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arXiv 2023年

作者： Jahanifar, Mostafa Raza, Manahil Xu, Kesi Vuong, Trinh Jewsbury, Rob Shephard, Adam Zamanitajeddin, Neda Kwak, Jin Tae Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir Tissue Image Analytics center Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering Korea University Korea Republic of

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause domain shift (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative domain generalization (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, they are applicable to most medical image analysis tasks as well. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Quantitative Analysis of EEG Neurofeedback using optimized 1-DPSO

Quantitative Analysis of EEG Neurofeedback using optimized 1...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Ms. Kiran Waghmare Meenakshi Malhotra Reeja Sr Center for Development of Advanced Computing Mumbai India Dayananda Sagar University Bengaluru India School of Computer Science and Engineering AP University India

Brain-computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to Neurological and allied sciences. This system measures the activity of the Central Nervous System (CNS) using biosignals and output is called Electroencephalography (EEG). The main purpose of BCI is to acquire EEG brain signals, identify patterns, extract features, and produce resultant actions. This process communicates with a modest electronic system designed for movements of physically challenged or paralyzed people. The purpose of this BCI system is to design a model to check the attention level of body movement. The movements are based on the EEG signals captured from the 19-electrode EEG headset. This allows gaining control over optimized real-time feature selection for EEG signals. The dataset of 30 subjects’ sample EEG signals is recorded for classification and analysis purpose. The EEG signals are classified using Logistic Regression, Decision Tree, and Random Forest algorithms. It is shown that Random Forest is the most efficient classifier with the highest accuracy of 99.47%.

关键词： Signal processing algorithms Feature extraction Brain modeling Prediction algorithms Electroencephalography Real-time systems Classification algorithms

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Dynamic Scene Adjustment for Player Engagement in VR Game

arXiv

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arXiv 2023年

作者： Liu, Zhitao Li, Yi Xie, Ning Fan, Youteng Tang, Haolan Zhang, Wei AVIC Chengdu Aircraft Design & Research Institute Chengdu China Center for Future Media the School of Computer Science and Engineering UESTC Chengdu China

Virtual reality (VR) produces a highly realistic simulated environment with controllable environment variables. This paper proposes a Dynamic Scene Adjustment (DSA) mechanism based on the user interaction status and performance, which aims to adjust the VR experiment variables to improve the user's game engagement. We combined the DSA mechanism with a musical rhythm VR game. The experimental results show that the DSA mechanism can improve the user's game engagement (task performance). Copyright © 2023, The Authors. All rights reserved.

关键词： Virtual reality

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Improving social media use for disaster resilience: challenges and strategies

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International Journal of Digital Earth 2023年第1期16卷 3023-3044页

作者： Nina S.N.Lam Michelle Meyer Margaret Reams Seungwon Yang Kisung Lee Lei Zou Volodymyr Mihunov Kejin Wang Ryan Kirby Heng Cai Department of Environmental Sciences Louisiana State UniversityBaton RougeUSA Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationUSA School of Information Studies Center for Computation and TechnologyLouisiana State UniversityBaton RougeUSA Division of Computer Science and Engineering Louisiana State UniversityBaton RougeUSA Department of Geography Texas A&M UniversityCollege StationUSA Escambia County Natural Resources Management PensacolaFloridaUSA

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and *** framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and *** paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive ***,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of ***,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.

关键词： Twitter use challenges and strategies disaster resilience social and geographical disparities emergency rescue Hurricane Isaac Hurricane Sandy Hurricane Harvey

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Complex adaptive systems science in the era of global sustainability crisis

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Geography and Sustainability 2025年第1期6卷 14-24页

作者： Li An B.L.Turner II Jianguo Liu Volker Grimm Qi Zhang Zhangyang Wang Ruihong Huang College of Forestry Wildlifeand EnvironmentAuburn University602 Duncan DriveAuburnAL 36849USA International Center for Climate and Global Change Research Auburn University602 Duncan DriveAuburnAL 36849USA School of Geographical Sciences and Urban Planning&School of Sustainability Arizona State UniversityPO Box 875302TempeAZ 85287-5302USA Center for Systems Integration and Sustainability Michigan State UniversityEast LansingMI 48823-5243USA Helmholtz Centre for Environmental Research-UFZ Department of Ecological ModellingPermoserstr.1504318 LeipzigGermany Department of Geography and Environment University of North Carolina at Chapel HillChapel HillNC 27599USA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTX 78712USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA

A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and *** science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such *** address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling *** intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.

关键词： Social-environmental systems Complex adaptive systems Sustainability science Agent-based models Artificial intelligence Data science

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario

arXiv

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arXiv 2023年

作者： Fluss, Noam Hacohen, Guy Weinshall, Daphna School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Edmond & Lily Safra Center for Brain Sciences The Hebrew University of Jerusalem Israel

Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that labeled and unlabeled data share the same label space. However, in practical real-world scenarios, especially when the labeled training dataset is limited in size, some classes may be totally absent from the labeled set. To address this broader context, we propose a general approach to augment existing SSL methods, enabling them to effectively handle situations where certain classes are missing. This is achieved by introducing an additional term into their objective function, which penalizes the KL-divergence between the probability vectors of the true class frequencies and the inferred class frequencies. Our experimental results reveal significant improvements in accuracy when compared to state-of-the-art SSL, open-set SSL, and open-world SSL methods. We conducted these experiments on two benchmark image classification datasets, CIFAR-100 and STL-10, with the most remarkable improvements observed when the labeled data is severely limited, with only a few labeled examples per class © 2023, CC BY.

关键词： Classification (of information)

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Towards Generic and Controllable Attacks Against Object Detection

arXiv

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arXiv 2023年

作者： Li, Guopeng Xu, Yue Ding, Jian Xia, Gui-Song The School of Computer Science Wuhan University Wuhan430079 China The School of Cyber Science and Engineering Wuhan University Wuhan430079 China The State Key Lab LIESMARS Wuhan University Wuhan430079 China The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Wuhan University Wuhan430072 China

Existing adversarial attacks against Object Detectors (ODs) suffer from two inherent limitations. Firstly, ODs have complicated meta-structure designs, hence most advanced attacks for ODs concentrate on attacking specific detector-intrinsic structures, which makes it hard for them to work on other detectors and motivates us to design a generic attack against ODs. Secondly, most works against ODs make Adversarial Examples (AEs) by generalizing image-level attacks from classification to detection, which brings redundant computations and perturbations in semantically meaningless areas (e.g., backgrounds) and leads to an emergency for seeking controllable attacks for ODs. To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations. For a detector-agnostic attack, LGP tracks high-quality proposals and optimizes three heterogeneous losses simultaneously. In this way, we can fool the crucial components of ODs with a part of their outputs without the limitations of specific structures. Regarding controllability, we establish an object-wise constraint that exploits foreground-background separation adaptively to induce the attachment of perturbations to foregrounds. Experimentally, the proposed LGP successfully attacked sixteen state-of-the-art object detectors on MS-COCO and DOTA datasets, with promising imperceptibility and transferability obtained. Codes are publicly released in https://***/liguopeng0923/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Security and Privacy Concerns in “Communication Systems” Blockchain Technology and the Internet of Things

Security and Privacy Concerns in “Communication Systems” B...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia TURKIYE Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management(GLBITM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems; however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT.

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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