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arXiv 2024年

作者： Atzori, Andrea Boutros, Fadi Damer, Naser Fenu, Gianni Marras, Mirko Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Fraunhofer Institute for Computer Graphics Research Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concerns. Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models with reduced reliance on authentic images, thereby mitigating data collection concerns. First, we explored the performance gap among recent state-of-the-art face recognition models, trained with synthetic data only and authentic (scarce) data only. Then, we deepened our analysis by training a state-of-the-art backbone with various combinations of synthetic and authentic data, gaining insights into optimizing the limited use of the latter for verification accuracy. Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind. Our results highlighted the effectiveness of FR trained on combined datasets, particularly when combined with appropriate augmentation techniques. © 2024, CC BY.

关键词： Face recognition

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A Comparative Study On Sentiment Lexicons For Automatic Labeling 13

A Comparative Study On Sentiment Lexicons For Automatic Labe...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Mehenaoui, Zohra Merabti, Chayma Tadjer, Houda Lafifi, Yacine LabSTIC Laboratory University 8 Mai 45 Guelma Algeria Computer Science Department University 8 Mai 45 Guelma Algeria

Sentiment analysis is a natural language processing task that involves extracting meaningful information concerning people's opinions and sentiments towards products, services, and more, which can be utilized in several applications. This task requires using data presented on online platforms. With the increasing use of the World Wide Web, a huge amount of data can be exploited. To do so, this data should be present in a suitable format like datasets. A sentiment analysis dataset generally requires reviews or comments and their sentiment labels (positive, negative, or neutral). Experts can do the labeling task manually, but this requires a lot of time and energy, especially when dealing with a massive data *** this paper, we performed automatic labeling of a dataset consisting of 5200 comments of students using different well-known sentiment analysis tools which are VADER, TextBlob, SpaCy, and SentiWordNet, making a comparison of these tools to find the most efficient one for automatic labeling of this dataset. The results showed that TextBlob outperforms the other tools with an accuracy of 92% and an F1-score of 89%. © 2024 Copyright for this paper by its authors.

关键词： Labeled data

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StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

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A Comparison of LEACH-Like Protocols to Improve Power Consumption Efficiency in Wireless Sensor Networks 8th

A Comparison of LEACH-Like Protocols to Improve Power Consum...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Benhadji, Mohammed Kaddi, Mohammed Omari, Mohammed Lagouch, Aakila LEESI Laboratory Material Sciences Department Ahmed Draïa University Adrar Adrar Algeria LDDI Laboratory Mathematics and Computer Science Department Ahmed Draïa University Adrar Adrar Algeria Computer Science and Engineering Department American University of Ras Al Khaimah Ras Al Khaimah United Arab Emirates

ISBN: (纸本)9789819748945

Many clustering-based routing techniques are widely used in wireless sensor networks due to their low power consumption and longer network life. One of the most well-known protocols is LEACH, which assisting in the decrease of sensor energy usage during the data aggregation and transmission phase to the base station. However, the cluster head selection process is regarded as a drawback that reduces the protocol’s efficiency. Recently, researchers have attempted to develop approaches relying on several optimization methods. In this article, we attempt to present the LEACH methodology, highlighting its flaws, then discussing some of the protocol enhancements that has improved the efficiency of the protocol. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering Energy consumption LEACH Wireless sensor network

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Enhanced Brain Tumor Segmentation via Transfer Learning and Attention Mechanism 1

Enhanced Brain Tumor Segmentation via Transfer Learning and ...

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1st Mediterranean Smart Cities Conference, MSCC 2024

作者： Aboussaleh, Ilyasse Riffi, Jamal Fazazy, Khalid El Mahraz, Mohamed Adnane Tairi, Hamid LISAC Laboratory Department of Computer Science Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah LISAC Laboratory Fez Morocco

ISBN: (纸本)9798350374001

Brain tumor segmentation involves the crucial process of distinguishing diseased regions within the brain from healthy tissue in medical imaging, playing a crucial role in diagnosis and treatment planning for brain tumors. Deep learning, particularly encoder-decoder architectures, has recently demonstrated significant advancements in this field. This study explores the application of transfer learning and the attention mechanism to develop various models for brain tumor segmentation tasks. By fine-tuning three established pre-trained models (VGG19, ResNet50, and MobileNetV2) during the encoder phase, we aim to enhance the recognition of pertinent features for this specific task. Additionally, integrating the attention mechanism in the decoder phase enables selective focus on specific features within the input feature map, facilitating the preservation of fine details while disregarding irrelevant information. Extensive experiments are conducted on the BraTS 2020 dataset, yielding promising results characterized by high accuracy and dice similarity coefficient scores. ©2024 IEEE.

关键词： Transfer learning

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Mean Teacher for Weakly Supervised Polyphonic Sound Event Detection: An Empirical Study 7

Mean Teacher for Weakly Supervised Polyphonic Sound Event De...

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7th International Conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Diffallah, Zhor Ykhlef, Hadjer Bouarfa, Hafida University of Blida 1 Lrdsi Laboratory Department of Computer Science Algeria University of Blida 1 Latsi Laboratory Department of Computer Science Algeria

ISBN: (纸本)9781665480420

Sound event detection refers to the task of categorizing the types of events occurring in an audio recording, in addition to pinpointing the start and end times of each occurrence. This task has recently grown in popularity as a result of its aptitude to enhance a myriad of applications. Building sound event detection systems heavily relies on the representational power of deep neural network architectures. Deep network architectures require a large amount of strongly annotated audio data, where the exact temporal locations of each sound event are indicated. However, manually annotating audio recordings with the type of events present and the corresponding time boundaries is both costly and laborious. To mend this, learning from weak labels has been adopted in an attempt to bypass the labeling barrier. In this paper, we examine the effect of incorporating weakly-labeled data into the training process of sound event detection systems. Moreover, we analyze the behavior of the Mean Teacher framework under various deep learning configurations. Our experimental results reveal that training a well calibrated Mean Teacher structure;on weakly-labeled data, can improve the predictive performance of sound event detection systems. © 2022 IEEE.

关键词： Network architecture

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If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces

If It's Not Enough, Make It So: Reducing Authentic Data Dema...

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International Conference on Automatic Face and Gesture Recognition

作者： Andrea Atzori Fadi Boutros Naser Damer Gianni Fenu Mirko Marras Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Fraunhofer Institute for Computer Graphics Research Germany Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concerns. Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models with reduced reliance on authentic images, and thus mitigating large authentic data collection concerns. First, we explored the performance gap among recent state-of-the-art face recognition models, trained only on synthetic data or authentic data. Then, we deepened our analysis by training a state-of-the-art back-bone with various combinations of synthetic and authentic data, gaining insights into optimizing the limited use of the latter for verification accuracy. Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind. Our results highlighted the effectiveness of FR trained on combined datasets, particularly when combined with appropriate augmentation techniques.

关键词： Training Data privacy Accuracy Face recognition Gesture recognition Data collection Data augmentation

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Modeling and Performance Assessment of Drone-based Food Delivery Logistics Using Colored Generalized Stochastic Petri Nets 1

Modeling and Performance Assessment of Drone-based Food Deli...

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1st International Conference on Logistics, ICL 2024

作者： Oukas, Nourredine Boulif, Menouar Zerrouki, Taha University of Bouira LIM Laboratory Bouira Algeria University of Boumerdes LIMOSE Laboratory Boumerdes Algeria University of Bouira Computer Science Department Bouira Algeria

ISBN: (纸本)9798331518844

This paper proposes a new modeling to a meal delivery system (MDS) with smart interconnected drones. The use of such Unmanned Aerial Vehicles (UAVs) in MDS is very much needed or even necessary in cases of delivering meals to customers located in tough-to-reach places. We use Colored Generalized Stochastic Petri Nets (CGSPN) to model and then simulate the MDS dynamic activities. The conducted simulation shows the proposed CGSPN ability to analyse the behaviour of the MDS before its actual installation. © 2024 IEEE.

关键词： Drones

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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