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Relevant bibliographies by topics / Sparsity-awareness

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Academic literature on the topic 'Sparsity-awareness'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Sparsity-awareness"

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Zhou, Deyu, Meng Zhang, Linhai Zhang, and Yulan He. "A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14594–601. http://dx.doi.org/10.1609/aaai.v35i16.17715.

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Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where the content of some users is limited and the model fails to capture discriminative features of users. To address this issue, we hypothesize that users could be grouped together based on their rating biases as well as degree of rating consistency and the knowledge learned from groups could be employed to analyze the users with limited data. Therefore, in th

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Wu, Yunfa, Bin Zhang, Anbo Meng, Yong-Hua Liu, and Chun-Yi Su. "A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation." Energies 15, no. 14 (2022): 5245. http://dx.doi.org/10.3390/en15145245.

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This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operating conditions. Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. Furthermore, a hybrid model of the catenary and XGBoost (HMCX) method is proposed for sag estimation, which improves the accuracy of sag estimation by i

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Alvaro, Lopez Paredes, Heredia Conde Miguel, and Loffeld Otmar. "Sparsity-aware 3D ToF Sensing." IEEE Sensors Journal 23, no. 4 (2023): 3973–89. https://doi.org/10.1109/JSEN.2023.3234533.

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In this work, we propose several time-of-flight (ToF) sensing schemes which tackle the challenge of covering very-wide areas and long ranges in nearly real time, with relatively simple implementation and low associated computational load. We thoroughly describe two methodologies for the resolution of the inverse problem. First, we extend a greedy algorithm, such as orthogonal matching pursuit (OMP), by considering an initial refinement of the spatial domain in which the signal recovery is performed. Then, we propose various nonadaptive techniques for the construction of the sensing matrices, r

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Wu, Zhijun, Zhuoning Bai, Lizhe Zhang, and Kenian Wang. "Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness." Security and Communication Networks 2022 (September 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/3757662.

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In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designe

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Asabere, Nana Yaw, and Amevi Acakpovi. "ROPPSA: TV Program Recommendation Based on Personality and Social Awareness." Mathematical Problems in Engineering 2020 (June 8, 2020): 1–15. http://dx.doi.org/10.1155/2020/1971286.

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The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness (ROP

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Javed, Arifa, Hongying Zan, Orken Mamyrbayev, et al. "Transformer-Based Re-Ranking Model for Enhancing Contextual and Syntactic Translation in Low-Resource Neural Machine Translation." Electronics 14, no. 2 (2025): 243. https://doi.org/10.3390/electronics14020243.

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Neural machine translation (NMT) plays a vital role in modern communication by bridging language barriers and enabling effective information exchange across diverse linguistic communities. Due to the limited availability of data in low-resource languages, NMT faces significant translation challenges. Data sparsity limits NMT models’ ability to learn, generalize, and produce accurate translations, which leads to low coherence and poor context awareness. This paper proposes a transformer-based approach incorporating an encoder–decoder structure, bilingual curriculum learning, and contrastive re-

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Rodrigues, Tânia F., Ricardo Silva, Fernando Fernández-Aranda, and Paulo P. P. Machado. "Emotion Regulation, Eating Psychopathology, and Putative Transdiagnostic Psychological Processes: Findings from an Exploratory Network Analysis in a College Sample." Nutrients 16, no. 20 (2024): 3452. http://dx.doi.org/10.3390/nu16203452.

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Objective: Considering the prevalence of ED-related prodromal symptoms among higher education students (making them a population at risk for developing EDs), the main goals of this study were to conduct a network analysis in a college sample and to explore multivariate dependencies between a selection of empirically informed variables of interest to eating psychopathology, namely difficulties in emotion regulation and psychological processes (e.g., interoceptive awareness, self-compassion, self-criticism, mindfulness, and experiential avoidance). Methods: The sample included 294 college studen

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Shi, Meihui, Derong Shen, Yue Kou, Tiezheng Nie, and Ge Yu. "Next point-of-interest recommendation by sequential feature mining and public preference awareness." Journal of Intelligent & Fuzzy Systems 40, no. 3 (2021): 4075–90. http://dx.doi.org/10.3233/jifs-200465.

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With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI recommendation, since human movement exhibits sequential patterns in LBSNs. However, due to the check-ins’ sparsity problem, exploiting sequential patterns in next POI recommendation is a challenging issue, which makes the learned sequential patterns unreliable. Inspired by the fact that auxiliary information can be incorporated to alleviate

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Li, Changyong, Shunchun Zhang, and Zhijie Ma. "RF-YOLOv7: A Model for the Detection of Poor-Quality Grapes in Natural Environments." Agriculture 15, no. 4 (2025): 387. https://doi.org/10.3390/agriculture15040387.

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This study addresses the challenges of detecting inferior fruits in table grapes in natural environments, focusing on subtle appearance differences, occlusions, and fruit overlaps. We propose an enhanced green grape fruit disease detection model named RF-YOLOv7. The model is trained on a dataset comprising images of small fruits, sunburn, excess grapes, fruit fractures, and poor-quality grape bunches. RF-YOLOv7 builds upon the YOLOv7 architecture by integrating four Contextual Transformer (CoT) modules to improve target-detection accuracy, employing the Wise-IoU (WIoU) loss function to enhance

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Zhao, Xin, Chong Li, Linghong Wang, Chao Dong, and Zhaoxuan Meng. "Application of Health Education Program based on Intelligent Recommendation Algorithm in the Development of School-age Children's Hand Hygiene Behavior." Scalable Computing: Practice and Experience 25, no. 4 (2024): 2876–88. http://dx.doi.org/10.12694/scpe.v25i4.2855.

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The cultivation of hand hygiene behavior among school-age children is an important way to prevent the spread of diseases and ensure children’s health. However, traditional health education methods lack personalized programs tailored to each child, which cannot effectively improve their hand hygiene awareness and behavior. In response to this issue, the study combines multi-objective particle swarm optimization algorithm to provide personalized hand hygiene behavior development recommendations for school-age children, improving their hand hygiene awareness and behavioral level. The study adopte

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Conference papers on the topic "Sparsity-awareness"

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Censor-Hillel, Keren, Dean Leitersdorf, and Volodymyr Polosukhin. "On Sparsity Awareness in Distributed Computations." In SPAA '21: 33rd ACM Symposium on Parallelism in Algorithms and Architectures. ACM, 2021. http://dx.doi.org/10.1145/3409964.3461798.

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Li, Yang, Tong Chen, Yadan Luo, Hongzhi Yin, and Zi Huang. "Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/206.

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Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborativ

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Zuoxu, Wang, Li Xinyu, Chen Chun-Hsien, and Zheng Pai. "A Hypergraph-Based Knowledge Representation Model for Smart Product-Service System Development." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66732.

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Abstract In the trend of digital servitization, manufacturing companies have been transforming their business paradigms to Smart product-service systems (Smart PSS) by integrating products and associated services as bundles. To support the knowledge-intensive process of Smart PSS development, massive domain knowledge should be well-organized and reused. However, due to the existence of non-binary relations caused by product-service bundles (PSB) and context-awareness concerns in the Smart PSS development activities, conventional graph-based approaches for knowledge representation may lose esse

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Li, Zhenni, Shuxue Ding, Yujie Li, and Wuhui Chen. "Dictionary learning with ℓ1/2 regularizer for sparsity based on proximal operator." In 2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST). IEEE, 2015. http://dx.doi.org/10.1109/icawst.2015.7314029.

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Weiwei Wu, Yansong Wang, and Zhaokun Xu. "A kind of adaptive filter based on a new sparsity measure function." In 2011 International Conference on Advanced Intelligence and Awareness Internet (AIAI 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.1480.

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Rupasingha, Rupasingha A. H. M., and Incheon Paik. "Evaluation of Web Service Recommendation Performance via Sparsity Alleviating by Specificity-Aware Ontology-Based Clustering." In 2018 9th International Conference on Awareness Science and Technology (iCAST). IEEE, 2018. http://dx.doi.org/10.1109/icawst.2018.8517251.

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