检索结果-内蒙古大学图书馆

Advancement in Technology (ICONAT), International Conference for

作者： Naveen Pol S. Arunarani R. Thiru Murugan V. Divya H Anwer Basha Gagan Singh ISBR Business School Bengaluru India Department of Computer Applications FSH SRM IST Chennai India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Virudhunagar India Department of Information Technology VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Department of BBA Meerut Institute of Technology (MIT) Meerut India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Due to technical advancements in this digital era, particularly in the e-commerce sector, the corporate paradigm has changed substantially. A common platform for advertising products is online marketplaces, sometimes known as ecommerce platforms. Businesses in the increasingly competitive e-commerce space require a strong marketing plan to boost sales conversion and differentiate between the competitions. Selected features, preprocessing, and training of models all depend on correct sequencing. The proposed approach utilized the SG smoothing filter during the preprocessing phase. Principal component analysis (PCA) is a statistical method that can be employed in feature selection to decrease the dimensionality of a dataset that contains numerous associated variables. Precise control over the qualities is necessary for RNN-RBM training. This approach appears to be far more cutting-edge than the current RBM and RNN algorithms. A significant improvement in accuracy was noted in the results, which reached 96.33%.

关键词： Training Sequential analysis Smoothing methods Accuracy Sensitivity Biological system modeling Neural networks Feature extraction Electronic commerce Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-Photon Subtraction from Squeezed Light toward Full Quantum State Engineering

Multi-Photon Subtraction from Squeezed Light toward Full Qua...

引用

Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Fukui, Kosuke Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

We have applied up to four annihilation operations to squeezed light by photon subtraction. The presence of negative values in the Wigner function for all states indicates the successful implementation of quantum mani... 详细信息

ISBN: (纸本)9781557525185

关键词： Squeezed light

来源：评论

学校读者我要写书评

暂无评论

Optimization Model for Machine Learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge computing

Optimization Model for Machine Learning Based Intrusion Dete...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Kiruthiga R S. Jeyalaksshmi V. Vanitha Abdul Azeem Khan Souvik Pal Rahul Kumar Department of Computer Applications SRM Institute of Science & Technology Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Department of Computer Applications Sri Sankara Arts and Science College Kanchipuram India Faculty of Islamic Technology Negara Brunei Darussalam Department of CS and Engineering Sister Nivedita University Kolkata India Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The Internet of Things (IoT) and 5G communications now primarily use mobile edge computing (MEC). The fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. The network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. Thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time.

关键词： Support vector machines Machine learning algorithms Optimization models Intrusion detection Internet of Things Security Synchronization Servers Monitoring Cyberattack

来源：评论

学校读者我要写书评

暂无评论

How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale

arXiv

引用

arXiv 2025年

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Aalborg University Copenhagen Department of Computer Science Copenhagen Denmark Center for Hybrid Intelligence Department of Management and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Aalborg University Copenhagen Department of Computer Science Pioneer Centre for Artificial Intelligence Copenhagen Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands and Software and Societal Systems Carnegie Mellon University PittsburghPA United States

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Video Captioning: A Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization

Enhancing Video Captioning: A Bayesian Normalized Attention-...

引用

Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Khallikkunaisa H. S. Niranjana Murthy Shirish Kulkarni Syed Mohd Uzair Iqbal Umang Pancha Natrayan L Department of Computer Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bangalore Karnataka India Symbiosis Law School Symbiosis Centre for Advanced Legal Studies and Research Symbiosis International (Deemed University) Pune India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The explosive increase of video data requires the creation of advanced automated video captioning systems that can produce effective textual descriptions. Current methods, such as traditional deep learning (DL) models, are plagued with drawbacks such as high computational expense, overfitting, and inefficacy in contextual modeling, resulting in poor caption generation. For resolution of these difficulties, this contribution introduces the Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization (BN-AMGN-MGO) model, engineered to improve video captioning automatization on the MSVD set. The proposed framework starts off with pre-processing, in which unnecessary frames are removed via template matching to get only the representative frames. The feature encoding process utilizes a Bayesian Normalized Neural Network (BNNN) for dynamically manipulating probabilistic weight distributions to counter overfitting and increase robustness. During the decoder process, a Hierarchical Attention-based Multi-Dimensional Edge Graph Neural Network (HAM-GNN) combines temporal context modeling, graph neural networks, and hierarchical attention mechanisms to create structured textual descriptions. Classification stage employs a temporal attention mechanism to project dialogue act labels into attention-weighted representations, utilizing speaker dependencies for better accuracy. To further optimize performance, MGO is employed to refine HAM-GNN’s hyperparameters through wind direction determination, spore dispersal search, dual propagation search, and cryptobiosis mechanisms, ensuring effective exploration and exploitation of the search space. The proposed BN-AMGN-MGO model achieves the lowest MSE (0.021), highest PSNR (34.89), and SSIM (0.905), ensuring high-quality caption generation. Additionally, it outperforms existing models with an accuracy of 94.5% while maintaining the shortest computational time (5.6 sec), making it a robust and efficient

关键词： Attention mechanisms Accuracy Computational modeling Streaming media Graph neural networks Encoding Bayes methods Computational efficiency Context modeling Overfitting

来源：评论

学校读者我要写书评

暂无评论

SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks

arXiv

引用

arXiv 2023年

作者： Tang, Bowen Wu, Chenggang Yew, Pen-Chung Zhang, Yinqian Xie, Mengyao Lai, Yuanming Kang, Yan Wang, Wei Wei, Qiang Wang, Zhe SKLP Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhongguancun Laboratory Beijing China Computer Science & Engineering Department The University of Minnesota-Twin Cities MN55455 United States Department of Computer Science and Engineering The Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

Transient Execution Attacks (TEAs) have gradually become a major security threat to modern high-performance processors. They exploit the vulnerability of speculative execution to illegally access private data, and transmit them through timing-based covert channels. While new vulnerabilities are discovered continuously, the covert channels can be categorised to two types: 1) Persistent Type, in which covert channels are based on the layout changes of buffering, e.g. through caches or TLBs;2) Volatile Type, in which covert channels are based on the contention of sharing resources, e.g. through execution units or issuing ports. The defenses against the persistent-type covert channels have been well addressed, while those for the volatile-type are still rather inadequate. Existing mitigation schemes for the volatile type such as Speculative Compression and Time-Division-Multiplexing will introduce significant overhead due to the need to stall the pipeline or to disallow resource sharing. In this paper, we look into such attacks and defenses with a new perspective, and propose a scheduling-based mitigation scheme, called SPECWANDS. It consists of three priority-based scheduling policies to prevent an attacker from transmitting the secret in different contention situations. SPECWANDS not only can defend against both inter-thread and intra-thread based attacks, but also can keep most of the performance benefit from speculative execution and resource-sharing. We evaluate its runtime overhead on SPEC 2017 benchmarks and realistic programs. The experimental results show that SPECWANDS has a significant performance advantage over the other two representative schemes. Copyright © 2023, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm:A High-Density EEG Source Imaging Study

引用

Cyborg and Bionic Systems 2024年第1期5卷 282-293页

作者： Huan Wen Yucun Zhong Lin Yao Yueming Wang The Department of Neurobiology Affiliated Mental Health Center&Hangzhou Seventh People’s HospitalZhejiang University School of MedicineHangzhouChina The Nanhu Brain-Computer Interface Institute HangzhouChina The MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang UniversityHangzhouChina The College of Computer Science Zhejiang UniversityHangzhouChina The College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhouChina The Qiushi Academy for Advanced Studies HangzhouChina

Complementary to brain-computer interface(BCI)based on motor imagery(MI)task,sensory imagery(SI)task provides a way for BCI construction using brain activity from somatosensory *** underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior *** this study,we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram(EEG)recording,and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the *** the experiment,participants were instructed to perform the left and right hand tasks in MI paradigm,sensory stimulation(SS)paradigm and SI *** statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts,frontal and right temporal areas inαbands,whereas real SS and imagined SI showed a similar activation *** similarity between SS and SI may provide a way to train the BCI system,while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI *** combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider *** studies concentrate on the hybrid decoding method combining MI or SI with SS,but the underlining neurophysiological correlates between them were *** and SI differed from each other within the ipsilateral sensorimotor cortex in alpha *** is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.

关键词： tactile sensation tasks eeg source imaging behavior recordingin motor imagery tactile imagery tactile sensation motor imagery mi tasksensory imagery si task brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Amplified Amplitude Estimation: Exploiting Prior Knowledge to Improve Estimates of Expectation Values

arXiv

引用

arXiv 2024年

作者： Simon, Sophia Degroote, Matthias Moll, Nikolaj Santagati, Raffaele Streif, Michael Wiebe, Nathan Department of Physics University of Toronto Canada Quantum Lab Boehringer Ingelheim Ingelheim am Rhein 55218 Germany Department of Computer Science University of Toronto Canada Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Studies Toronto Canada

We provide a method for estimating the expectation value of an operator that can utilize prior knowledge to accelerate the learning process on a quantum computer. Specifically, suppose we have an operator that can be expressed as a concise sum of projectors whose expectation values we know a priori to be O(ϵ). In that case, we can estimate the expectation value of the entire operator within error ϵ using a number of quantum operations that scales as O(1/√ϵ). We then show how this can be used to reduce the cost of learning a potential energy surface in quantum chemistry applications by exploiting information gained from the energy at nearby points. Furthermore, we show, using Newton-Cotes methods, how these ideas can be exploited to learn the energy via integration of derivatives that we can estimate using a priori knowledge. This allows us to reduce the cost of energy estimation if the block-encodings of directional derivative operators have a smaller normalization constant than the Hamiltonian of the system. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum chemistry

来源：评论

学校读者我要写书评

暂无评论

Oil spill characterization and monitoring using symlet analysis from synthetic-aperture radar images 1st

Oil spill characterization and monitoring using symlet analy...

引用

1st International Conference on Advances in Electrical and computer Technologies, ICAECT 2019

作者： Jagdish, Mukta Jerritta, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies VISTAS Chennai India Department of Electronics and Communication Engineering Vels Institute of Science Technology & Advanced Studies VISTAS Chennai India

ISBN: (纸本)9789811555572

Background: To identify and monitor the occurrence of oil spill in the ocean using image data obtained from different satellites. It is to characterize and identify type and other physical characteristics of oil using appropriate image processing techniques and to predict the physical characteristics of oil spill using machine learning methods. Methods: In this paper, forty sample images were used with wavelet transform analysis and machine learning techniques. For wavelet analysis (SYMLET family such as sym2, sym3, sym4, sym5, sym6, sym7, sym8 analysis) and machine learning, k-nearest neighbor algorithm is applied to optimize the oil spill feature sets. Features included RGB, spreading, complexity, standard deviation, entropy, ellipticity, intensity and correlation coefficient. This experiment was conducted on RADARSAT-2 SAR images. The features were classified using k-nearest neighbor algorithm. Seventy percent of features used for training and thirty percent for testing. Results: The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. It also represents 92.6581% accuracy for crude oil using SYMLET 5 analysis which indicates better characterization of oil spills. Results denote oil spill detection using synthetic-aperture radar (SAR) remote sensing which provides an excellent tool in oil spill characterization;various features can be extracted from SAR data set. © Springer Nature Singapore Pte Ltd 2020.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Transformer-Based Material Recognition Via Short-Time Contact Sensing

SSRN

引用

SSRN 2024年

作者： Liu, Zhenyang Shao, Yitian Li, Qiliang Su, Jingyong School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing Beijing100871 China

Embodied intelligence needs haptic sensing for spontaneous and accurate material recognition. The haptic sensing module of an intelligent system can acquire material data through either sliding or tapping motions. Sliding movements are commonly adopted for collecting the spatial frequency features of the material but are less time-efficient than tapping. Here, we introduce a haptic sensing framework that can extract material features from short-time tapping signals. To improve the performance of material recognition, transfer learning is used by transferring the knowledge of pretrained model training on large-scale images into haptic sensing. The waveforms of the tapping signals are encoded as images to be input into a transformer model tailored for image recognition tasks. The encoding employs line graph image-point scaling, effectively accommodating signals that exhibit large variations in magnitude and temporal structures. Using the LMT haptic material database containing sliding and tapping data, our study showcases the efficacy of the proposed framework in material recognition tasks, especially for short-time (≤ 60 ms) sensing via tapping interactions. The findings provide fresh insights into haptic sensing technologies and may help improve the physical interaction capabilities of embodied intelligence, such as medical and rescue robots. © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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：