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2021 IEEE Symposium on Computers and Communications (ISCC)最新文献

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Analysis of Comorbidities of Alcohol Use Disorder 酒精使用障碍的合并症分析
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631512
A. Ebrahimi, U. Wiil, Marjan Mansourvar, A. Naemi, K. Andersen, A. Nielsen
{"title":"Analysis of Comorbidities of Alcohol Use Disorder","authors":"A. Ebrahimi, U. Wiil, Marjan Mansourvar, A. Naemi, K. Andersen, A. Nielsen","doi":"10.1109/ISCC53001.2021.9631512","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631512","url":null,"abstract":"Alcohol Use Disorder (AUD) is a clinical diagnosis based on signs and symptoms that are related to excessive alcohol use and it increases the risk of many clinical conditions, psychological instabilities, and social issues. In this study, we aim to identify the comorbidities of Hazardous and Harmful drinkers. We obtained comorbidity networks for Hazardous, and Harmful drinkers using social network analysis techniques. Each network consists of several nodes that represent the diagnostic codes that patients were given during their hospitalization. To precisely identify the most exclusive comorbidities in each drinking group, we proposed a four-step process based on a machine learning algorithm and aggregation functions. Our findings show that the majority of the identified comorbidities in the Harmful drinking group are related to ICD-10 chapters XI, XIX, and XIII. The comorbidities of the Hazardous drinking group, however, did not present a similarly clear pattern.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937920","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}
引用次数: 1
Research and Application of Reinforcement Learning Recommendation Method for Taobao 淘宝强化学习推荐方法的研究与应用
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631429
Lan Huang, Xiaofang Zhang, Yan Wang, Xuping Xie
{"title":"Research and Application of Reinforcement Learning Recommendation Method for Taobao","authors":"Lan Huang, Xiaofang Zhang, Yan Wang, Xuping Xie","doi":"10.1109/ISCC53001.2021.9631429","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631429","url":null,"abstract":"Nowadays, many e-commerce companies are using reinforcement learning recommendation methods to maximize long-term benefits. Alibaba Group and Nanjing University build “Virtual Taobao”, a Taobao simulator. In this paper, we proposed TTD3 based on TD3 and trained it in Virtual Taobao. There are three important improvements in TTD3's training process. First, the current actor-network and target actor-network will predict two candidate actions for Virtual Taobao's current state, and the action with a larger value evaluated by the current critic-network is selected as the final execution action. Second, the Ornstein-Uhlenbeck (OU) process is used as the exploration noise to improve the agent's ability to explore Virtual Taobao. Third, prioritized experience replay is adopted to improve sampling efficiency. TTD3 achieves the highest average CTR of about 0.85 in Virtual Taobao which is superior to TD3 as well as DPPO, SAC, and DDPG used by Virtual Taobao's author.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124084894","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}
引用次数: 0
Proportional representation to increase data utility in k-anonymous tables 比例表示增加k-匿名表中的数据效用
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631457
Fabien Viton, Clémence Mauger, Gilles Dequen, Jean-Luc Guérin, G. L. Mahec
{"title":"Proportional representation to increase data utility in k-anonymous tables","authors":"Fabien Viton, Clémence Mauger, Gilles Dequen, Jean-Luc Guérin, G. L. Mahec","doi":"10.1109/ISCC53001.2021.9631457","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631457","url":null,"abstract":"The increasing number of published data has allowed the development of data mining, resting on the use of the data to extract knowledge. At the same time, to tackle privacy concerns, anonymization models such as k-anonymity have emerged. Because k-anonymity transforms original data, there is an impact on the utility of altered data for data mining. In this paper, we propose a new writing of the anonymous tables using an anonymization post-treatment. The proposed representation allows to keep more information on the distribution of the original values in the anonymous equivalence classes while being usable directly as input for neural networks for data mining purposes. We test our experimental protocol on two data sets from anonymization research field: Adult data set and an extract from the register of voters of Florida (USA). With these experiments, we show the superiority in data utility of our approach against classical approaches.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127133042","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}
引用次数: 0
Automatic Parameter Tuning for Big Data Pipelines with Deep Reinforcement Learning 基于深度强化学习的大数据管道参数自动调优
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631440
Houssem Sagaama, Nourchene Ben Slimane, Maher Marwani, S. Skhiri
{"title":"Automatic Parameter Tuning for Big Data Pipelines with Deep Reinforcement Learning","authors":"Houssem Sagaama, Nourchene Ben Slimane, Maher Marwani, S. Skhiri","doi":"10.1109/ISCC53001.2021.9631440","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631440","url":null,"abstract":"Tuning big data frameworks is a very important task to get the best performance for a given application. However, these frameworks are rarely used individually, they generally constitute a pipeline, each having a different role. This makes tuning big data pipelines an important yet difficult task given the size of the search space. Moreover, we have to consider the interaction between these frameworks when tuning the configuration parameters of the big data pipeline. A trade-off is then required to achieve the best end-to-end performance. Machine learning based methods have shown great success in automatic tuning systems, but they rely on a large number of high quality learning examples that are rather difficult to obtain. In this context, we propose to use a deep reinforcement learning algorithm, namely Twin Delayed Deep Deterministic Policy Gradient, TD3, to tune a fraud detection big data pipeline. We show through the conducted experiments that the TD3 agent improves the overall performance of the pipeline by up to 63% with only 200 training steps, outperforming the random search on the high-dimensional search space.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127677864","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}
引用次数: 3
DeepGAN: Generating Molecule for Drug Discovery Based on Generative Adversarial Network 深度gan:基于生成对抗网络的药物发现分子生成
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631396
Mengdi Xu, Jiandong Cheng, Yirong Liu, Wei Huang
{"title":"DeepGAN: Generating Molecule for Drug Discovery Based on Generative Adversarial Network","authors":"Mengdi Xu, Jiandong Cheng, Yirong Liu, Wei Huang","doi":"10.1109/ISCC53001.2021.9631396","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631396","url":null,"abstract":"As one of the most core links in the pharmaceutical industry, drug discovery is an important direction for the application of artificial intelligence technology. It is still a huge challenge to accelerate the discovery process. To address it, we have developed a generative model for de novo small-molecule based on Generative Adversarial Network algorithm called DeepGAN. It is worth mentioning that we make DeepSMILES as training object, which has avoided the limitations of SMILES. And the addition of reinforcement learning keeps away from non-differentiable problem of the discriminator. The model is trained to optimize the rewards and adversarial loss in specific areas through strategy gradient. In this way, DeepGAN compares favorably to ORGAN and its derivatives OR(W)GAN and Naive RL which have been already well-tested. The experiments indicate our model can create molecules which can maintain molecular diversity, increase validity and show improvement in the desired metrics.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818456","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}
引用次数: 2
GAVIN: A new platform for enriching 3D virtual indoor navigation with social-based geotags 加文:一个利用基于社会的地理标签丰富3D虚拟室内导航的新平台
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631432
Christian Sicari, Valeria Lukaj, A. Celesti, M. Fazio, M. Villari
{"title":"GAVIN: A new platform for enriching 3D virtual indoor navigation with social-based geotags","authors":"Christian Sicari, Valeria Lukaj, A. Celesti, M. Fazio, M. Villari","doi":"10.1109/ISCC53001.2021.9631432","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631432","url":null,"abstract":"Social Networks, geotags, and Virtual Reality (VR) are parts of the everyday life of most of the people in the world. In particular, geotags represents a way to discover places through metadata added to media. At the same time, VR reproduces with high accuracy real places that we can navigate through a browser or a 3D visor. In this paper, we present GAVIN to fill the gap between social media geotagging and Virtual Indoor Navigation. GAVIN is a platform for the navigation of virtual environments able to exploit BIM Digital Twins and geolocated data coming from different sources and, in particular, from the social experience bounded to a real/virtual place. In the paper we provide design and implementation details on the GAVIN platform, and we present some performance evaluations.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923318","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}
引用次数: 0
A Scalable Real-Time Multiagent Decision Making Algorithm with Cost 一种具有成本的可扩展实时多智能体决策算法
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631510
P. Cotae, Myong Kang, Alexander Velazquez
{"title":"A Scalable Real-Time Multiagent Decision Making Algorithm with Cost","authors":"P. Cotae, Myong Kang, Alexander Velazquez","doi":"10.1109/ISCC53001.2021.9631510","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631510","url":null,"abstract":"We focus on a real-time multiagent decision making problem in a collaborative setting including a cost factor for the planning and execution of actions. We present the centralized coordination of a multiagent system in which the team must make a collaborative decision to maximize the global payoff. We used the framework of Coordination Graphs, which exploit dependencies among agents to decompose the global payoff function value as the sum of local terms. We revise the centralized Max-Plus algorithm by presenting a new Cost Max-Plus algorithm for planning and acting by including the cost in the local interactions of agents. We propose a two-step planning and acting algorithm called Factored Value-MCTS-Cost-Max-Plus algorithm that is online, anytime, and scalable in terms of the number of agents and their local interactions.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129729566","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}
引用次数: 0
Organising Committee 组委会
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1016/B978-0-08-034798-1.50005-7
D. Green
{"title":"Organising Committee","authors":"D. Green","doi":"10.1016/B978-0-08-034798-1.50005-7","DOIUrl":"https://doi.org/10.1016/B978-0-08-034798-1.50005-7","url":null,"abstract":"","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960536","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}
引用次数: 2
A novel MLP based on compensation method for the effects of High Power Amplifier N onlinearities in Non-Linear SCMA systems 一种基于补偿方法的非线性SCMA系统中大功率放大器N的非线性影响的MLP
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631463
I. Abidi, Maha Cherif, M. Hizem, Iness Ahriz, R. Bouallègue
{"title":"A novel MLP based on compensation method for the effects of High Power Amplifier N onlinearities in Non-Linear SCMA systems","authors":"I. Abidi, Maha Cherif, M. Hizem, Iness Ahriz, R. Bouallègue","doi":"10.1109/ISCC53001.2021.9631463","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631463","url":null,"abstract":"As Sparse code multiple access (SCMA) has proved to be a fascinating research in order to meet the requirements of future wireless communication systems. To reach high power efficiency, wireless communication systems are equipped with high power amplifiers (HPAs). In this paper, we investigate the effects of distortions due to high power amplifiers (HPA) nonlinearities. We study the performance of amplified SCMA systems, in terms of bit error rate (BER). Message passing algorithm (MPA) is considered for SCMA detectors. BER performance is derived and evaluated for Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Numerical results and comparisons are provided for several system parameters, such as the input back-off (IBO). Indeed, we propose a new distortion cancellation technique based on feed-forwarded neural networks (FNNs) to restore the system performance via eliminating the HPA nonlinearities at transmitter and receiver sides. It is confirmed that the proposed pre-distorter and post-distorter with neural network exhibit a good performance improvement of quality of the transmission. Specifically, post-distortion based on NNs shows a better BER performance, which is almost close to the one of the linear system.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115311327","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}
引用次数: 0
Multi-Scale Relation Network for Person Re-identification 人物再识别的多尺度关系网络
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631515
Yi Ma, Tian Bai, Wenyu Zhang, Shuang Li, Jian Hu, Mingzhe Lu
{"title":"Multi-Scale Relation Network for Person Re-identification","authors":"Yi Ma, Tian Bai, Wenyu Zhang, Shuang Li, Jian Hu, Mingzhe Lu","doi":"10.1109/ISCC53001.2021.9631515","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631515","url":null,"abstract":"Person re-identification (reID) has received extensive study and achieved great progress in recent years. Extensive research has proved that combining global and local features is an effective solution to improve the performance of person reidentification tasks. While many existing reID approaches are still suffering from occlusions, body part missing, different lighting, and background clutter, where the learned features may achieve a sub-optimal solution. In this paper, we propose an efficient network structure, Multi-Scale Relation Network (MSRN), which can not only extract robust regional features and global features but also integrate the asymptotic cues and relations between them. In addition, we introduce a dynamic loss weight as supplementary components to improve learning efficiency and the representation capacity of our model. Extensive experiments are conducted on three widely used datasets, including Market-I501, DukeMTMC-ReID and CUHK03-NP. The experimental results indicate that our proposed method achieves the state-of-the-art results on three datasets.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132014501","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}
引用次数: 2
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