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The Good, The Bad and The Ugly: The Opportunities, Challenges and the Mitigation Strategies of the Young Indigenous Social Media Users of the Chittagong Hill Tracts in Bangladesh 25

The Good, The Bad and The Ugly: The Opportunities, Challenge...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Rofi, Ishmam Bin Eshita, Mashiyat Mahjabin Chakma, Avijoy Ahmed, Md. Sabbir Haque, S M Taiabul Noor, Jannatun Department of Computer Science Brac University Dhaka Bangladesh Computer Science Bowie State University BowieMD United States Department of Computer Science and Engineering Brac University Dhaka Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh C2SG Research Group Brac University Dhaka Bangladesh

ISBN: (纸本)9798400713941

The Chittagong Hill Tracts (CHT) of Bangladesh is home to numerous Indigenous ethnic communities, and their languages, rituals, and values are distinct from those of the mainstream population. These diferences, coupled with the past eight decades of turbulent political history, have contributed to the decline of communal harmony among diferent stakeholders in this region, which has been further aggravated by the advent of social media. In this work, we study the unique challenges faced by Indigenous young community members in Bangladesh when using the social media. Through a qualitative approach involving interviews and focus group discussion sessions, we investigate the online experiences encountered by this population along with their protection and coping mechanisms. Our fndings provide a nuanced portrayal of both the internal and external challenges faced by these users. We further connect our fndings to the broader issues in HCI and ofer a few design recommendations. © 2025 Association for Computing Machinery. All rights reserved.

关键词： Indigenous HCI

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StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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A Comprehensive Review of Flower Classification Techniques Using Deep Learning 4

A Comprehensive Review of Flower Classification Techniques U...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Varshney, Abhilasha Varshney, Sudeep Chaudhary, Ankur Varshney, Abhilasha School of Engineering and Technology Sharda University Department of Computer Science Sharda Greater Noida India DS Group Noida India ABES Engieering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350306118

This paper analyzes deep learning techniques for categorizing flowers, covering new advances, issues, and advancements in this emerging field. As the significance of automated flower identification continues to grow across many domains, deep learning algorithms have emerged as powerful tools for attaining accurate and effective categorization. By combining a variety of papers, methods, and datasets, this study offers a structured overview of the state-of-The-Art in deep learning-based flower categorization. The main concepts, architectures, pre-processing techniques, transfer learning methodology, and performance evaluations employed in diverse research are covered in the article. By examining the benefits, drawbacks, and contrasting assessments of different methods, this research provides helpful insights for academics, practitioners, and enthusiasts interested in employing deep learning for flower categorization. © 2023 IEEE.

关键词： computer vision

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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XMD : An end-To-end framework for interactive explanation-based debugging of NLP models 61

XMD : An end-To-end framework for interactive explanation-ba...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Lee, Dong-Ho Kadakia, Akshen Joshi, Brihi Chan, Aaron Liu, Ziyi Narahari, Kiran Shibuya, Takashi Mitani, Ryosuke Sekiya, Toshiyuki Pujara, Jay Ren, Xiang Department of Computer Science University of Southern California United States RandD Center Sony Group Corporation

ISBN: (纸本)9781959429708

NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model. While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility. Thus, we propose XMD : The first open-source, end-Toend framework for explanation-based model debugging. Given task-or instance-level explanations, users can flexibly provide various forms of feedback via an intuitive, web-based UI. After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanations align with the user feedback. The new model can then be easily deployed into real-world applications via Hugging Face. Using XMD , we can improve the model s OOD performance on text classification tasks by up to 18%.1. © ACL-DEMO 2023. All rights reserved.

关键词： Natural language processing systems

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Feature Extraction in Arabic Sign Language using Hand and Wrist Localization Techniques 7

Feature Extraction in Arabic Sign Language using Hand and Wr...

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7th International Conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Rastogi, Umang Kumar, Sushil Rawat, Gaurav Srmist University Department of Computer Science and Engineering UP Ghaziabad India Kiet Group of Institutions Department of Computer Science and Engineering UP Ghaziabad India Kiet Group of Institutions Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9781665454018

Recognition of hand and wrist gestures is the vital and challenging problems in human computer interaction (HCI) community. In this paper, an essential phase in the process of identifying hand gestures is presented using innovative hand detection and wrist localization method. There has not been much focused to localizing the wrist, and available work are very few which cover a wide range of circumstances. On a publicly available dataset, the suggested approach was implemented, tested and the findings obtained support its effectiveness. By contrasting this method with earlier research, the effectiveness of this method and the significance of the localization with wrist phase has been presented. In this work, we suggest to use proposed method which is applicable for general sign language recognition (SLR) and Arabic digit SLR. The proposed technique (having error = 125 when maximum detection (E) = 1 and error 175 when E = 0.5) exhibits the better results as compeered to the existing method (having error = 131.8 when E = 1 and error 323.3 when E = 0.5). Using this paper, the researchers will explore to investigate novel creation of a real-time Arabic SL application using hand characteristics in order to propose other classifiers to boost the effectiveness of the detection of hands and wrists method. © 2022 IEEE.

关键词： Errors

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DiCLET-TTS: Diffusion Model Based Cross-Lingual Emotion Transfer for Text-to-Speech - A Study Between English and Mandarin

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 3418-3430页

作者： Li, Tao Hu, Chenxu Cong, Jian Zhu, Xinfa Li, Jingbei Tian, Qiao Wang, Yuping Xie, Lei Northwestern Polytechnical University School of Computer Science Xi'an710072 China Tsinghua University Institute for Interdisciplinary Information Sciences Beijing100084 China ByteDance Speech Audio and Music Intelligence Group Shanghai200233 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

While the performance of cross-lingual TTS based on monolingual corpora has been significantly improved recently, generating cross-lingual speech still suffers from the foreign accent problem, leading to limited naturalness. Besides, current cross-lingual methods ignore modeling emotion, which is indispensable paralinguistic information in speech delivery. In this article, we propose DiCLET-TTS, a Diffusion model based Cross-Lingual Emotion Transfer method that can transfer emotion from a source speaker to the intra- and cross-lingual target speakers. Specifically, to relieve the foreign accent problem while improving the emotion expressiveness, the terminal distribution of the forward diffusion process is parameterized into a speaker-irrelevant but emotion-related linguistic prior by a prior text encoder with the emotion embedding as a condition. To address the weaker emotional expressiveness problem caused by speaker disentanglement in emotion embedding, a novel orthogonal projection based emotion disentangling module (OP-EDM) is proposed to learn the speaker-irrelevant but emotion-discriminative embedding. Moreover, a condition-enhanced DPM decoder is introduced to strengthen the modeling ability of the speaker and the emotion in the reverse diffusion process to further improve emotion expressiveness in speech delivery. Cross-lingual emotion transfer experiments show the superiority of DiCLET-TTS over various competitive models and the good design of OP-EDM in learning speaker-irrelevant but emotion-discriminative embedding. © 2014 IEEE.

关键词： Speech synthesis

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Exploring Artificial Neural Network Models for c-VEP Decoding in a Brain-Artificial Intelligence Interface

Exploring Artificial Neural Network Models for c-VEP Decodin...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Miao, Zhengqing Meunier, Anja Žák, Michal Robert Grosse-Wentrup, Moritz Justus Liebig University Giessen Department of Psychology Giessen Germany University of Vienna Doctoral School Computer Science Faculty of Computer Science Vienna Austria University of Vienna Research Group Neuroinformatics Faculty of Computer Science Vienna Austria University of Vienna Vienna Cognitive Science Hub Vienna Austria University of Vienna Research Network Data Science Vienna Austria

ISBN: (纸本)9798350386226

The Conversational Brain-Artificial Intelligence Interface (BAI) is a novel brain-computer interface (BCI) that uses artificial intelligence (AI) to help individuals with severe language impairments communicate. It translates users' broad intentions into coherent, context-specific responses through an advanced AI conversational agent. A critical aspect of intention translation in BAI is the decoding of code-modulated visual evoked potentials (c-VEP) signals. This study evaluates five different artificial neural network (ANN) architectures for decoding c-VEP-based EEG signals in the BAI system, highlighting the efficacy of lightweight, shallow ANN models and pre-training strategies using data from other participants to enhance classification performance. These results provide valuable insights for the application of ANN models in decoding c-VEP-based EEG signals and may benefit other c-VEP-based BCI systems. © 2024 IEEE.

关键词： Chatbots

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