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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Fu Yuan, Liangliang Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665401265

Large scale RNA-protein binding data made computational identification of RNA-protein interactions possible, which provides great convenience for revealing the interplay between non-coding RNAs and RNA-binding proteins (RBPs). Various machine learning methods have been applied to the prediction of RNA-protein interactions (RPIs). However, most of the RPI predictors were designed for linear RNAs, while methods for circular RNAs (circRNAs) are few. Besides, most of the predictors use only RNA sequence or only protein sequences as input. Automatic feature learning from RNA and RBP sequences using deep neural networks has great potential to improve RPI predictors. In this paper, we propose a new method, PSi-bind, to predict the binding relationship between RNAs and RBPs, which is featured by a pseudo-Siamese neural network. We feed both RNA and RBP sequence embedding features to the network and perform an end-to-end learning to discover the latent association between RNAs and RBPs. Especially, we train our model on a large-scale circRNA dataset. The experimental results show that the model achieves very high accuracy on independent test set, and outperforms the state-of-the-art RPI predictors by a large margin. © 2021 IEEE.

关键词： RNA

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A Dynamic Online Dashboard for Tracking the Performance of Division 1 Basketball Athletic Performance

A Dynamic Online Dashboard for Tracking the Performance of D...

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Pacific Rim International Symposium on Dependable Computing

作者： Erica Juliano Chelsea Thakkar Christopher Taber Mehul Raval Tolga Kaya Samah Senbel School of Computer Scoence and Engineering Sacred Heart Univeristy CT USA Department of Physical Therapy and Human Movement Science Sacred Heart Univeristy CT USA School of Engineering and Applied Science Ahmedabad University Ahmedabad Gujarat India

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes can be compared to the group averages, while coaches have access to all athletes and can compare them to each other and the team averages for all parameters. A simple color-coded design was utilized to convey the coaches which of the measured parameters is in an acceptable range and which is deficient. The dashboard was reviewed by the athletes, coaches, and exercise scientists and was useful for their needs.

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Discovering Chunks in Neural Embeddings for Interpretability

arXiv

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arXiv 2025年

作者： Wu, Shuchen Alaniz, Stephan Schulz, Eric Akata, Zeynep Explainable Machine Learning Lab Helmholtz Munich Germany Max Planck Institute for Biological Cybernetics Germany Munich Center for Machine Learning Germany Department of Computer Science Technical University of Munich Germany Institute for Human-Centered AI Helmholtz Munich Germany

Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process. © 2025, CC BY.

关键词： Network embeddings

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Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach 4

Predicting Price Direction of Bitcoin based on Hybrid Model ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Karahyla, Jai Kishan Sharma, Neelam Chamoli, Sushant Shirgire, Anil Kant, Ravi Chauhan, Amit Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University Human Rights HaryanaMullana Ambala India Lovely Professional University Physical Education Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr D y Patil Institute of Technology Department of Civil Engineering MaharashtraPimpri Pune India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. © 2023 IEEE.

关键词： Bitcoin

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Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity

Speech AI for All: Promoting Accessibility, Fairness, Inclus...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Wu, Shaomei Wenzel, Kimi Li, Jingjin Li, Qisheng Pradhan, Alisha Kushalnagar, Raja Lea, Colin Koenecke, Allison Vogler, Christian Hasegawa-Johnson, Mark Su, Norman Makoto Ratner, Nan Bernstein *** Mountain ViewCA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States *** SeattleWA United States Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Gallaudet University WashingtonDC United States Apple CupertinoCA United States Cornell University IthacaNY United States Technology Access Program Gallaudet University WashingtonDC United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana-ChampaignIL United States Department of Computational Media University of California Santa Cruz Santa CruzCA United States Department of Hearing and Speech Sciences University of Maryland College Park College ParkMD United States Neuroscience and Cognitive Science University of Maryland College Park College ParkMD United States

ISBN: (纸本)9798400713958

Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain. © 2025 Copyright held by the owner/author(s).

关键词： Speech enhancement

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A hybrid deep learning architecture using 3D CNNs and GRUs for human action recognition

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International Journal of Engineering, Transactions B: Applications 2020年第5期33卷 959-965页

作者： Savadi Hosseini, M. Ghaderi, F. Human-Computer Interaction lab Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Video contents have variations in temporal and spatial dimensions, and recognizing human actions requires considering the changes in both directions. To this end, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and their combinations have been used to tackle the video dynamics. However, a hybrid architecture usually results in a more complex model and hence a greater number of parameters to be optimized. In this study, we propose to use a stack of gated recurrent unit (GRU) layers on top of a two-stream inflated convolutional neural network. Raw frames and optical flow of the video are processed in the first and second streams, respectively. We first segment the video frames in order to be able to track the video contents in more details and by using 3D CNNs extract spatial-temporal features, called local features. We then import the sequence of local features to the GRU network, and use a weighted averaging operator to aggregate the outcome of the two processing flows, called global features. The evaluations confirm acceptable results for the two HMDB51 and UCF101 datasets. The proposed method resulted in a 1.6% improvement in the classification accuracy of the HMDB51 challenging dataset compared to the best reported results. © 2020 Materials and Energy Research Center. All rights reserved.

关键词： Convolutional neural networks

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chuan Tang Xi Yang Bojian Wu Zhizhong Han Yi Chang School of Artificial Intelligence Jilin University China MoE Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China Zhejiang University The Department of Computer Science Wayne State University USA

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

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A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing

arXiv

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arXiv 2023年

作者： Fazeli, Shayan Levine, Lionel Beikzadeh, Mehrab Mirzasoleiman, Baharan Zadeh, Bita Peris, Tara Sarrafzadeh, Majid Computer Science Department Ucla Los Angeles United States Department of Psychology Chapman University Los Angeles United States The Jane and Terry Semel Institute for Neuroscience and Human Behavior Ucla Los Angeles United States

Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing success and maturity, mental health-focused applications have seen comparatively limited success in spite of the fact that stress and anxiety disorders are among the most common issues people deal with in their daily lives. In the hopes of furthering progress in this domain through the development of a more robust analytic framework for the measurement of indicators of mental health, we propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response. Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices and leveraging them to map short-term episodes to semantically efficient embeddings for a given task. Additionally, we leverage an inter-modality contrastive objective, with the advantages of rendering our framework both modular and scalable. The focus on optimizing both local and global aspects of our embeddings via a hierarchical structure renders transferring knowledge and compatibility with other devices easier to achieve. In our pipeline, a task-specific pooling based on an attention mechanism, which estimates the contribution of each modality on an instance level, computes the final embeddings for observations. This additionally provides a thorough diagnostic insight into the data characteristics and highlights the importance of signals in the broader view of predicting episodes annotated per mental health status. We perform training experiments using a corpus of realworld data on perceived stress, and our results demonstrate the efficacy of the proposed approach in performance improvements. © 2023, CC BY-NC-SA.

关键词： Embeddings

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Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation

arXiv

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arXiv 2022年

作者： Wang, Siyu Chen, Xiaocong McAuley, Julian Cripps, Sally Yao, Lina The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia Data61 CSIRO Australia Human Technology Institute University of Technology Sydney Australia Computer Science Department UCSD United States

Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical with commonly sparse user feedback data in the context of recommender systems. To circumvent the lack of interaction of current RL-based recommender systems, we propose to learn a general Model-Agnostic Counterfactual Synthesis (MACS) Policy for counterfactual user interaction data augmentation. The counterfactual synthesis policy aims to synthesise counterfactual states while preserving significant information in the original state relevant to the user's interests, building upon two different training approaches we designed: learning with expert demonstrations and joint training. As a result, the synthesis of each counterfactual data is based on the current recommendation agent's interaction with the environment to adapt to users' dynamic interests. We integrate the proposed policy Deep Deterministic Policy Gradient (DDPG), Soft Actor Critic (SAC) and Twin Delayed DDPG in an adaptive pipeline with a recommendation agent that can generate counterfactual data to improve the performance of recommendation. The empirical results on both online simulation and offline datasets demonstrate the effectiveness and generalisation of our counterfactual synthesis policy and verify that it improves the performance of RL recommendation agents. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Comment to Federal Trade Commission on Disclosure Guidelines and Dark Patterns

SSRN

引用

SSRN 2025年

作者： Burke, Caitlin Gunawan, Johanna King, Jennifer Kshirsagar, Mihir Acquisti, Alessandro Chetty, Marshini Choffnes, David Egelman, Serge Gray, Colin M. Hartzog, Woodrow Im, Jane Mayer, Jonathan Mhaidli, Abraham Narayanan, Arvind Roesner, Franziska Schaffner, Brennan Schaub, Florian Strahilevitz, Lior Tong, Nicole Wang, Charlie Wilson, Christo Zeng, Eric Department of Communication Stanford University United States Khoury College of Computer Sciences Northeastern University United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States Heinz College Carnegie Mellon University United States Department of Computer Science University of Chicago United States Khoury College of Computer Sciences Northeastern University United States United States Purdue University United States Boston University School of Law United States School of Information Division of Computer Science and Engineering University of Michigan United States Princeton University United States School of Information University of Michigan United States Princeton University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States Department of Computer Science University of Chicago United States School of Information University of Michigan United States University of Chicago United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States CyLab Carnegie Mellon University United States Stanford University School of Law United States Princeton University United States

As documented in several research studies, consumers may encounter dark patterns in many online contexts, such as when making choices to consent to the disclosure of personal information or to cookies, when interacting with services and applications like games or content feeds that seek to capture and extend consumer attention and time spent, and in e-commerce, including at multiple points along a purchasing journey. Dark patterns may start with the advertising of a product or service, and can be present across the whole customer path, including sign-up, purchase, and cancellation. As a result, the Commission’s guidance should cover the business’s entire interaction with the consumer to ensure that they are allowed to engage in free and informed transactions. © 2025, The Authors. All rights reserved.

关键词： Sales

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