardware elements. We evaluate and compare three models: Traditional external server with GPU in the local network, DPDK accelerated version of the previous model and the proposed GPU-accelerated in-network computing switch model. We investigate several benchmarks including both component-level and system-wide analysis. The examined use cases are related to video stream processing tasks like box blurring, Gaussian blurring and edge detection, demonstrating the performance improvement of our proposed design.
{"title":"Towards an in-network GPU-accelerated packet processing framework","authors":"Péter Vörös, Dávid Kis, P. Hudoba, Gergely Pongrácz, S. Laki","doi":"10.1109/CITDS54976.2022.9914271","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914271","url":null,"abstract":"Software-defined networking and data-plane programmability have opened up the possibilities for switches to be used for novel applications that are different than simple packet forwarding. Various tasks from low-level robot control to signal and data processing can be offloaded to network devices. In the past years, solutions exploiting programmable switching ASIC, FPGA or the combination of both have emerged. In this paper, we propose a GPU-accelerated switch design for supporting payload processing tasks in the network. The proposed design combines the processing capabilities of GPUs and the kernel-bypass library DPDK. We define different image processing use cases that can benefit from in-network computing, allowing execution without the need for an external server. The proposed method cannot only make the overall system performance better, but also reduce the power consumption since it requires less hardware elements. We evaluate and compare three models: Traditional external server with GPU in the local network, DPDK accelerated version of the previous model and the proposed GPU-accelerated in-network computing switch model. We investigate several benchmarks including both component-level and system-wide analysis. The examined use cases are related to video stream processing tasks like box blurring, Gaussian blurring and edge detection, demonstrating the performance improvement of our proposed design.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"51 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":"124587919","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/citds54976.2022.9914195
{"title":"CITDS 2022 Cover Page","authors":"","doi":"10.1109/citds54976.2022.9914195","DOIUrl":"https://doi.org/10.1109/citds54976.2022.9914195","url":null,"abstract":"","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"24 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":"127056249","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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