2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第9页

Deep Learning Poison Data Attack Detection 深度学习有毒数据攻击检测

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00137

Henry Chacón, S. Silva, P. Rad

{"title":"Deep Learning Poison Data Attack Detection","authors":"Henry Chacón, S. Silva, P. Rad","doi":"10.1109/ICTAI.2019.00137","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00137","url":null,"abstract":"Deep neural networks are widely used in many walks of life. Techniques such as transfer learning enable neural networks pre-trained on certain tasks to be retrained for a new duty, often with much less data. Users have access to both pre-trained model parameters and model definitions along with testing data but have either limited access to training data or just a subset of it. This is risky for system-critical applications, where adversarial information can be maliciously included during the training phase to attack the system. Determining the existence and level of attack in a model is challenging. In this paper, we present evidence on how adversarially attacking training data increases the boundary of model parameters using as an example of a CNN model and the MNIST data set as a test. This expansion is due to new characteristics of the poisonous data that are added to the training data. Approaching the problem from the feature space learned by the network provides a relation between them and the possible parameters taken by the model on the training phase. An algorithm is proposed to determine if a given network was attacked in the training by comparing the boundaries of parameters distribution on intermediate layers of the model estimated by using the Maximum Entropy Principle and the Variational inference approach.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"623 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114424858","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: 19

Triplet Deep Hashing with Joint Supervised Loss for Fast Image Retrieval 基于联合监督损失的三重深度哈希快速图像检索

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00090

Mingyong Li, Hongya Wang, Liangliang Wang, Kaixiang Yang, Yingyuan Xiao

{"title":"Triplet Deep Hashing with Joint Supervised Loss for Fast Image Retrieval","authors":"Mingyong Li, Hongya Wang, Liangliang Wang, Kaixiang Yang, Yingyuan Xiao","doi":"10.1109/ICTAI.2019.00090","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00090","url":null,"abstract":"In recent years, the emerging hashing techniques have been successful in large-scale image retrieval. Due to its strong learning ability, deep hashing has become one of the most promising solutions and achieved good results in practice. However, existing deep hashing methods had some limitations, for example, most methods consider only one kind of supervised loss, which leads to insufficient utilization of supervised information. To address this issue, we proposed a Triplet Deep Hashing method with Joint supervised Loss based on convolution neural network (JLTDH) in this work. The proposed JLTDH method combine triplet likelihood loss and linear classification loss, moreover, the triplet supervised label is adopted, which contains richer supervised information than that of pointwise and pairwise label. At the same time, in order to overcome the cubic increase in the number of triplets and make triplet training more effective, we adopt a novel triplet selection method. The whole process is divided into two stages, in the first stage, taking the triplets generated by the triplet selection method as the input of CNN, the three CNNs with shared weights are used for image feature learning, the last layer of the network outputs a preliminary hash code. In the second stage, relying on the hash code of the first stage and the joint loss function, the neural network model is further optimized so that the generated hash code has higher query precision. We perform extensive experiments on three public benchmark datasets CIFAR-10, NUS-WIDE, and MS-COCO. Experimental results demonstrate that the proposed method outperforms the compared methods, the method is also superior to all previous deep hashing methods based on triplet label.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114906489","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

Towards a High-Level Multi-label Classification from Complex Networks 面向复杂网络的高级多标签分类

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00159

Vinícius H. Resende, M. Carneiro

{"title":"Towards a High-Level Multi-label Classification from Complex Networks","authors":"Vinícius H. Resende, M. Carneiro","doi":"10.1109/ICTAI.2019.00159","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00159","url":null,"abstract":"Multi-label learning aims to solve problems in which data items can have multiple class labels assigned simultaneously, e.g., text categorization, image annotation, medical diagnosis, etc. However, as most of multi-label techniques are derived from the single-label ones, existing techniques perform the multi-label classification only based on the physical features of the data (e.g., distance, similarity or distribution), ignoring the semantic meaning of the data, such as the formation pattern. Inspired by recent advances in the use of complex networks for single-label learning, this exploratory work aims to investigate a multi-label solution able to combine existing multi-label classifiers with a high-level classifier based on complex networks measures, aiming to present a new concept of multi-label classification that, besides the physical attributes, also analyzes the topological structure of the data. Experimental results considering both artificial and real-world data sets emphasize respectively the salient features of our technique in comparison to the traditional ones and its potential to improve the predictive performance of those techniques, especially in data sets characterized by higher cardinality and density of labels, which often denote more difficult scenarios to multi-label learning.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757607","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

Unsupervised Pedestrian Trajectory Prediction with Graph Neural Networks 基于图神经网络的无监督行人轨迹预测

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00119

Mingkun Wang, Dian-xi Shi, Naiyang Guan, Tao Zhang, Liujing Wang, Ruoxiang Li

{"title":"Unsupervised Pedestrian Trajectory Prediction with Graph Neural Networks","authors":"Mingkun Wang, Dian-xi Shi, Naiyang Guan, Tao Zhang, Liujing Wang, Ruoxiang Li","doi":"10.1109/ICTAI.2019.00119","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00119","url":null,"abstract":"Trajectory prediction can aid in target tracking, automatic system navigation, social behavior prediction, analysis and other computer vision tasks. When people walk in crowded spaces such as sidewalks, subway and airports, etc., they naturally adjust their walking style according to the scene context and follow common social etiquette, such as maintaining separation and avoiding collisions. Accurate prediction of trajectories is a big challenge in a crowded scenario where interaction between targets may cause complex societal dynamics. Unlike the prediction of a single person trajectory, it is difficult to capture the real motion of multiple people by only considering the historical positions of each individual separately. Benefit from the recent success of graph neural networks, we propose a model called GNN-TP for pedestrian trajectory prediction. GNN-TP is a purely data-driven model that simultaneously infers the interactions between pedestrians in an unsupervised way and predicts their future trajectories jointly in crowded scenes. On the one hand, GNN-TP infers the interactions employing the observed historical trajectories. We transfer pedestrians' information on the graph-structured data and classify the interaction type based on edges' features. On the other hand, it learns the dynamical model and predicts future trajectories based on the inferred interactions and the observations. Extensive experiments show that our trajectory prediction model achieves efficient and state-of-the-art performance on several public datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124135123","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: 8

Combining Constraint Languages via Abstract Interpretation 通过抽象解释组合约束语言

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00016

Pierre Talbot, D. Cachera, É. Monfroy, C. Truchet

引用次数: 2

Internet of Things Security Analytics and Solutions with Deep Learning 物联网安全分析与深度学习解决方案

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00033

Luke Holbrook, M. Alamaniotis

{"title":"Internet of Things Security Analytics and Solutions with Deep Learning","authors":"Luke Holbrook, M. Alamaniotis","doi":"10.1109/ICTAI.2019.00033","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00033","url":null,"abstract":"This study presents a new solution applied to defending networks of Internet of Things (IoT) devices. It aims at providing a comprehensive solution to defending the IoT and establishing a protocol for IoT security. Recent attacks that compromised over 120 million devices highlighted the need for enhancing IoT security. This paper introduces the adoption of deep learning for critical security applications by utilizing snapshots of network traffic from nine real-world IoT devices. Furthermore, a set of tools, and in particular, Support Vector Machines (SVM), Random Forest and Deep Neural Network (DNN) algorithms are tested and compared against one another to determine which is the most deployable and provide the highest accuracy of anomaly detection. The obtained results exhibited that all three tested algorithms provided high accuracy. However, the deep neural network provides the highest coefficient of determination compared to the other tested models, making DNN more suitable for this type of applications. Finally, the DNN's learning autonomy feature allows omission of humans from the loop resulting in time efficient real-world algorithm.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793977","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

Crowd Counting via Enhanced Feature Channel Convolutional Neural Network 基于增强特征通道卷积神经网络的人群计数

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00118

Yinlong Bian, Jiehong Shen, Xin Xiong, Ying Li, Wei-Ji He, Peng Li

{"title":"Crowd Counting via Enhanced Feature Channel Convolutional Neural Network","authors":"Yinlong Bian, Jiehong Shen, Xin Xiong, Ying Li, Wei-Ji He, Peng Li","doi":"10.1109/ICTAI.2019.00118","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00118","url":null,"abstract":"Accurate crowd counting is important for interpreting and understanding crowd, which has great practical significance in video monitoring, public safety, urban planning, the construction of intelligent shopping malls and so on. For accurate counting, many excellent algorithms have been proposed, but there are still some challenges in terms of scale variation, occlusion, inaccurate counting in various backgrounds and so forth. In this paper, we propose a new model EFCCNN (Enhanced Feature Channel Convolutional Neural Network) to deal with these challenges. The proposed EFCCNN model has three main contributions. We propose a new convolutional neural network, which can be trained by end-to-end, and it performs better than other crowd counting networks. Additionally, we use SENet (Squeeze-and-Excitation Network) structure to change the channel weight, which can enhance the significant channel, and we use residual structure to transmit the channel weight to improve the counting precision. The SENet structure is helpful to solve the problem of scale variation and occlusion. The EFCCNN model is the first crowd counting model using channel weight information. Furthermore, a new loss function focusing on the structural information of images is proposed, which reduces the mean absolute error of crowd counting, effectively solves the problem of inaccurate counting in various backgrounds, such as crowd miscounting in the tree and brush background, and improves the quality of the crowd density map on SSIM (Structural Similarity Index Measure). Experiments on ShanghaiTech, Mall, UCF_CC_50 dataset show that EFCCNN have a lower mean absolute error of crowd counting and a higher quality density map.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131487932","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

Estimating Vehicle Speed on Highway Roads from Smartphone Sensors Using Deep Learning Models 使用深度学习模型从智能手机传感器估计高速公路上的车辆速度

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00138

Norhan Elsayed Amer Abdelgawad, A. El-Mahdy, W. Gomaa, A. Shoukry

{"title":"Estimating Vehicle Speed on Highway Roads from Smartphone Sensors Using Deep Learning Models","authors":"Norhan Elsayed Amer Abdelgawad, A. El-Mahdy, W. Gomaa, A. Shoukry","doi":"10.1109/ICTAI.2019.00138","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00138","url":null,"abstract":"Speed estimation is an open research area because of its importance in many applications and the necessity of replacing GPS due to smartphones battery drainage. Relying on integrating accelerometer values is challenging and generally requires continual utilization of external references to correct speed, because of error accumulation. Therefore, highway speed estimation, in particular, is a difficult problem due to the maintenance of high speeds by vehicles for a long time and the scarcity of reference points like uneven road surfaces, turns, and stops. In this paper, we investigate exploiting micro road surface unevenness that results in vibrations on the accelerometer readings without integrating the acceleration signal. In particular, we employ deep 1D convolutional neural networks to learn and extract robust features that learn the relation between such complex vibrations and speed. Also, the use of bidirectional LSTMs is investigated to benefit from both forward and backward dependencies in the sensed data, and allow a form of integration. Specifically, two highway speed estimation models are proposed. The first uses a deep convolutional neural network with 5 layers and the second uses a deep bidirectional LSTM neural network. The inputs to both networks are the readings from the accelerometer and gyroscope sensors of a smartphone. The methods achieved mean absolute error results of 5.53 km/hr and 3.71 km/hr, respectively; whereas a related LSTM based method, resulted in a high error rate of 68.05 km/hr. Finally, an implementation of the proposed CNN model on an android and iOS smartphones is described and analyzed.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130010989","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

AudioMask: Robust Sound Event Detection Using Mask R-CNN and Frame-Level Classifier AudioMask:使用掩码R-CNN和帧级分类器的鲁棒声音事件检测

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00074

Alireza Nasiri, Yuxin Cui, Zhonghao Liu, Jing Jin, Yong Zhao, Jianjun Hu

{"title":"AudioMask: Robust Sound Event Detection Using Mask R-CNN and Frame-Level Classifier","authors":"Alireza Nasiri, Yuxin Cui, Zhonghao Liu, Jing Jin, Yong Zhao, Jianjun Hu","doi":"10.1109/ICTAI.2019.00074","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00074","url":null,"abstract":"Deep learning methods have recently made significant contributions to sound event detection. These methods either use a block-level approach to distinguish parts of audio containing the event, or analyze the small frames of the audio separately. In this paper, we introduce a new method, AudioMask, for rare sound event detection by combining these two approaches. AudioMask first applies Mask R-CNN, a state-of-the-art algorithm for detecting objects in images, to the log mel-spectrogram of the audio files. Mask R-CNN detects audio segments that might contain the target event by generating bounding boxes around them in time-frequency domain. Then we use a frame-based audio event classifier trained independently from Mask R-CNN, to analyze each individual frame in the candidate segments proposed by Mask R-CNN. A post-processing step combines the outputs of the Mask R-CNN and the frame-level classifier to identify the true events. By evaluating AudioMask over the data sets from 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge Task 2, We show that our algorithm performs better than the baseline models by 13.3% in the average F-score and achieves better results compared to the other non-ensemble methods in the challenge.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"38 8 Pt 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737218","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

Learning Sparse Support Vector Machine with Relaxation and Rounding 基于松弛和舍入的稀疏支持向量机学习

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00139

Xiangyu Tian, Shizhong Liao

{"title":"Learning Sparse Support Vector Machine with Relaxation and Rounding","authors":"Xiangyu Tian, Shizhong Liao","doi":"10.1109/ICTAI.2019.00139","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00139","url":null,"abstract":"A sparse representation of Support Vector Machines (sparse SVMs) is desirable for many applications. However, for large-scale problems with high-dimensional features solving sparse SVMs remains a challenging problem, and most of the existing work are heuristic in that there are no performance guarantees and can't effectively control the trade-off between the sparsity and the accuracy of the decision hyperplane. To address this issue, we propose a new method for via relaxation and rounding, which obtains (ε,δ-approximate solution in Õ(n/εδ) time with probability at least 1-δ. Such regularization explicitly penalizes parameters different from zero with no further restrictions. We first show that learning sparse SVMs with ℓ_0 norm can be reformulated as an exactly Boolean program by introducing Boolean variables to each parameter. With dual and Boolean relaxation, this Boolean problem can be relaxed as a convex programming. For the ε-approximate solution of this convex programming, we get a feasible solution of the original problem without loss accuracy by a determined rounding. We analyze the proposed method in details and give a provable guarantee which is missing from the previous work. Experimental results on both synthetic data and real world data support our theoretical results and verify the validity of the proposed method.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130640418","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