2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献

{"title":"EM-RITE 2019 Workshop Organizing Committee","authors":"","doi":"10.1109/iri.2019.00010","DOIUrl":"https://doi.org/10.1109/iri.2019.00010","url":null,"abstract":"","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"10 2-3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121015188","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}

{"title":"A Non-Cooperative Game Model for Overlapping Community Detection in Social Networks","authors":"Huan Yang, Zhan Bu, Yuyao Wang, Xi Xiong, Chengcui Zhang","doi":"10.1109/IRI.2019.00055","DOIUrl":"https://doi.org/10.1109/IRI.2019.00055","url":null,"abstract":"Due to its broad real-life application, overlapping community detection (in the realm of a social network) has attracted considerable interests from many researchers. However, current methods fail to reveal the full community structure and its formation process. Thus, here we present an effective and scalable overlapping community detection algorithm, which formulates the target problem as a non-cooperative game played by multiple social actors (players). Specifically, we allow each player to join multiple communities, and the strategy of each player is denoted by a community membership vector. The adopted utility function in our game integrates both the high-order proximity and the similarity between membership vectors of social actors. By properly weighting and rewiring the original social network, our approach can nicely enhance the global community structure by incorporating higher-order node proximities. Moreover, we formally prove that the proposed game resembles and matches how a potential game works (in the classical sense in game theory), indicating that the Nash equilibrium point must exist. To find such Nash equilibrium point, we use a stochastic gradientascent method to update the community membership vectors of players. Extensive experiments are conducted on both synthetic and real-world social networks. After comparing our method with six baseline methods, we obtain convincing results in terms of how well the methods reveal communities, as well as its scalability.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675027","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}

{"title":"A Trust Aware Unsupervised Learning Approach for Insider Threat Detection","authors":"Maryam Aldairi, Leila Karimi, J. Joshi","doi":"10.1109/IRI.2019.00027","DOIUrl":"https://doi.org/10.1109/IRI.2019.00027","url":null,"abstract":"With the rapidly increasing connectivity in cyberspace, Insider Threat is becoming a huge concern. Insider threat detection from system logs poses a tremendous challenge for human analysts. Analyzing log files of an organization is a key component of an insider threat detection and mitigation program. Emerging machine learning approaches show tremendous potential for performing complex and challenging data analysis tasks that would benefit the next generation of insider threat detection systems. However, with huge sets of heterogeneous data to analyze, applying machine learning techniques effectively and efficiently to such a complex problem is not straightforward. In this paper, we extract a concise set of features from the system logs while trying to prevent loss of meaningful information and providing accurate and actionable intelligence. We investigate two unsupervised anomaly detection algorithms for insider threat detection and draw a comparison between different structures of the system logs including daily dataset and periodically aggregated one. We use the generated anomaly score from the previous cycle as the trust score of each user fed to the next period's model and show its importance and impact in detecting insiders. Furthermore, we consider the psychometric score of users in our model and check its effectiveness in predicting insiders. As far as we know, our model is the first one to take the psychometric score of users into consideration for insider threat detection. Finally, we evaluate our proposed approach on CERT insider threat dataset (v4.2) and show how it outperforms previous approaches.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131646044","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}

{"title":"Towards Federated Learning Approach to Determine Data Relevance in Big Data","authors":"Ronald Doku, D. Rawat, Chunmei Liu","doi":"10.1109/IRI.2019.00039","DOIUrl":"https://doi.org/10.1109/IRI.2019.00039","url":null,"abstract":"In the past few years, data has proliferated to astronomical proportions; as a result, big data has become the driving force behind the growth of many machine learning innovations. However, the incessant generation of data in the information age poses a needle in the haystack problem, where it has become challenging to determine useful data from a heap of irrelevant ones. This has resulted in a quality over quantity issue in data science where a lot of data is being generated, but the majority of it is irrelevant. Furthermore, most of the data and the resources needed to effectively train machine learning models are owned by major tech companies, resulting in a centralization problem. As such, federated learning seeks to transform how machine learning models are trained by adopting a distributed machine learning approach. Another promising technology is the blockchain, whose immutable nature ensures data integrity. By combining the blockchain's trust mechanism and federated learning's ability to disrupt data centralization, we propose an approach that determines relevant data and stores the data in a decentralized manner.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132538036","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}

{"title":"A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data","authors":"Tawfiq Hasanin, T. Khoshgoftaar, Joffrey L. Leevy","doi":"10.1109/IRI.2019.00026","DOIUrl":"https://doi.org/10.1109/IRI.2019.00026","url":null,"abstract":"Severe class imbalance between the majority and minority classes in large datasets can prejudice Machine Learning classifiers toward the majority class. Our work uniquely consolidates two case studies, each utilizing three learners implemented within an Apache Spark framework, six sampling methods, and five sampling distribution ratios to analyze the effect of severe class imbalance on big data analytics. We use three performance metrics to evaluate this study: Area Under the Receiver Operating Characteristic Curve, Area Under the Precision-Recall Curve, and Geometric Mean. In the first case study, models were trained on one dataset (POST) and tested on another (SlowlorisBig). In the second case study, the training and testing dataset roles were switched. Our comparison of performance metrics shows that Area Under the Precision-Recall Curve and Geometric Mean are sensitive to changes in the sampling distribution ratio, whereas Area Under the Receiver Operating Characteristic Curve is relatively unaffected. In addition, we demonstrate that when comparing sampling methods, borderline-SMOTE2 outperforms the other methods in the first case study, and Random Undersampling is the top performer in the second case study.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125064041","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}

{"title":"IRI 2019 Panel II","authors":"B. Thuraisingham","doi":"10.1109/iri.2019.00014","DOIUrl":"https://doi.org/10.1109/iri.2019.00014","url":null,"abstract":"Artificial Intelligence (AI) emerged as a field of study in Computer Science in the late 1950s. Researchers were interested in designing and developing systems that could behave like humans. This interest resulted in substantial developments in areas such as expert systems, machine learning, planning systems, reasoning systems and robotics. However, it is only recently that these AI systems are being used in practical applications in various fields such as medicine, finance, marketing, defense, and manufacturing. The main reason behind the success of these AI systems is due to the developments in data science and high-performance computing. For example, it is now possible collect, store, manipulate, analyze and retain massive amounts of data and therefore the AI systems are now able to learn patterns from this data and make useful predictions. While AI has been evolving as a field during the past sixty years, the developments in computing systems and data management systems have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be identified. Furthermore, the computing systems are being attacked by malware resulting in disastrous consequences. In order words, as progress is being made with technology, the security of these technologies is in serious question due to the malicious attacks. Over the decade. AI and Security are being integrated. For example, machine learning techniques are being applied to solve security problems such as malware analysis, intrusion detection and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine leading techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. Furthermore, while collecting massive amounts of data causes security and privacy concerns, big data analytics applications in cyber security is exploding. For example, an organization can","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345099","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}

{"title":"A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices","authors":"Y. Tu, Saad Sadiq, Yudong Tao, M. Shyu, Shu‐Ching Chen","doi":"10.1109/IRI.2019.00040","DOIUrl":"https://doi.org/10.1109/IRI.2019.00040","url":null,"abstract":"Deep neural networks (DNNs) have seen tremendous industrial successes in various applications, including image recognition, machine translation, audio processing, etc. However, they require massive amounts of computations and take a lot of time to process. This quickly becomes a problem in mobile and handheld devices where real-time multimedia applications such as face detection, disaster management, and CCTV require lightweight, fast, and effective computing solutions. The objective of this project is to utilize specialized devices such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) in a heterogeneous computing environment to accelerate the deep learning computations with the constraints of power efficiency. We investigate an efficient DNN implementation and make use of FPGA for fully-connected layer and GPU for floating-point operations. This requires the deep neural network architecture to be implemented in a model parallelism system where the DNN model is broken down and processed in a distributed fashion. The proposed heterogeneous framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127117725","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}

{"title":"DAUB 2019 Workshop Organizing Committee","authors":"","doi":"10.1109/iri.2019.00011","DOIUrl":"https://doi.org/10.1109/iri.2019.00011","url":null,"abstract":"","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129146814","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}

{"title":"A Framework for the Analysis of Biomechanical Loading Using Human Motion Tracking","authors":"Jan P. Vox, F. Wallhoff","doi":"10.1109/IRI.2019.00020","DOIUrl":"https://doi.org/10.1109/IRI.2019.00020","url":null,"abstract":"In this work, a feedback system for biomechanical load analysis based on joint angles and execution duration of recognized motions is described. For automatic analysis, the system must be set up by experts for the respective application case. The system can be trained individually to recognize motion sequences and parameterized with critical joint angle ranges and times for the evaluation. The system processes Cartesian joint positions, which can be captured by different types of motion tracking systems. The data processing steps filtering, normalization, feature extraction, classification, and segmentation are described. For classification, a Support Vector Machine with polynomial kernel is used that achieves a recognition accuracy up to 87% for 19 different gymnastic motions. In conclusion, a system with an associated user interface is shown, which is able to assist and analyze the user in motion sequences.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129839312","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}

{"title":"DIM 2019 Workshop Organizing Committee","authors":"","doi":"10.1109/iri.2019.00009","DOIUrl":"https://doi.org/10.1109/iri.2019.00009","url":null,"abstract":"","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"47 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123767747","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}