2022 14th International Conference on Machine Learning and Computing (ICMLC)最新文献_第5页

Unsupervised Anomaly Detection by Autoencoder with Feature Decomposition 基于特征分解的自编码器无监督异常检测

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529924

Yihao Guo, Xinning Zhu, Zheng Hu, Zhiqiang Zhan

{"title":"Unsupervised Anomaly Detection by Autoencoder with Feature Decomposition","authors":"Yihao Guo, Xinning Zhu, Zheng Hu, Zhiqiang Zhan","doi":"10.1145/3529836.3529924","DOIUrl":"https://doi.org/10.1145/3529836.3529924","url":null,"abstract":"In unsupervised anomaly detection tasks, a crucial challenge is modeling the underlying structure of normal data without knowing the definition or ratio of anomalies. The introduction of robustness against anomalous data in autoencoder architecture is a significant research focus in order to address this challenge. In this paper, we propose a model implemented by an autoencoder with two decoders, called Feature Decomposition AutoEncoder (FDAE). It maps all data into a high-dimensional latent feature space. Many studies have proved RSR technology and RPCA technology to improve the performance of anomaly detection models. FDAE employs RSR and RPCA techniques in the latent space to decompose latent features into normal features and abnormal features, then decodes them separately using two decoders. Furthermore, we design an optimization strategy to enable FDAE to prioritize modeling the underlying structure of normal data from unlabeled data to reduce the interference caused by unknown anomalous data. We demonstrate the high performance of FDAE in unsupervised anomaly detection tasks through experiments on five public datasets. In addition, we study the variation of FDAE’s anomaly detection capability under different noise scenarios on the MNIST dataset.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114775239","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

Label Aggregation with Clustering for Biased Crowdsourced Labeling 带有偏见的众包标签聚类的标签聚合

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529861

Ming Wu, Qianmu Li, Jing Zhang, J. Hou

{"title":"Label Aggregation with Clustering for Biased Crowdsourced Labeling","authors":"Ming Wu, Qianmu Li, Jing Zhang, J. Hou","doi":"10.1145/3529836.3529861","DOIUrl":"https://doi.org/10.1145/3529836.3529861","url":null,"abstract":"With the rapid development of crowdsourcing learning, amount of label aggregation methods are proposed to infer the true labels of instances from multiple noisy labels provided by inexpert crowd workers. Most of the label aggregation methods take the reliabilities of workers and the difficulties of instances into account and construct the probabilistic models, then infer the aggregated label and estimate the parameters simultaneously. However, to the best of our knowledge, label aggregation for biased crowdsourced labeling scenarios has not been sufficiently studied. Biased labeling is a critical factor that affects the performance of label aggregation and is hard to detect and model. To this end, this paper proposes a novel Label Aggregation with Clustering method for Biased Labeling (LACBL), to improve the quality of crowd labels by mitigating the labeling bias. LACBL detects the labeling bias of the dataset using clustering methods and then decreases the ratio of the biased class labels according to the bias. Finally, a label aggregation method is applied to the renewed label set. Experimental results on four real-world datasets show that LACBL outperforms other state-of-the-art label aggregation algorithms.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116034177","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

Mixed network sentiment analysis combining sentiment features and multiple attention 结合情感特征和多重关注的混合网络情感分析

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529903

Siqi Zhan, Donghong Qin, Zhizhan Xu

引用次数: 0

Multi-View Federated Learning with Data Collaboration 数据协作的多视图联邦学习

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529904

Yitao Yang, Xiucai Ye, Tetsuya Sakurai

{"title":"Multi-View Federated Learning with Data Collaboration","authors":"Yitao Yang, Xiucai Ye, Tetsuya Sakurai","doi":"10.1145/3529836.3529904","DOIUrl":"https://doi.org/10.1145/3529836.3529904","url":null,"abstract":"Under the privacy protection policy, federated learning has received more and more attention. Vertical federated learning (VFL) uses the same samples local in different parties to build prediction model. However, the same samples (overlapping samples) may be limited, while a large number of non-overlapping samples in each party are not utilized. If the non-overlapping samples can be utilized for training, it can benefit the prediction model. In this paper, we propose a novel VFL method, called Multi-View Federated Learning with Data collaboration (FedMC), to solve the problem of insufficient overlapping samples by exploiting suitable non-overlapping samples for data training. The proposed FedMC method first constructs a common feature space based on the overlapping samples, then projects the non-overlapping samples into the common feature space. We measure the similarity for each pair of the non-overlapping samples by calculating their distance in this space. When the distance is less than a threshold, we match them and add this pair to the overlapping samples. The expanded overlapping samples are finally used for training to build the prediction model. We evaluate the proposed method on real-world datasets. The experimental results show that the proposed method can improve the classification result by exploiting the non-overlapping samples for training.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124826085","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: 5

The Title of the Paper: Research on insurance marketing application based on hash link-table improved association rule algorithm 论文题目:基于哈希链表改进关联规则算法的保险营销应用研究

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529840

Xianmei He, Shaohua Teng

引用次数: 0

Pseudo Reward and Action Importance Classification for Sparse Reward Problem 稀疏奖励问题的伪奖励与动作重要性分类

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529918

Qingtong Wu, Dawei Feng, Yuanzhao Zhai, Bo Ding, Jie Luo

引用次数: 0

Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda 开发一种基于TinyML的四室冰箱(TBFCR)，用于有效储存药品:案例研究:卢旺达的药房

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529932

Joseph Habiyaremye, M. Zennaro, C. Mikeka, Emmanuel Masabo

{"title":"Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda","authors":"Joseph Habiyaremye, M. Zennaro, C. Mikeka, Emmanuel Masabo","doi":"10.1145/3529836.3529932","DOIUrl":"https://doi.org/10.1145/3529836.3529932","url":null,"abstract":"Medical products are very sensitive to temperature; the improper temperature may lead to their inefficacity. Apart from products that are stored at room temperature, remaining medical products are stored in electronically controlled refrigerators. A lot of researchers have proposed different refrigeration systems controlled with the help of the internet of things (IoT). Due to some issues such as storage capacity, computing energy, and computing speed, data processing in IoT-based applications is generally done at the cloud through cloud computing technology. Those applications are suffering issues like latency, data control, internet connectivity, network traffic, and operation cost. In this paper, we are experimentally developing a four rooms fridge controlled with an Arduino board that embeds a machine learning (ML) algorithm to control the temperature for efficient storage of medical products. We tried to develop an ML model that will monitor the closing and opening of the fridge door (while taking some medicines), predict and display the remaining time for the internal temperature to go beyond the acceptable temperature range. The result from our experiments shows that the model runs onto the controller and can predict well the internal fridge temperature at an accuracy of 96%.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125496100","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

ADFNet: Attention-based Fusion Network for Few-shot RGB-D Semantic Segmentation ADFNet:基于注意力的少镜头RGB-D语义分割融合网络

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529864

Chengkai Zhang, Jichao Jiao, Weizhuo Xu, Ning Li, Mingliang Pang, Jianye Dong

{"title":"ADFNet: Attention-based Fusion Network for Few-shot RGB-D Semantic Segmentation","authors":"Chengkai Zhang, Jichao Jiao, Weizhuo Xu, Ning Li, Mingliang Pang, Jianye Dong","doi":"10.1145/3529836.3529864","DOIUrl":"https://doi.org/10.1145/3529836.3529864","url":null,"abstract":"∗Deep CNNs have made great progress in image semantic segmentation. However, they require a large-scale labeled image dataset, which might be costly. Moreover, the model can hardly generalize to unseen classes. Few-shot segmentation, which can learn to perform segmentation on new classes from a few labeled samples, has been developed recently to tackle the problem. In this paper, we proposed a novel prototype network to undertake the challenging task of few-shot semantic segmentation on complex scenes with RGB-D datasets, which is named ADFNet (Attention-based Depth Fusion Network). Our ADFNet learns class-specific prototypes from both RGB channels and depth channels. Meanwhile, we proposed an attention-based fusion module to fuse the depth feature into the image feature that can better utilize the information of the support depth images. We also proposed RELIEF-prototype which refines the prototype and provides an additional improvement to the model. Furthermore, we proposed a new few-shot RGB-D segmentation benchmark based on SUN RGB-D, named SUN RGB-D-5i. Experiments on SUN RGB-D-5i show that our method achieves the mIoU score of 27.4% and 34.6% for 1-shot and 5-shot settings respectively, outperforming the baseline method by 4.2% and 4.4% respectively.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128717674","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

Simulated Annealing Algorithm Based on Single-direction Greedy Decoding for Solving Corridor Allocation Problem 基于单向贪婪解码的模拟退火算法求解走廊分配问题

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529848

Yufan Zhou, Xinhua Yang, Ailing Shen, Juan Lin, Yiwen Zhong

{"title":"Simulated Annealing Algorithm Based on Single-direction Greedy Decoding for Solving Corridor Allocation Problem","authors":"Yufan Zhou, Xinhua Yang, Ailing Shen, Juan Lin, Yiwen Zhong","doi":"10.1145/3529836.3529848","DOIUrl":"https://doi.org/10.1145/3529836.3529848","url":null,"abstract":"The Corridor Allocation Problem (CAP) is an NP-hard combinatorial optimization problem which aims to find the optimal layout of facilities on both side of a corridor, so as to minimize the flow cost between all pairs of facilities. Most existing metaheuristics use permutation of facilities to represent a solution, then a decoding strategy is used to map the solution representation into a layout. The decoding strategies used by those metaheuristics may lead to inconsistence between solution representation and the corresponding layout. For example, two facilities, which are far apart from each other in the representation, may become adjacent to each other in the layout. This inconsistence may affect the performance of metaheuristic. To overcome this shortage, this paper presents a Single-direction Greedy Decoding (SGD) strategy to map a permutation-based solution representation into a layout. Using the SGD strategy, a Hybrid Simulated Annealing (HSA) is proposed for solving the CAP. In HSA, a hybrid neighborhood structure is designed to produce candidate solutions. The HSA algorithm is experimentally analyzed on 23 benchmark instances with up to 70 facilities. Experimental results confirm the advantage of the SGD strategy and the hybrid neighborhood structure. Furthermore, the HSA algorithm found new optimal solutions on 13 instances.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124515954","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

Lucas-Kanade-based Face Detection Network: An Unsupervised Approach to Improve the Precision and Stability of video-based Face Detector 基于lucas - kanade的人脸检测网络:一种提高视频人脸检测精度和稳定性的无监督方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529839

Chang-Lin Li, Shuai Dong, Kun Zou, Wensheng Li

{"title":"Lucas-Kanade-based Face Detection Network: An Unsupervised Approach to Improve the Precision and Stability of video-based Face Detector","authors":"Chang-Lin Li, Shuai Dong, Kun Zou, Wensheng Li","doi":"10.1145/3529836.3529839","DOIUrl":"https://doi.org/10.1145/3529836.3529839","url":null,"abstract":"Though significant works have been made in image-based face detection, precise and stable video-based face detection remains a big challenge. What's more, the jittering of bounding boxes has an important impact on the stability of face attributes analysis. To address these issues, this paper presents an unsupervised single-stage face detector, named Lucas-Kanade-based face detection network (LK-FDN). Our key idea is that the detections of the same landmark in adjacent frames should be coherent with optical flow registration. By combining RetinaFace and the optical flow algorithm in a multi-task learning framework, LK-FDN does not require any video-level annotation, because the coherency of optical flow is a source of supervision and can be leveraged during detector training. Essentially, LK-FDN augments the training loss function with a registration loss. Supervised by the registration loss, the retraining process is conducted, which computes optical flow registration in the forward pass, and back-propagates gradients that ensure temporal coherency in the detector. The output of LK-FDN is a more precise and stable video-based face detector. Specifically, the advantages of LK-FDN are summarized in the following three aspects: (1) Compared with RetinaFace(baseline), LK-FDN improves the average precision (AP) by 0.43%, 0.23%, and 4.1% respectively on the easy, medium, and hard group of WIDER FACE. (2) Compared with RetinaFace, LK-FDN improves the precision without spending any extra inference time in test time, because LK is conducted during the retraining backward process. (3) LK-FDN significantly shrinks the jittering in video detection on the self-built ElevatorFace dataset without any video-level annotation. (4) According to the self-built evaluation criterion, the score of stability is improved from 88.409 to 97.119, which verifies the effectiveness of the LK-FDN.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126610279","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