Block level cloud data deduplication scheme based on attribute encryption by Wenting GE, Weihai LI, Nenghai YU

“…Due to the existing cloud data deduplication schemes mainly focus on file-level deduplication.A scheme was proposed, based on attribute encryption, to support data block-level weight removal.Double granularity weight removal was performed for both file-level and data block-level, and data sharing was achieved through attribute encryption.The algorithm was designed on the hybrid cloud architecture Repeatability detection and consistency detection were conducted by the private cloud based on file labels and data block labels.A Merkle tree was established based on block-level labels to support user ownership proof.When a user uploaded the cipher text, the private cloud utilized linear secret sharing technology to add access structures and auxiliary information to the cipher text.It also updated the overall cipher text information for new users with permissions.The private cloud served as a proxy for re-encryption and proxy decryption, undertaking most of the calculation when the plaintext cannot be obtained, thereby reducing the computing overhead for users.The processed cipher text and labels were stored in the public cloud and accessed by the private cloud.Security analysis shows that the proposed scheme can achieve PRV-CDA (Privacy Choose-distribution attacks) security in the private cloud.In the simulation experiment, four types of elliptic curve encryption were used to test the calculation time for key generation, encryption, and decryption respectively, for different attribute numbers with a fixed block size, and different block sizes with a fixed attribute number.The results align with the characteristics of linear secret sharing.Simulation experiments and cost analysis demonstrate that the proposed scheme can enhance the efficiency of weight removal and save time costs.…”
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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance by Jose L. M. Perez, Roberto S. M. Barros, Silas G. T. C. Santos

“…Additionally, it introduces a self-training approach to provide more labels and optimize model learning and adaptation. …”
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A Multi-Level Multiple Contrastive Learning Method for Single-Lead Electrocardiogram Atrial Fibrillation Detection by Yonggang Zou, Peng Wang, Lidong Du, Xianxiang Chen, Zhenfeng Li, Junxian Song, Zhen Fang

“…However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. …”
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Microarrays as Model Biosensor Platforms to Investigate the Structure and Affinity of Aptamers by Jennifer A. Martin, Yaroslav Chushak, Jorge L. Chávez, Joshua A. Hagen, Nancy Kelley-Loughnane

“…The results indicate a minimum distance (linker length) from the surface and thymine nucleobase linker provides reproducible binding across varying conditions. An indirect labeling method, where the target was labeled with a biotin followed by a brief Cy3-streptavidin incubation, provided a higher signal-to-noise ratio and over two orders of magnitude improvement in limit of detection, compared to direct Cy3-protein labeling. …”
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Unlocking the potential of hydrogen deuterium exchange via an iterative continuous-flow deuteration process by Kevin Tatoueix, Marco Lepron, Cédric Barboux, Marie-Christine Scherrmann, Grégory Pieters, Sophie Feuillastre

“…Abstract Labelled compounds bearing hydrogen isotopes are keystones in diverse areas constituting a multi-billion dollar global market including drugs, diagnostics, biology, toxicology and smart materials. …”
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Feature selection algorithm for uncertain text classification by WANG Bo, JIA Yan, YANG Shu-qiang, ZHOU Bin

“…For text data with fixed feature values and uncertain class labels, features were ranked according to the correlation between features and uncertain class labels evaluated by HSIC. …”
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Kombinasi K-Means dan Support Vector Machine (SVM) untuk Memprediksi Unsur Sara pada Tweet by Wiga Maulana Baihaqi, Muliasari Pinilih, Miftakhul Rohmah

“…Obtained from the analysis that can automatically contain sentences on social media containing no SARA or not, but the corpus about sentences containing SARA does not yet exist, other than that the sentence label indicates SARA or no sentence. This study aims to make sentence corpus containing SARA elements obtained from twitter, then label sentences with labels containing elements of SARA and not, and conduct group sentiments. …”
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Multi-view graph neural network for fraud detection algorithm by Zhuo CHEN, Miao ZHU, Junwei DU

“…Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view graph neural network for fraud detection (MGFD) algorithm was proposed.First, A structure-independent encoder was used to encode the attributes of nodes in the network to learn the difference between the fraud node and the normal node.The hierarchical attention mechanism was designed to integrate the multi-view information in the network, and made full use of the interaction information between different perspectives in the network to model the nodes on the basis of learning differences.Then, based on the data imbalance ratio sampled subgraph, the sample was constructed according to the connection characteristics of fraud nodes for classification, which solved the problem of imbalance sample labels.Finally, the prediction label was used to identify whether a node is fraudulent.Experiments on real-world datasets have shown that the MGFD algorithm outperforms the comparison method in the field of graph-based fraud detection.…”
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SEGMENTASI LESI KULIT MONKEYPOX MENGGUNAKAN ARSITEKTUR U-NET by NI PUTU DIAN ASTUTIK, IGN LANANG WIJAYAKUSUMA

“…The U-Net model reached an accuracy of 88.07%, though some signs of overfitting were observed, likely due to low-quality label information from the watershed labeling process, which necessitates parameter tuning.…”
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Human activity recognition system based on active learning and Wi-Fi sensing by Guangzhi ZHAO, Zhipeng ZHOU, Wei GONG, Shaoqing CHEN, Haoquan ZHOU

“…Human activity recognition system based on deep learning and Wi-Fi sensing has gradually become the mainstream research field and has been developed in recent years.However, related systems heavily rely on training with huge labeled samples to reach a high accuracy, which is labor-intensive and unrealistic for many real-world scenarios.To solve this problem, a system that combines active learning with Wi-Fi based human activity recognition—ALSensing was proposed, which was able to train a well-perform classifier with limited labeled samples.ALSensing was implemented with commercial Wi-Fi devices and evaluated in six real environments.The experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% of total training samples, 58.97% recognition accuracy using 15% of total training samples, while the existing full-supervised system reaches 62.19% recognition accuracy.It demonstrates that ALSensing has a similar performance with baseline but requires much less labeled samples.…”
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Long-term live-cell imaging of GFAP+ astroglia and laminin+ vessels in organotypic mouse brain slices using microcontact printing by Christian Humpel

“…Using microcontact printing, antibodies are applied directly onto the slices in a controlled 400-μm-diameter pattern. Astrocytes are labeled with glial fibrillary acidic protein (GFAP), and vessels are labeled with laminin. …”
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Newly Generated Cells in the Dentate Gyrus Differentially Respond to Brief Spatial Exploration and Forced Swim in Adult Female Balb/C Mice by Gerardo Bernabé Ramírez-Rodríguez, María del Ángel Ocaña-Fernández, Leonardo Ortiz-López

“…We found a decreased activation of IdU-labelled cells in mice exposed to forced-swim stress with increase number of CldU-labelled cells. …”
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Human Supervision is Key to Achieving Accurate AI-assisted Wildlife Identifications in Camera Trap Images by Sarah E. Huebner, Meredith S. Palmer, Craig Packer

“…Using public support to extract information from vast datasets has become a popular method for accurately labeling wildlife data in camera trap (CT) images. …”
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ANALYSIS OF THE DETERMINANTS OF CONSUMER DECISIONS IN PURCHASING HALAL BEAUTY PRODUCTS by Eritrina Rizki Chairani, Qurtubi, Imam Djati Widodo, Rahma Fariza

“…Therefore, a further evaluation of the level of halal awareness and concern is carried out by conducting a descriptive analysis on each item of halal label indicators. Through this analysis, the results were obtained that 62.5% of the eight halal label indicator items were in the range of positive categories which showed that the level of halal awareness and concern of female students was high.…”
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Assessment of Cardiovascular Apoptosis in the Isolated Rat Heart by Magnetic Resonance Molecular Imaging by Karl-Heinz Hiller, Christiane Waller, Matthias Nahrendorf, Wolfgang R. Bauer, Peter M. Jakob

“…Because labeling of early apoptotic cell death in intact organs by histological and immunohistochemical methods remains challenging, the use of Gd-DTPA-labeled annexin V in MRI is clearly an improvement in rapid targeting of apoptotic cells in the ischemic and reperfused myocardium.…”
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Scale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection by Lunfeng Guo, Yizhe Zhang, Jiayin Liu, Huajie Liu, Yunwang Li

“…Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. …”
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Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification by Uk Jo, Seoung Bum Kim

“…Semi-supervised learning (SSL) models, which leverage both labeled and unlabeled datasets, have been increasingly applied to classify wafer bin map patterns in semiconductor manufacturing. …”
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Chlorophyll performances as an indicator of compost quality: Effectiveness of liquid humic substances and compost tea by Bouchra El Hayany, Khaoula En-Nejmy, Ghizlen El Mezouari El Glaoui, Mohamed Hafidi, Loubna El Fels

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MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data by Zaid A. El Shair, Samir A. Rawashdeh

“…Additionally, MEVDT includes manually annotated ground truth labels — consisting of object classifications, pixel-precise bounding boxes, and unique object IDs — which are provided at a labeling frequency of 24 Hz. …”
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