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International Conference on computer Supported Cooperative Work in Design

作者： Ana Paula Pimentel Claudia Motta António Correia Daniel Schneider Postgraduate Program in Informatics (PPGI) UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha USA

This paper proposes an agenda of possible solutions relevant to the challenge of polarization of extreme positions. The challenge of mitigating polarization of extreme positions has been a hot topic in politics, journalism, new media, and, more recently, in CSCW and HCI research. In this study, the main focus is to investigate the harmful effects of polarization from a neuroscience point of view. The suggested solutions are based on theories of social cognitive neuroscience and encourage awareness of inappropriate behavior, an increase in the diversity of arguments, and the exercise of individual and collective responsibility, to mitigate the problem of polarization that threatens public debate and, consequently, democracy.

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Optimization of hyper parameter bandwidth on naïve Bayes kernel density estimation for the breast cancer classification 2

Optimization of hyper parameter bandwidth on naïve Bayes ke...

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2nd International Conference on information and Communications technology, ICOIACT 2019

作者： Sasongko, Theopilus Bayu Arifin, Oki Al Fatta, Hanif Information Technology Department Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Management of Informatics Study Program Economy and Business Departement Politeknik Negeri Lampung Lampung Indonesia

ISBN: (纸本)9781728116556

Cancer is viewed as the most harmful disease in the world. One of the most deadly cancer diseases attacking the women is breast cancer. In this paper, used Naïve Bayes classifier Kernel Density Estimation in breast cancer Wisconsin dataset take from UCI data repository. Naïve Bayes with Kernel Density Estimation was used as it could enable the Naïve Bayes to process the quantitative data. However, the performance of KDE (kernel density estimation) highly depends upon the size of the bandwidth parameter used to control the curve. The problem an accurate choice of parameter bandwidth (h) using trial and error technique requires a long process. A large bandwidth (h) will over-smooth the density and mask the structure in the data, a small bandwidth (h) will yield a density estimate that is spiky and very hard to interpret. The aim of this research is to compare the optimization of grid search parameter with the genetic algorithm to determine the bandwidth parameter (h) on the Naïve Bayes Kernel Density Estimator algorithm. The parameters used to compare the two optimization methods included accuracy, AUC (Area under Curve), and computation time. The experiment results on the test of the significance value on the comparison of grid search and algorithm in determination of the bandwidth parameter (h) naïve Bayes kernel density used Mann-Whitney Test. The result of the significance ranking showed that mean rank of the genetic algorithm accuracy was 489.95 better than the mean value of the rank of grid search accuracy at only 189.05, mean rank of AUC genetic algorithm was 491.82 better than mean rank of AUC grid search only at 187.18. The mean rank of computation time grid search was 243.37 faster than mean rank of computation time genetic algorithm at 435.63. © 2019 IEEE

关键词： Classification (of information)

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Sodium-ion battery cycle life prediction using machine learning

Sodium-ion battery cycle life prediction using machine learn...

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International Conference on Green Energy, Computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

arXiv

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arXiv 2022年

作者： Wang, Shengbo Wen, Shiping Yang, Yin Cao, Yuting Shi, Kaibo Huang, Tingwen The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar School of Information Science and Engineering Chengdu University Chengdu611040 China Science Program Texas A & M University at Qatar Doha23874 Qatar

This paper presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks. © 2022, CC BY-NC-ND.

关键词： Quadratic programming

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Linguistic structure analysis of Thai and white hmong languages for machine translation 4

Linguistic structure analysis of Thai and white hmong langua...

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4th Joint International Conference on Digital Arts, Media and technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT-NCON 2019

作者： Prasomsuk, Sukchatri Phumsaranakhom, Phuwitson Nuankaew, Pratya Nuankaew, Wongpanya Information Technology Program University of Phayao Phayao Thailand Computer Engineering Program University of Phayao Phayao Thailand Business Computer Program University of Phayao Phayao Thailand Information Technology Program Rajabhat Mahasarakham University Thailand

ISBN: (纸本)9781538680728

The Machine Translation (MT) is an automatic human language translation from one language to another language by computer namely 'Interlingual MT'. The most importance key of translation process is to understand the structure of the language. The research objectives are study the appropriate language structure, present the translation pattern, and develop a dictionary for Thai and White Hmong translations. The scope of the research is limited to the language structure which defines the word not exceed than 4 syllables. Linguistic structure analysis used the translation pattern and Human Aided Machine Translation (HAMT) process. The experimental results indicate that the translation process can be used in two ways are translation from primary language to target language and direct translation. Our dictionary has been developed with classification 6,677 words,3,229 sentences were employed for system testing. The proposed prototype translation system is achieved a superior efficiency outcome of translation than Google and Microsoft by F-Measure are 0.496, 0.316, and 0.244 respectively. For future work, the researcher intend to present of the appropriate machine translation algorithm and committed to developing a translation application between Thai and Hmong languages. © 2019 IEEE.

关键词： Machine translation

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Performance LoRa technology for Autonomous Vehicles

Performance LoRa Technology for Autonomous Vehicles

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International Seminar on Intelligent technology and Its Applications (ISITIA)

作者： Puput Dani Prasetyo Adi Iwan Purnama Arfanda Anugrah Siregar Angga Putra Juledi Firman Edi Abdul Karim Yuyu Wahyu Fairuz Iqbal Maulana Suhardi Atmoko Budi Susilo Toni Ilham Maliki Harahap Lintang Patria National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Science and Technology Labuhanbatu University Labuhanbatu North Sumatra Indonesia Telecommunication Engineering Study Program Politeknik Negeri Medan Medan City North Sumatra Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Riau Islands Indonesia Department of Information Technology Budi Darma University Medan North Sumatra Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Teacher Training and Education Labuhanbatu University Labuhanbatu North Sumatra Indonesia Faculty of Engineering Universitas Terbuka Jakarta Indonesia

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss.

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Hit Song Prediction based on Gradient Boosting Decision Tree 7

Hit Song Prediction based on Gradient Boosting Decision Tree

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7th NAFOSTED Conference on information and computer Science, NICS 2020

作者： Pham, Bang-Dang Tran, Minh-Triet Pham, Hoang-Long Advanced Program in Computer Science Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9780738105536

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research, we tackle this question by focusing on predicting rank of hit songs in the next 6 months. Our dataset is used in ZALO AI CHALLENGE 2019 in Hit Song Prediction problem including not only songs but also its information such as composer, artist name, released date, etc. Because of that, while most previous work formulates hit song prediction as a regression or classification problem, we present in this paper how to apply Gradient Boosting technique to treat it as a ranking problem. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position with 1.48815 Root Mean Square Error - our result dominates most of the solution in this competition (better than 3rd ranked solution of 87 in total). Moreover, it is possible to further improve by extracting chords, tones and more information from each song to obtain the highlights of songs and by using linguistics model to offer high-level features of metadata. © 2020 IEEE.

关键词： Decision trees

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Vehicle Counting: Survey and Experiments 7

Vehicle Counting: Survey and Experiments

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7th NAFOSTED Conference on information and computer Science, NICS 2020

作者： La, Hoang-Phong Ha, Minh-Thao Nguyen, Hai-Long Nguyen, Manh-Thien Advanced Program in Computer Science Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9780738105536

Traffic management needs information about traffic to control the flow of transports. With millions of traffic video cameras acting as sensors around the world, collecting the information about traffic flow in real-time is quite easy, but using that information to process and control the traffic flow is a challenge. For detecting vehicles, old methods like inductive loop detectors (ILD), infrared detectors (IRDs), laser sensors, etc. have problems with high cost, efficiency, difficulty, etc. The methods we use in this paper are detection-based counting, regression-based counting. The authors propose a new method that is the combination of two methods above to achieve better results. We also evaluate the viability of using Deep Learning pre-trained models include Faster R-CNN, SSD, YOLO for detection-based. We experiment on 2018 AI CITY CHALLENGE datasets and Vehicles Nepal datasets. Our results show the effectiveness of the combining method in accuracy compares to using each of the methods separately. © 2020 IEEE.

关键词： Object detection

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Facial Expression Recognition in the Wild: Efficiency of Modified-Category and Ensemble Learning Methods 7

Facial Expression Recognition in the Wild: Efficiency of Mod...

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7th NAFOSTED Conference on information and computer Science, NICS 2020

作者： Bui-Xuan, Bao Nguyen-Hoang, Bao-Minh Truong, Cong-Anh Nguyen-Tran, Quang-Duy Advanced Program in Computer Science Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9780738105536

Human facial expression plays a significant role in the medical field, the automotive industry, and so on. Recent research and achievements in recognizing the expressions by using CNNs have been published, conducted on old datasets, i.e. CKP, FER+, etc. However, those are unnatural and less challenging. We authors propose two methods to deal with a new and more realistic dataset called CAER-S first introduced in ICCV 2019. Instead of using the original images of CAER-S, we prepare our dataset by extracting the faces to focus mainly on facial expressions. The first method is to merge some categories sharing the most mispredictions mutually. Nonetheless, the results are indecisive to conclude this method's efficiency. The other is to use plenty of pre-trained models to find the best three of them for ensembles. The ensembles are weighted majority voting and soft voting. These are applied to fuse the three models' results to return the final one whose accuracy is higher than each's separately. This work contributes to advanced facial expression recognition research, especially with using the new dataset CAER-S. © 2020 IEEE.

关键词： Automotive industry

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