Article
Computer Science, Artificial Intelligence
Carlos Perinan-Pascual
Summary: The study focused on developing a model to automatically estimate the semantic relatedness between words, showing that the model's performance depends on independently constructed word embeddings and their interaction. By using a weighted average of cosine-similarity coefficients derived from independent word embeddings in a double vector space, high correlations with human judgments were achieved. The evaluation of word associations through a measure that considers both the rank ordering of word pairs and the strength of associations revealed findings unnoticed by traditional measures like Spearman's and Pearson's correlation coefficients.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Software Engineering
Fahimeh Ebrahimi, Miroslav Tushev, Anas Mahmoud
Summary: This article introduces an automated approach for classifying mobile apps into more focused categories of functionally related application domains, aiming to enhance app visibility and discoverability. By using word embeddings to generate numeric semantic representations of app descriptions and classifying them, more cohesive app categories can be generated.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Marco Rospocher
Summary: This paper investigates the problem of automatically detecting explicit song lyrics, proposing to tackle it with the FASTTEXT classifier. The evaluation shows that the FASTTEXT classifier is effective for explicit lyrics detection, substantially outperforming a reference approach for the task.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Kadir Gunel, Mehmet Fatih Amasyali
Summary: In this study, the authors investigate knowledge transfer between two different sentence embedding models, aiming to enhance the representational power of a lightweight model by leveraging the sophisticated features of a high-performing model. Instead of aligning logits or hidden states, their approach aligns the output sentence vectors of the teacher model with the word vector representations of the student model. Two minimization techniques are implemented in this process, and the enhanced embeddings are evaluated using WMT datasets and benchmark tasks. The results show that the proposed models retain and convey information in a model-specific manner.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
Summary: This study introduces a method that combines word embeddings with word-class embeddings in multiclass text classification tasks, which significantly improves the training of deep-learning models. Empirical evidence shows that this approach consistently enhances multiclass classification accuracy and is conceptually simple to implement.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Biochemical Research Methods
Md Shariful Islam Bhuyan, Itsik Pe'er, M. Sohel Rahman
Summary: The paper introduces a gradient boosting classifier SICaRiO for reliable detection of true indels, trained with gold-standard dataset from the 'Genome in a Bottle' consortium. The study also compares prediction difficulty for three categories of indels over different sequencing pipelines, and ranks genomic features based on their predictivity.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Dwi Sunaryono, Riyanarto Sarno, Joko Siswantoro
Summary: This study proposes a classification method for automatic epilepsy detection from EEG signals. The method processes the original signals using DFT and DWT, and classifies the signals using GBMs fusion. A genetic algorithm is utilized to select important features. The experimental results demonstrate that the proposed GBMs fusion improves the classification performance and achieves perfect epilepsy detection.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Agronomy
Junliang Fan, Jing Zheng, Lifeng Wu, Fucang Zhang
Summary: Accurate estimation of plant transpiration (T) is crucial for agricultural production, and this study investigated the use of machine learning models to estimate daily T of summer maize. Incorporating soil water content and leaf area index variables improved model performance, with the deep neural network (DNN) model slightly outperforming others.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Sajjad Shumaly, Mohsen Yazdinejad, Yanhui Guo
Summary: The study conducted sentiment analysis on a Persian website using fastText and CNN methods, achieving higher accuracy and independence from pre-processing compared to other research. By collecting reviews, creating word embeddings, and comparing multiple models, the research addressed the main issue in Persian sentiment analysis.
PEERJ COMPUTER SCIENCE
(2021)
Article
Thermodynamics
Xiaosong Ding, Chong Feng, Peiling Yu, Kaiwen Li, Xi Chen
Summary: This paper investigates the real-time prediction of nitrogen oxides (NOX) emission using around 17000 samples from a waste incineration power plant. A hybrid procedure is developed to select appropriate features from the unsynchronized data and establish a model based on gradient boosting decision tree (GBDT). The computational experiments show that GBDT outperforms its popular counterparts, supporting vector regression (SVR) and long short-term memory (LSTM), with low root mean square error (RMSE) values for training and test data. Shapley additive explanations (SHAP) analysis is also conducted.
Article
Computer Science, Artificial Intelligence
Francisco Janez-Martino, Rocio Alaiz-Rodriguez, Victor Gonzalez-Castro, Eduardo Fidalgo, Enrique Alegre
Summary: Spam emails are unwanted messages that can be harmful, and the authors propose a topic-based approach for classifying spam emails. They provide two new datasets containing approximately 15K emails each in English and Spanish. Experimental results show that TF-IDF and LR achieve the highest performance for English dataset, with a F1 score of 0.953 and 94.6% accuracy, while TF-IDF and NB achieve a F1 score of 0.945 and 98.5% accuracy for Spanish dataset.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Archana Goyal, Vishal Gupta, Manish Kumar
Summary: This article introduces a novel named entity recognition (NER) system using deep learning strategies and enhanced word embeddings. Enhanced word embeddings are generated by combining FastText word embeddings with minimal feature embeddings, improving the computational power of deep learning methods. Experimental results on corpora in two different languages demonstrate that the Bidirectional GRU and CNN model with enhanced word embeddings achieved high Precision, Recall, and F-score values in bilingual named entity recognition tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Salima Lamsiyah, Abdelkader El Mahdaouy, Bernard Espinasse, Said El Alaoui Ouatik
Summary: The proposed method in this paper is an unsupervised approach for generic extractive multi-document summarization, which selects relevant sentences based on a combination of three scores: sentence content relevance, sentence novelty, and sentence position. Experimental results show that this method outperforms several state-of-the-art methods and achieves promising results compared to the best performing methods, including supervised deep learning based methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Sangdi Lin, Bahareh Azarnoush, George Runger
Summary: This paper proposes a multi-target boosting method, named MTBR, for regression problems. Although it builds models separately for each target attribute, all target attributes are utilized when building each model by selecting the best models from all target attributes in each boosting iteration. The novel knowledge transfer approach introduced in this method uses the tree structure learned from one target attribute to predict another, proving the effectiveness of MTBR in leveraging knowledge from multiple target attributes and improving model accuracy through experiments with six datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Ergonomics
David M. Goldberg
Summary: This study utilizes machine learning to analyze accident narratives and convert them into analyzable fields. By assessing five dimensions, it provides information on the body part(s) injured, the source of the injury, the type of event causing the injury, whether hospitalization occurred, and whether amputation occurred. The study also demonstrates the generalizability of the models by analyzing accident narratives from different industries, improving organizations' ability to analyze textual accident narratives.
JOURNAL OF SAFETY RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Rachel Carlos Duque Reis, Seiji Isotani, Carla Lopes Rodriguez, Kamila Takayama Lyra, Patricia Augustin Jaques, Ig Ibert Bittencourt
COMPUTERS & EDUCATION
(2018)
Article
Computer Science, Interdisciplinary Applications
Tiago Roberto Kautzmann, Patricia A. Jaques
COMPUTERS & EDUCATION
(2019)
Article
Computer Science, Interdisciplinary Applications
Felipe de Morais, Patricia A. A. Jaques
Summary: Research on affect dynamics in digital learning systems focuses on how students' emotions evolve while using these systems. Recent studies suggest that affect dynamics can vary based on students' demographic and personal characteristics, such as nationality. However, there is a lack of understanding regarding affect dynamics in Latin American learners and the potential differences based on gender and emotion duration. This study introduces an affect dynamics model for Brazilian students using a Math DLS, highlighting the role of gender and emotion duration. Analyzing affect dynamics across cultures allows for better comprehension and application of these models, while also considering the influence of emotion duration and learners' gender.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Joice Cazanoski Gomes, Patricia A. Jaques
Summary: Misconceptions play a significant role in the learning process. This paper proposes a solution that utilizes expression trees and the DBSCAN algorithm to identify misconceptions by clustering similar errors. This approach allows for effective and efficient misconception diagnosis.
2023 IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, ICALT
(2023)
Article
Education & Educational Research
Rodrigo Smiderle, Sandro Jose Rigo, Leonardo B. Marques, Jorge Arthur Pecanha de Miranda Coelho, Patricia A. Jaques
SMART LEARNING ENVIRONMENTS
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Felipe de Morais, Tiago R. Kautzmann, Ig I. Bittencourt, Patricia A. Jaques
TRANSFORMING LEARNING WITH MEANINGFUL TECHNOLOGIES, EC-TEL 2019
(2019)
Proceedings Paper
Computer Science, Information Systems
Rodrigo Smiderle, Leonardo Marques, Jorge Artur P. de M. Coelho, Sandro J. Rigo, Patricia A. Jaques
2019 IEEE 19TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2019)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Tiago Roberto Kautzmann, Patricia Augustin Jaques
HIGHER EDUCATION FOR ALL: FROM CHALLENGES TO NOVEL TECHNOLOGY-ENHANCED SOLUTIONS
(2018)
Article
Computer Science, Artificial Intelligence
Ingo Jost, Joao Francisco Valiati
INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Helena Reis, Danilo Alvares, Patricia Jaques, Seiji Isotani
INTELLIGENT TUTORING SYSTEMS, ITS 2018
(2018)
Article
Computer Science, Artificial Intelligence
Ricardo Gerhardt, Joao F. Valiati, Jose Vicente Canto dos Santos
Proceedings Paper
Computer Science, Information Systems
Felipe de Morais, Ig I. Bittencourt, Seiji Isotani, Patricia A. Jaques
2017 IEEE 17TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT)
(2017)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
