检索结果-内蒙古大学图书馆

19th IEEE-India-Council International Conference (INDICON)

作者： Maan, Jitendra Nagwekar, Riddhi Jayraj Tata Consultancy Serv EGG S&S Serv Gurgaon India Tata Consultancy Serv EGG S&S Serv Pune Maharashtra India

ISBN: (纸本)9781665473507

With the rapidly transforming digital world, various smart platforms/Post M&A Integration solutions are being utilized to accelerate the transition process of M&A activities. But the procedures include considering innumerable important factors such as finding out common methodologies, gap analysis, customized planning, redaction of documents, contract analysis etc., which need to be taken care of within relatively less time as industries are growing exponentially. In our study, we developed an ML-based solution which is intended to automate and improve the process of integration and planned post-merge activities. The paper presents a solution that focuses on identifying process synergies, reducing dependencies on human manual evaluation/analysis to reduce errors and also, keeps bird-eye view on overall process in order to track the status of activities to close. ML-powered Post M&A Integration solution helps to save time which is consumed in analysis like identifying gaps or similarities by comparing huge amounts of data, reviewing hundreds of checklists. We are maintaining accurate questionnaires for various domains at granular level considering all basic as well as trending sectors to evaluate synergies between firms by comparing their responses using COSINE similarity functionality of machine learning. The calculated score projects a checklist of items to tackle for filling the gaps by both organizations in the entire process to have smooth completion of M&A activities.

关键词： Mergers and acquisitions machine learning unsupervised algorithm Target Company Parent company deal integration unsupervised algorithm Integration Synergy Post Merger Integration

来源：评论

学校读者我要写书评

暂无评论

Attack Detection in Smart Home IoT Networks using CluStream and Page-Hinkley Test

Attack Detection in Smart Home IoT Networks using CluStream ...

引用

IEEE Latin-American Conference on Communications (LATINCOM)

作者： Nakagawa, Fernando H. Y. Barbon Junior, Sylvio Zarpelao, Bruno Bogaz Univ Estadual Londrina Dept Comp Sci Londrina Parana Brazil

ISBN: (纸本)9781665440356

The expansion of IoT device networks increases the demand for security systems that detect attacks against these new targets. These devices have simple hardware, limited memory and processing power, and often are required to have low energy consumption. Batch supervised learning algorithms have been employed to address this issue, but they present some limitations. These algorithms demand benign and malicious labeled samples to be trained, which can be hard to obtain in real networks. Also, once they are trained, it is hard to update the learning model with newly found behaviors. In this work, we propose an online and unsupervised scheme to detect attacks in smart home IoT networks. This scheme is based on the combination of two algorithms: CluStream and Page-Hinkley Test. It does not require labeled samples to be trained and learns incrementally as it is used. Tests were performed over data obtained from publicly available datasets consisting of multiple smart home devices and the results are encouraging. Different types of attacks were detected with an overall detection rate around 97%, while the precision stayed above 87%.

关键词： Internet of Things stream learning unsupervised algorithm attack detection

来源：评论

学校读者我要写书评

暂无评论

Dynamic real-time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

引用

APPLIED ENERGY 2022年 307卷 118314-118314页

作者： Yin, Sihua Yang, Haidong Xu, Kangkang Zhu, Chengjiu Zhang, Shaqing Liu, Guosheng Guangdong Univ Technol Guangdong Engn Res Ctr Green Mfg & Energy Efficie Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Sch Management Guangzhou 510006 Guangdong Peoples R China

Uncertain working conditions will lead to abnormal energy consumption and energy loss of high energy consumption machines. Therefore, it is necessary to detect abnormal energy consumption data, which is designed to optimize energy efficiency. In this study, two unsupervised anomaly detection methods are proposed, suitable for dynamic real-time abnormal energy consumption detection. As for point anomaly, a Rain Flow-based Connectivity Outlier Factor algorithm is proposed, which is a connectivity-based detection method. While, as for collective anomaly, a Rain Flow-based Mean Nearest Neighbor Distance Anomaly Factor algorithm is proposed, which is a distance-based detection method. The proposed two algorithms adopt the methods of time sorting and piecewise cubic Hermite interpolating polynomial, respectively. The aim is to accurately locate anomalies in the time dimension. Then, abnormal energy consumption is automatically found by setting a dynamic real-time threshold. To verify the superiority and the effectiveness of the proposed method, the actual energy consumption data from aluminum extrusion enterprises are used for simulation experiments. The results demonstrate that the proposed method can detect anomalies quickly and accurately. Finally, this study presents an energy efficiency optimization framework based on anomaly detection. Our tests in energy management systems show that the proposed framework can optimize energy efficiency and reduce energy waste. This provides an effective solution to clean production and low carbon manufacturing of high energy consumption machines.

关键词： High energy consumption machine Uncertainty Stream data Anomaly detection unsupervised algorithm Energy efficiency optimization

来源：评论

学校读者我要写书评

暂无评论

Cross Different Time Periods Detection algorithm based on YOLOv4 40

Cross Different Time Periods Detection Algorithm based on YO...

引用

40th Chinese Control Conference (CCC)

作者： Fan, Jiaqi Huo, Tianjiao Wei, Zhenqi Wang, Jiachen Li, Xin Gao, Bingzhao Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China

ISBN: (纸本)9789881563804

With the development of autonomous driving vehicle target detection technology, multi-target detection in complex environment has gradually become the focus of attention. In this paper, based on the YOLOv4 algorithm, the target detection problem is carried out on the images from different time periods in the training and test set. Firstly, we train the unsupervised CycleGAN model to get some synthetic images at night. Secondly, these synthetic night images combined with the images collected in the sunny weather are input to train the YOLOv4 detector and then the final model is used to detect the targets at night. The experimental results show that the detection accuracy of our model for car, person and traffic sign is improved by 3.65%, 4.29% and 5.42% respectively when compared to the baseline model, that is, only training the images from the daytime and testing the targets during the night. In addition, the recall values of these three targets are all increased by more than 5%, which indicates that our model reduces the missing detection rate. The above experimental results have fully demonstrated the effectiveness of the combination of supervised and unsupervised algorithm for target detection cross different time periods. And the proposed model greatly improves the safety of the autonomous driving vehicles, which is of great significance for target detection in the road.

关键词： YOLOv4 CycleGAN Cross Domain Detection Transfer Learning unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

YAKE! Keyword extraction from single documents using multiple local features

引用

INFORMATION SCIENCES 2020年 509卷 257-289页

作者： Campos, Ricardo Mangaravite, Vitor Pasquali, Arian Jorge, Alipio Nunes, Celia Jatowt, Adam Polytech Inst Tomar Ci2 Smart Cities Res Ctr Tomar Portugal Univ Porto FCUP Porto Portugal Univ Beira Interior Dept Math Covilha Portugal Kyoto Univ Grad Sch Informat Kyoto Japan INESC TEC INESC Technol & Sci LIAAD Porto Portugal Univ Beira Interior Ctr Math & Applicat Covilha Portugal Univ Fed Minas Gerais Belo Horizonte MG Brazil

As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains. (C) Elsevier Inc. All rights reserved.

关键词： Keyword extraction Information extraction unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2020年第2期12卷 117-131页

作者： El Aissaoui, Ouafae El Madani, Yasser El Alami Oughdir, Lahcen Dakkak, Ahmed El Allioui, Youssouf Sidi Mohamed Ben Abdellah Univ FPT LSI Fes Morocco Mohammed V Univ ENSIAS Rabat Morocco Hassan First Univ FPK LS3M Khouribga Morocco

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

关键词： unsupervised algorithm supervised algorithm K-means naive Bayes adaptive E-learning systems Felder-Silverman learning style model

来源：评论

学校读者我要写书评

暂无评论

Habituation as a neural algorithm for online odor discrimination

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2020年第22期117卷 12402-12410页

作者： Shen, Yang Dasgupta, Sanjoy Navlakha, Saket Cold Spring Harbor Lab Simons Ctr Quantitat Biol POB 100 Cold Spring Harbor NY 11724 USA Univ Calif San Diego Comp Sci & Engn Dept La Jolla CA 92093 USA

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

关键词： habituation odor discrimination background subtraction systems neuroscience unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

WSAD: An unsupervised Web Session Anomaly Detection Method 16

WSAD: An Unsupervised Web Session Anomaly Detection Method

引用

16th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Sun, Yizhen Xie, Yiman Wang, Weiping Zhang, Shigeng Gao, Jun Chen, Yating Hunan Elect Power Corp State Grid Informat & Commun Co Changsha Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Hunan Key Lab Internet Things Elect Changsha Peoples R China

ISBN: (纸本)9781728199160

Web servers in the Internet are vulnerable to Web attacks, to detect Web attacks, a commonly used method is to detect anomalies in the request parameters by making regular-expression-based matching rules for the parameters based on known security threats. However, such methods cannot detect unknown anomalies well and they can also be easily bypassed by using techniques like transcoding. Moreover, existing anomaly detection methods are usually based on a single HTTP request, which is easy to ignore the attack behavior within a period of time, such as brute-force password cracking attack. In this paper, we propose an unsupervised Web Session Anomaly Detection method called WSAD. WSAD uses ten features of web session to perform anomaly detection. After extracting the ten features, WSAD uses the DBSCAN algorithm to cluster the features of each session and outputs the outliers found in the clustering process as anomalies. We evaluate the performance of WSAD on several datasets from multiple real websites of a company. The results indicate that WSAD could detect malicious behaviors that could not be detected by Web Application Firewall, and it almost has no false positives.

关键词： Anomaly detection Web session unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

RPAD: An unsupervised HTTP Request Parameter Anomaly Detection Method 19

RPAD: An Unsupervised HTTP Request Parameter Anomaly Detecti...

引用

19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Sun, Yizhen Xie, Yiman Wang, Weiping Zhang, Shigeng Wu, Yuxi Feng, Jingchuan Hunan Elect Power Corp State Grid Informat & Commun Co Changsha Hunan Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Hunan Peoples R China Hunan Key Lab Internet Things Elect Changsha Hunan Peoples R China

ISBN: (纸本)9781665403924

Web servers in the Internet are vulnerable to Web attacks. A general way to launch Web attacks is to carry attack payloads in HTTP request parameters, e.g. SQL Injection and XSS attacks. To detect Web attacks, a commonly used method is to detect anomalies in the request parameters by making regular-expression-based matching rules for the parameters based on known security threats. However, such methods cannot detect unknown anomalies well and they can also be easily bypassed by using techniques like transcoding. Moreover, existing anomaly detection methods are usually based on supervised learning methods that require a large number of high-quality labelled samples as training sets, which are difficult to obtain in real situations. In this paper, we propose an unsupervised HTTP Request Parameter Anomaly Detection method called RPAD. RPAD uses five features of HTTP request parameters to perform anomaly detection including type, length, number of tokens, encoding type and character feature. After extracting the five features, RPAD uses the DBSCAN algorithm to cluster the parameters of each target access request and outputs the outliers found in the clustering process as anomalies. We evaluate the performance of RPAD on several datasets from multiple real websites of a Cyber Security Company. The results indicate that RPAD is highly efficient in detecting deviating abnormal parameter values with an accuracy of 99%.

关键词： Anomaly detection HTTP request parameter unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

Cross Different Time Periods Detection algorithm based on YOLOv4

Cross Different Time Periods Detection Algorithm based on YO...

引用

第40届中国控制会议

作者： Jiaqi Fan Tianjiao Huo Zhenqi Wei Jiachen Wang Xin Li Bingzhao Gao State Key Laboratory of Automotive Simulation and Control Jilin University College of Computer Science and Technology Jilin University Clean Energy Automotive Engineering Center Tongji University

With the development of autonomous driving vehicle target detection technology, multi-target detection in complex environment has gradually become the focus of attention. In this paper, based on the YOLOv4 algorithm, the target detection problem is carried out on the images from different time periods in the training and test set. Firstly, we train the unsupervised CycleGAN model to get some synthetic images at night. Secondly, these synthetic night images combined with the images collected in the sunny weather are input to train the YOLOv4 detector and then the final model is used to detect the targets at night. The experimental results show that the detection accuracy of our model for car, person and traffic sign is improved by 3.65%, 4.29% and 5.42% respectively when compared to the baseline model, that is, only training the images from the daytime and testing the targets during the night. In addition, the recall values of these three targets are all increased by more than 5%,which indicates that our model reduces the missing detection rate. The above experimental results have fully demonstrated the effectiveness of the combination of supervised and unsupervised algorithm for target detection cross different time periods. And the proposed model greatly improves the safety of the autonomous driving vehicles, which is of great significance for target detection in the road.

关键词： YOLOv4 CycleGAN Cross Domain Detection Transfer Learning unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：