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Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang and Changming Sun
Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representa...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Shiwei Ruan, Hao Cang, Huixin Chen, Tianying Yan, Fei Tan, Yuan Zhang, Long Duan, Peng Xing, Li Guo, Pan Gao and Wei Xu
Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. Howeve...
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Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters and Seyit Camtepe
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware familie...
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Xuyang Wang, Yajun Du, Danroujing Chen, Xianyong Li, Xiaoliang Chen, Yongquan Fan, Chunzhi Xie, Yanli Li and Jia Liu
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previ...
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Pieter Cawood and Terence Van Zyl
The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the...
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Elie Saad, Marcin Paprzycki, Maria Ganzha, Amelia Badica, Costin Badica, Stefka Fidanova, Ivan Lirkov and Mirjana Ivanovic
There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In gene...
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Robyn C. Thompson, Seena Joseph and Timothy T. Adeliyi
The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that ha...
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Adamantia G. Spatioti, Ioannis Kazanidis and Jenny Pange
Distance education is now a reality introducing a ?specific methodology of flexible and interactive multiform learning?. Due to its characteristics, different instructional design models apply to distance education as guidelines of the design thinking pr...
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Luiz Henrique dos Santos Fernandes, Ana Carolina Lorena and Kate Smith-Miles
Various criteria and algorithms can be used for clustering, leading to very distinct outcomes and potential biases towards datasets with certain structures. More generally, the selection of the most effective algorithm to be applied for a given dataset, ...
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Gabriel Dahia and Maurício Pamplona Segundo
We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem i...
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Kuekyeng Kim, Yuna Hur, Gyeongmin Kim and Heuiseok Lim
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations?usually ...
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Ouiame Filali Marzouki,Mohammed Khalidi Idrissi,Samir Bennani
Pág. pp. 18 - 39
This meta-analysis has two aims: a) to address the main effects of social constructivist mobile learning environments on learners? knowledge acquisition and their academic achievements b) to address potential factors regarding design principles and instr...
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Zheng Li, Xueyuan Huang, Liupeng Gong, Ke Yuan and Chun Liu
Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users? check-in sequences, and are brittle to users with few interactions. M...
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Noa Mansbach and Amos Azaria
It is difficult to overestimate the importance of detecting human deception, specifically by using speech cues. Indeed, several works attempt to detect deception from speech. Unfortunately, most works use the same people and environments in training and ...
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Tianshu Zhang, Wenwen Dai, Zhiyu Chen, Sai Yang, Fan Liu and Hao Zheng
Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate network ar...
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Shuang Guo, Yarong Du and Liang Liu
In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the dest...
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Alex Cevallos-Culqui, Claudia Pons, Gustavo Rodriguez
Pág. 81 - 111
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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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Renaud Fabre, Otmane Azeroual, Patrice Bellot, Joachim Schöpfel and Daniel Egret
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular...
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