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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches

Procedia computer science 2024年 251卷 740-745页

作者： Zakia Labd Said Bahassine Khalid Housni Fatima Zahrae Aithamou Aadi Laboratory of research in informatics L@RI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained.

关键词： Glove Text Classification, Deep Learning, Natural Language Processing (NLP), Recurrent Convolutional Neural Networks (RCNN)

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Bridging the Artificial Intelligence Knowledge and Skill Gaps in Africa: a Case of the 3rdGoogle Tensorflow Bootcamp and FEDGEN Mini-Workshop

Bridging the Artificial Intelligence Knowledge and Skill Gap...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetiba, Emmanuel Wejin, John S. Oshin, Oluwadamilola Ifijeh, Ayodele H. Lawal, Comfort Thakur, Surendra Colin Awelewa, Ayokunle A. Kala, Raymond Jules Ajayi, Priscilla O. Akanle, Matthew B. Sweetwiliams, Faith O. Nnaji, Uche Owolabi, Emmanuel Idowu-Bismark, Olabode Sobola, Gabriel Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Covenant University Department of Electrical and Information Engineering CApIC-ACE Jalingo Nigeria Taraba State University Department of Computer Science Jalingo Nigeria Covenant University Department of Electrical and Information Engineering CApIC-ACE Ota Nigeria Durban University of Technology Department of Information Technology Kzn E-Skills Co-Laboratory Durban4001 South Africa Covenant University Department of Electrical and Information Engineering Ota Nigeria International University of Grand Bassam Grand Bassam Ivory Coast Covenant University Center for Systems and Information Services CApIC-ACE Ota Nigeria Covenant University Department of Computer and Information Science CApIC-ACE Ota Nigeria Ltd 405 Justice Mahomed St. Gauteng Pretoria0002 South Africa

ISBN: (纸本)9798350358155

In transiting from one civilization to another, technology has played a vital and positive role. In the 21st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man's capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11th to 13th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant University. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations. © 2024 IEEE.

关键词： Federated learning

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KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-School Recommendation System 19th

KNN-Based Collaborative Filtering for Fine-Grained Intellig...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Xu, Jinfeng Liu, Jiyi Ma, Zixiao Wang, Yuyang Wang, Wei Ngai, Edith Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Software Engineering Sun Yat-Sen University Guangdong Zhuhai528406 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Computer Science Cornell University IthacaNY14853 United States Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BITUniversity Guangdong Shenzhen518172 China

ISBN: (纸本)9783031651250

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Collaborative filtering

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer systems science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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非平衡压缩实现高灵敏度、线性响应协同的离电压力传感器

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science Bulletin 2024年第14期69卷 2221-2230页

作者：杨静李志斌伍莹沈勇张明陈彬袁国江肖松华冯建松张旭唐毓蔚丁孙安陈晓龙王太宏 Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518055China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055China Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education Engineering Research Center of Advanced Semiconductor Technology and Application of Ministry of EducationChangsha Semiconductor Technology and Application Innovation Research InstituteCollege of Semiconductors(College of Integrated Circuits)Hunan UniversityChangsha 410082China School of Environmental and Chemical Engineering Yanshan UniversityQinhuangdao 066004China School of Integrated Circuits Nanjing UniversitySuzhou 215163China

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal ***,the current strategies for stabilizing axial microstructures(e.g.,micro-pyramids)are mainly susceptible to structural stiffening during compression,thereby limiting the realization of high sensitivity and ***,we report a bending-induced nonequilibrium compression process that effectively enhances the compressibility of microstructures,thereby crucially improving the efficiency of interfacial area growth of electric double layer(EDL).Based on this principle,we fabricate an iontronic flexible pressure sensor with vertical graphene(VG)array ***-high sensitivity(185.09 kPa^(-1))and linearity(R^(2)=0.9999)are realized over a wide pressure range(0.49 Pa–66.67 k Pa).It also exhibits remarkable mechanical stability during compression and *** sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception(MLP)neural *** opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.

关键词： Artificial mechanoreceptor Pressure sensor Vertical grapheme Ionic nanofiber Object recognition E-skin

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Wheat Rust Disease Segmentation from Ground Imagery 25

Wheat Rust Disease Segmentation from Ground Imagery

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Anwar, Hirra Khan, Muhammad Jawad Fayyaz, Muhammad Mian, Ajmal Saeed Shafait, Faisal School of Mechanical and Manufacturing Engineering Nust Islamabad Pakistan Prince Sattam Bin Abdul Aziz University Department of Electrical Engineering Al-Kharj Saudi Arabia Crop Diseases Research Institute National Agriculture Research Center Islamabad Pakistan University of Western Australia Perth Australia School of Electrical Engineering & Computer Science Nust Islamabad Pakistan National Center of Artificial Intelligence Deep Learning Laboratory Islamabad Pakistan

ISBN: (纸本)9798350379037

Wheat rust disease is a major agricultural threat, causing significant yield losses globally. Precision agriculture and autonomous robot systems represent pivotal advancements in modern farming and disease detection, leveraging technology to enhance crop management, optimize resource use, and improve productivity. These systems rely heavily on accurate and comprehensive data to train effectively. However, current datasets often fall short of meeting the specific needs of these advanced agricultural technologies. This paper addresses this gap by introducing a primary contribution in the form of an open-source dataset focused on wheat rust disease segmentation for precision agriculture. Open-source datasets in literature are either general plant disease datasets or wheat disease classification datasets. Our proposed wheat rust segmentation dataset, Focussed Rust Disease Images (FRDI), provides close-up views of disease manifestations on wheat leaves, capturing the natural variability and complexity of diseases in field conditions, thereby meeting the requirements of precision agriculture systems. This facilitates detailed analysis and effective training of deep learning models designed for disease detection and segmentation tasks for real-world agricultural environments. Furthermore, in our work we present an improved wheat rust disease detection and segmentation technique which is based on the concept of co-salient feature extraction for disease identification within complex and cluttered field images. By integrating localized and generic contextual information, our proposed approach effectively identifies key features and intricate patterns associated with wheat rust disease. © 2024 IEEE.

关键词： Semantic Segmentation

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Instance-Level Panoramic Audio-Visual Saliency Detection and Ranking 24

Instance-Level Panoramic Audio-Visual Saliency Detection and...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Ruohao Niu, Dantong Qu, Liao Qi, Yanyu Shi, Ji Yue, Wenzhen Xing, Bowei Chen, Taiyan Ying, Xianghua National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798400706868

Panoramic audio-visual saliency detection is to segment the most attention-attractive regions in 360° panoramic videos with sound. To meticulously delineate the detected salient regions and effectively model human attention shift, we extend this task to more fine-grained instance scenarios: identifying salient object instances and inferring their saliency ranks. In this paper, we propose the first instance-level framework that can simultaneously be applied to segmentation and ranking of multiple salient objects in panoramic videos. Specifically, it consists of a distortion-aware pixel decoder to overcome panoramic distortions, a sequential audio-visual fusion module to integrate audio-visual information, and a spatio-temporal object decoder to separate individual instances and predict their saliency scores. Moreover, owing to the absence of such annotations, we create the ground-truth saliency ranks for the PAVS10K benchmark. Extensive experiments demonstrate that our model is capable of achieving state-of-the-art performance on the PAVS10K for both saliency detection and ranking tasks. The code is available at https://***/ruohaoguo/pavsodr. © 2024 ACM.

关键词： Contrastive Learning

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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Open-Vocabulary Audio-Visual Semantic Segmentation 24

Open-Vocabulary Audio-Visual Semantic Segmentation

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Ruohao Qu, Liao Niu, Dantong Qi, Yanyu Yue, Wenzhen Shi, Ji Xing, Bowei Ying, Xianghua National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Berkeley Ai Research University of California Berkeley BerkeleyCA United States College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798400706868

Audio-visual semantic segmentation (AVSS) aims to segment and classify sounding objects in videos with acoustic cues. However, most approaches operate on the close-set assumption and only identify pre-defined categories from training data, lacking the generalization ability to detect novel categories in practical applications. In this paper, we introduce a new task: open-vocabulary audio-visual semantic segmentation, extending AVSS task to open-world scenarios beyond the annotated label space. This is a more challenging task that requires recognizing all categories, even those that have never been seen nor heard during training. Moreover, we propose the first open-vocabulary AVSS framework, OV-AVSS, which mainly consists of two parts: 1) a universal sound source localization module to perform audio-visual fusion and locate all potential sounding objects and 2) an open-vocabulary classification module to predict categories with the help of the prior knowledge from large-scale pre-trained vision-language models. To properly evaluate the open-vocabulary AVSS, we split zero-shot training and testing subsets based on the AVSBench-semantic benchmark, namely AVSBench-OV. Extensive experiments demonstrate the strong segmentation and zero-shot generalization ability of our model on all categories. On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU on base categories and 29.14% mIoU on novel categories, exceeding the state-of-the-art zero-shot method by 41.88%/20.61% and open-vocabulary method by 10.2%/11.6%. The code is available at https://***/ruohaoguo/ovavss. © 2024 ACM.

关键词： Semantic Segmentation

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An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

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computers, Materials & Continua 2022年第2期70卷 3667-3683页

作者： Hassen Mestiri Imen Barraj Abdullah Alsir Mohamed Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia METS Research Group Electrical Engineering DepartmentNational Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

The Advanced Encryption Standard cryptographic algorithm,named AES,is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information *** of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret *** now,a several AES fault detection schemes against fault injection attacks have been *** this paper,so as to ensure a high level of security against fault injection attacks,a new efficient fault detection scheme based on the AES architecture modification has been *** this reason,the AES 32-bit round is divided into two half rounds and input and pipeline registers are implemented between *** proposed scheme is independent of the procedure the AES is ***,it can be implemented to secure the pipeline and iterative *** evaluate the robustness of the proposed fault detection scheme against fault injection attacks,we conduct a transient and permanent fault attacks and then we determine the fault detection capability;it is about 99.88585%and 99.9069%for transient and permanent faults *** have modeled the AES fault detection scheme using VHDL hardware language and through hardware FPGA *** FPGA results demonstrate that our scheme can efficiently protect the AES hardware implementation against fault *** can be simply implemented with low *** addition,the FPGA implementation performances prove the low area overhead and the high efficiency and working frequency for the proposed AES detection scheme.

关键词： Security cryptographic circuits AES hardware implementation fault detection fault attacks

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