检索结果-内蒙古大学图书馆

5th Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Amini, Hessam Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9781959429050

Although attention weights have been commonly used as a means to provide explanations for deep learning models, the approach has been widely criticized due to its lack of faithfulness. In this work, we present a simple approach to compute the newly proposed metric AtteFa, which can quantitatively represent the degree of faithfulness of the attention weights. Using this metric, we further validate the effect of the frequency of informative input elements and the use of contextual vs. non-contextual encoders on the faithfulness of the attention mechanism. Finally, we apply the approach on several real-life binary classification datasets to measure the faithfulness of attention weights in real-life settings. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Dynamic Synergy: Leveraging RIS and Reinforcement Learning for Secure, Adaptive Underlay Cognitive Radio Networks

Dynamic Synergy: Leveraging RIS and Reinforcement Learning f...

引用

International Global Information Infrastructure Symposium (GIIS)

作者： Deemah H. Tashman Soumaya Cherkaoui Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

Reconfigurable intelligent surfaces (RISs) have recently been employed to facilitate communication and improve performance by reflecting signals through configuring phase shifts toward the intended destination. This article examines the physical layer security of an underlay cognitive radio network aided by an RIS and in the presence of multiple eavesdroppers. The research is conducted under practical conditions, encompassing RIS hardware constraints and cascaded fading channels. An optimization problem is proposed with the objective of maximizing the secrecy rate of secondary users by optimizing the reflection angles of the RIS and the transmission power of the secondary user transmitter. A deep reinforcement learning method, specifically the soft actor-critic, is presented as a solution. The results section demonstrates the effect of altering the number of RIS elements on security. We also analyze the impact of hardware limitations and cascade levels on the secrecy rate. The effect of varying the number of eavesdroppers and the maximum permissible transmission power are also examined.

关键词： Fading channels Radio transmitters Reconfigurable intelligent surfaces Physical layer security Deep reinforcement learning Hardware Reflection Cognitive radio Security Optimization

来源：评论

学校读者我要写书评

暂无评论

Model Predictive Control for Automated Network Assurance in Intent-Based Networking enabled Service Function Chains 36

Model Predictive Control for Automated Network Assurance in ...

引用

36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Athanasopoulos, Nikolaos Lambadaris, Ioannis Falkner, Matthias Carleton University Department of Systems and Computer Engineering Ottawa Canada Ácole de Technologie Supérieure Department of Software and It Engineering Montreal Canada Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Cisco Systems Ottawa Canada

ISBN: (纸本)9781665477161

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications' requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations. © 2023 IEEE.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Addressing Different Goal Selection Strategies In Hindsight Experience Replay With Actor-Critic Methods For Robotic Hand Manipulation 2

Addressing Different Goal Selection Strategies In Hindsight ...

引用

2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Shams, Ayman Fevens, Thomas Concordia University Department of Computer Science and Software Engineering Montréal Canada

ISBN: (纸本)9781665459440

One of the most challenging problems in reinforcement learning is dealing with minimal rewards obtained from an environment. We present a combined technique of Twin Delayed Deep Deterministic Policy Gradient known as TD3, an off-policy Reinforcement Learning algorithm with Hindsight Experience Replay (HER). This combined technique allows for sampleefficient learning from sparse and binary rewards and avoids the need for complicated reward engineering. We use the challenge of moving things with a robotic arm to illustrate our methodology. We specifically tested six different tasks: pushing, sliding, picking up and placing in the Fetch environment, as well as manipulating a block, an egg, or a pen with our hands. We solely use binary rewards every time to indicate whether or not a task has been performed. In a comparative study, we primarily concentrate on the impact of various goal selection strategies of HER replay butter on both DDPG and TD3. We discovered that HER was crucial in enabling training in these demanding situations. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning

引用

Tsinghua Science and Technology 2022年第2期27卷 244-256页

作者： Kainan Zhang Zhi Tian Zhipeng Cai Daehee Seo the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA the Department of Electrical and Computer Engineering George Mason UniversityFairfaxVA 22030USA the National Center of Excellence in Software Sangmyung UniversitySeoul 03016Republic of Korea

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data *** social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： graph embedding privacy preservation adversarial learning

来源：评论

学校读者我要写书评

暂无评论

Empirical Investigation of role of Meta-learning approaches for the Improvement of software Development Process via software Fault Prediction 22

Empirical Investigation of role of Meta-learning approaches ...

引用

26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Hussain, Shahid Ibrahim, Naseem Department of Computer Science and Software Engineering Penn State University United States

ISBN: (纸本)9781450396134

Context: software engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of softwa

关键词： Metrics Ensemble method Performance Fault Prediction Classification

来源：评论

学校读者我要写书评

暂无评论

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

来源：评论

学校读者我要写书评

暂无评论

Surface EMG Signal Analysis using Hand-Crafted Features for Detection and Classification of GTC seizures 2

Surface EMG Signal Analysis using Hand-Crafted Features for ...

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Naveed, Maryam Gul Khawaja, Sajid Usman Akram, Muhammad National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Epileptic seizures with the risk of sudden unexpected death in epilepsy affect the quality of life. Nearly, one-fourth of the individuals suffer from seizures that cannot be treated with medications. Due to the high-level possibility of injuries and complications, generalized tonic-clonic seizures have a considerable contribution to unexpected death. These generalized tonic-clonic seizures activity need to be detected and identified through brain and muscle activity, heart rates, and EMG signals. In this paper, we propose a framework for distinguishing normal activity from seizure activity along-with its categorization. Proposed framework focuses on extraction of multiple sEMG hand-crafted features with the time and frequency domain analysis. The proposed methodology for sEMG signals and for GTC class detection has been tested using multiple classifiers including KNN, SVM and ensembles. The obtained results have shown 10% improvement in classification over the state-of the-art approaches available in literature. © 2022 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset

Centralized Machine Learning Versus Federated Averaging: A C...

引用

作者： Peng, Sony Yang, Yixuan Mao, Makara Park, Doo-Soon Department of Software Convergence Soonchunhyang University Korea Republic of Department of Computer Software Engineering Soonchunhyang University Korea Republic of

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated;smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment. Copyright © 2022 KSII.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

People Counting System Using OpenCV Algorithms and Edge Computing for Safety Management

People Counting System Using OpenCV Algorithms and Edge Comp...

引用

2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Seitbattalov, Zhexen Y. Canbolat, Huseyin Atanov, Sabyrzhan K. Moldabayeva, Zhanar S. Kyzyrkanov, Abzal E. Maidanov, Adil K. L. N. Gumilyov Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan Ankara Yildirim Beyazit University Department of Electrical and Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350335040

The necessity of ensuring people's safety in urban conditions during gathering in a certain indoor or outdoor area, as well as during the operation of technical equipment, requires measurement of their number, weights, the distance between them, density and many other parameters. The collection of those parameters and analysis allows you to ensure the optimal numerical value of people per area and not exceed the critical peak value, ensuring the safety of people's health and life. However, automating those processes requires stable network bandwidth and enormous computing resources for data transferring to the data center and its processing, as well as applying artificial intelligence algorithms to the data and sending the results back to the end-user. In this paper, to avoid the disadvantages of cloud computing, we consider Raspberry Pi with a camera module as an edge device to develop an automated people-counting system based on using OpenCV algorithms and Edge Computing for Safety Management. The proposed system can be used for indoor or outdoor areas, during scaffolding works and lift's operation for controlling an optimal number of people. As a result of the application the edge computing, network bandwidth and computing resources of the data center have been optimized. © 2023 IEEE.

关键词： Image processing

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：