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BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-31页

作者： Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner. © 2023 Copyright held by the owner/author(s).

关键词： Visualization

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Image Emotion Classification network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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Digital Communications and networks

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Digital Communications and networks 2022年第3期8卷 235-236页

作者： Ruidong Li Shiwen Mao Periklis Chatzimisios Constandinos X.Mavromoustakis Intelligent computation and network laboratory(ICNL)at Kanazawa University Japan the Wireless Engineering Research and Education Center(WEREC) Department of Information and Electronic Engineering at the International Hellenic University(Greece) Constandinos X.Mavromoustakis is currently a Professor at the Department of Computer Science at the University of Nicosia Cyprus

Guest editorial The emerging applications,suchas Augmented and Virtual Realities(AR/VR),InternetofThings(IoT),4K/8Kstreaming,raisestrongrequirementsto movecomputationfrom thecloudtotheedgestobecloser *** are tremendous possibilities for the network edge,which may includeavariety ofentities,such as small datacenters,end devices,and resource-abundant network *** together provide the network computation and intelligence to users.

关键词： network IoT Communications

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural networks

A New Method for Predefined-Time Synchronization Control of ...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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Hiding information in a Reordered Codebook Using Pairwise Adjustments in Codewords 5

Hiding Information in a Reordered Codebook Using Pairwise Ad...

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5th International Conference on computer Vision and Computational Intelligence, CVCI 2024

作者： Chang, Chin-Chen Liu, Jui-Chuan Chang, Ching-Chun Lin, Yijie Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan Information and Communication Security Research Center Feng Chia University Taichung407 Taiwan

ISBN: (数字)9781510680067

ISBN: (纸本)9781510680050

The fundamental data structure of our proposed scheme is a pixel pair. The pixel values are adjusted through simple yet well-designed equations depending on different conditions. The computation method provides an easy recovery of the original values for the pixel pair as well. For some applications, it’s important to extract the hiding data as well as to recover the original media. In order to apply to a wider range of applications, more and more reversible data hiding (RDH) schemes are introduced or enhanced in the recent decades. Therefore, the two goals of the novel scheme proposed are to increase embedding capacity and to recover original media easily. For vector quantization (VQ) compression, it’s a well-known and efficient method to compress images. A well-trained codebook is crucial to maintain the visual quality of recovered images as good as possible. An index table is generated based on the codebook to full-fill the purpose of compression. Most of studies utilize index tables as the embedding carriers for VQ or VQ-variant compression methods. There are a few state-of-the-art schemes make use of codebooks to embed secret and our proposed scheme extends the study on the codebook embedding. The codewords in a codebook are divided into pixel pairs and secret bits are embedded by adjusting the pixel values of these split pairs to form a stego codebook. During extraction, secret bits are extracted first out from the stego codebook and a recovered codebook is obtained before recovering VQ compressed images. © 2024 SPIE.

关键词： Pixels

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A 16 nm 140 TOPS/W 5 μj/Inference Keyword Spotting Engine Based on 1D-BCNN

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第12期70卷 4564-4568页

作者： Lin, Tay-Jyi Ting, Yi-Hsuan Hsu, Meng-Ze Lin, Kuan-Han Huang, Chung-Ming Tsai, Fu-Cheng Sheu, Shyh-Shyuan Chang, Shih-Chieh Yeh, Chingwei Wang, Jinn-Shyan SoC Research Center AIM-HI Department of Computer Science and Information Engineering Chiayi621 Taiwan SoC Research Center AIM-HI Department of Electrical Engineering Chiayi621 Taiwan Hsinchu300 Taiwan Hsinchu310 Taiwan

This brief presents an event-driven keyword spotting (KWS) system for reducing the significant but usually ignored energy dissipations on the 'always-on' A/D converter and microphone. 'Low energy per inference' and 'fast responsiveness' are new design goals of such KWS engine. A 7-layer 1-dimensional binarized convolutional neural network (1D-BCNN) was designed to achieve 95% inference accuracy for detecting 10 keywords, plus silence and unknown, from raw speech, and 64 32-element signed binary inner product units were allocated in the engine to deliver the 4,096 operations/cycle maximum throughput. The 16nm implementation consumes only 0.1mm2 silicon area and 5μJ /inference energy (including memory accesses), while achieving 1.72ms response time. The performance is comparable to state-of-the-art KWS designs without sacrificing number of detectable keywords or inference accuracy. © 2004-2012 IEEE.

关键词： Energy efficiency

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Segmentation for Colorectal cancer histopathological images analysis

Image Segmentation for Colorectal cancer histopathological i...

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2022 IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2022

作者： Wu, Meng-Ling Chang, Jui-Hung Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan Taiwan National Cheng Kung University Computer and Network Center Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (数字)9781665494915

ISBN: (纸本)9781665494915

Colorectal cancer (CRC) is the third most common malignancy and the second most deadly cancer. The most efficient way to determine CRC staging is to analyze whole slide digital pathology images;therefore, it is certainly important to ensure the accuracy of pathology slide *** can obtain medical quantized data of pathological images by implementing deep learning methods. These methods not only can light pathologists' load but also can provide accurate computing *** this paper, we use U-2-NET as our backbone to perform Binary Image Segmentation on CRC pathology slides. CRC pathology slides have a variety of non-conforming shapes and colors which is an enormous challenge for detecting cancer areas. U-2-NET was originally used in the Salient Object Detection (SOD) task to find the most unique regions of human attention, which can be used to identify abnormal regions in pathological slices. Moreover, the RSU block of U-2-NET can handle long-term and short-term dependencies, which we believe helps maintain contextual information. With the large computational costs, U-2-NET is hard to implement for application. Our purposed method can use preprocessing, image-selecting mechanisms and transfer learning concepts to solve this *** results show that the model trained with a small part of the data set and a modified small object function has the best results for Binary Image Segmentation of colorectal cancer pathology sections by U-2-NET, with the best IOU (0.77) and Dice Loss (0.83) compared with other models (MSRFCNN, FCN, SegNet, and Unet). Furthermore, after transferring learning using pre-trained weights from the SOD dataset, the results are improved compared to those of learning the network from scratch. © 2022 IEEE.

关键词： Pathology

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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ElectrodeNet - A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 346-357页

作者： Huang, Enoch Hsin-Ho Chao, Rong Tsao, Yu Wu, Chao-Min Department of Electrical Engineering National Central University Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701401 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman's rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy. © 2023 IEEE.

关键词： Cochlear implants

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