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Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning

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arXiv 2022年

作者： Wu, Bohong Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Though offering amazing contextualized token-level representations, current pre-trained language models actually take less attention on acquiring sentence-level representation during its self-supervised pre-training. If self-supervised learning can be distinguished into two subcategories, generative and contrastive, then most existing studies show that sentence representation learning may more benefit from the contrastive methods but not the generative methods. However, contrastive learning cannot be well compatible with the common token-level generative self-supervised learning, and does not guarantee good performance on downstream semantic retrieval tasks. Thus, to alleviate such obvious inconveniences, we instead propose a novel generative self-supervised learning objective based on phrase reconstruction. Empirical studies show that our generative learning may yield powerful enough sentence representation and achieve performance in Sentence Textual Similarity (STS) tasks on par with contrastive learning. Further, in terms of unsupervised setting, our generative method outperforms previous state-of-the-art SimCSE on the benchmark of downstream semantic retrieval tasks. © 2022, CC BY.

关键词： Semantics

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

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arXiv 2023年

作者： Lee, Daeun Son, Sejung Jeon, Hyolim Kim, Seungbae Han, Jinyoung Dept. of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Dept. of Applied Artificial Intelligence Dept. of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Computer Science and Engineering College of Engineering University of South Florida TampaFL United States

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Gig2Gether: Datasharing to Empower, Unify and Demystify Gig Work 25

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Gig2Gether: Datasharing to Empower, Unify and Demystify Gig ...

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

作者： Hsieh, Jane Zhang, Angie Surati, Sajel Xie, Sijia Ayala, Yeshua Sathiya, Nithila Kuo, Tzu-Sheng Lee, Min Kyung Zhu, Haiyi Software and Societal Systems Department Carnegie Mellon University PittsburghPA United States University of Texas at Austin AustinTX United States Bowdoin College BrunswickME United States Carnegie Mellon University PittsburghPA United States Washington University in St. Louis St. LouisMO United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States School of Information University of Texas at Austin AustinTX United States

ISBN: (纸本)9798400713941

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future use cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact. © 2025 Copyright held by the owner/author(s).

关键词： Empowerment of personnel

Steps Towards Satisficing Distributed Dynamic Team Trust

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arXiv 2023年

作者： Hunt, Edmund R. Baber, Chris Sobhani, Mehdi Milivojevic, Sanja Yusuf, Sagir Musolesi, Mirco Waterson, Patrick Maynard, Sally School of Engineering Mathematics and Technology University of Bristol United Kingdom School of Computer Science University of Birmingham United Kingdom Bristol Digital Futures Institute University of Bristol United Kingdom Department of Computer Science University College London United Kingdom Human Factors and Complex Systems Group Loughborough University United Kingdom

Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains. Team members should be trusted to work towards agreed goals and in accordance with shared values. In this paper, our concern is with the definition of goals and values such that it is possible to define ‘trust’ in a way that is interpretable, and hence usable, by both humans and robots. We argue that the outcome of team activity can be considered in terms of ‘goal’, ‘individual/team values’, and ‘legal principles’. We question whether alignment is possible at the level of ‘individual/team values’, or only at the ‘goal’ and ‘legal principles’ levels. We argue for a set of metrics to define trust in human-robot teams that are interpretable by human or robot team members, and consider an experiment that could demonstrate the notion of ‘satisficing trust’ over the course of a simulated mission. © 2023, CC BY.

关键词： Robots

Sentence Representation Learning with Generative Objective rather than Contrastive Objective

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arXiv 2022年

Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training. However, contrastive objectives which dominate the current sentence representation learning bring little linguistic interpretability and no performance guarantee on downstream semantic tasks. We instead propose a novel generative self-supervised learning objective based on phrase reconstruction. To overcome the drawbacks of previous generative methods, we carefully model intra-sentence structure by breaking down one sentence into pieces of important phrases. Empirical studies show that our generative learning achieves powerful enough performance improvement and outperforms the current state-of-the-art contrastive methods not only on the STS benchmarks, but also on downstream semantic retrieval and reranking tasks. Our code is available at https://***/chengzhipanpan/PaSeR. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

Research on Autonomous Robots Navigation based on Reinforcement Learning

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arXiv 2024年

作者： Wang, Zixiang Yan, Hao Wang, Yining Xu, Zhengjia Wang, Zhuoyue Wu, Zhizhong College of Engineering and Computer Science Syracuse University SyracuseNY United States Human Factors in Information Design Bentley University WalthamMA United States SATM Cranfield University Cranfield United Kingdom Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Mountain View CA United States

Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become one of the key methods to achieve autonomous navigation of robots. In this work, an autonomous robot navigation method based on reinforcement learning is introduced. We use the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback. By combining the Q-value function with the deep neural network, deep Q network can handle high-dimensional state space, so as to realize path planning in complex environments. Proximal policy optimization is a strategy gradient-based method, which enables robots to explore and utilize environmental information more efficiently by optimizing policy functions. These methods not only improve the robot's navigation ability in the unknown environment, but also enhance its adaptive and self-learning capabilities. Through multiple training and simulation experiments, we have verified the effectiveness and robustness of these models in various complex scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store

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arXiv 2023年

作者： Yang, Biqi Tang, Weiliang Gao, Xiaojie Li, Xianzhi Liu, Yun-Hui Fu, Chi-Wing Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China The School of Computer Science and Technology Huazhong University of Science and Technology China The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain. Existing instance segmentation methods typically rely on a tedious process of scene collection, mask annotation, and network fine-tuning for every single Stock Keeping Unit (SKU). This paper presents SKU-Patch, a new patch-guided instance segmentation solution, leveraging only a few image patches for each incoming new SKU to predict accurate and robust masks, without tedious manual effort and model re-training. Technical-wise, we design a novel transformer-based network with (i) a patch-image correlation encoder to capture multi-level image features calibrated by patch information and (ii) a patch-aware transformer decoder with parallel task heads to generate instance masks. Extensive experiments on four storehouse benchmarks manifest that SKU-Patch is able to achieve the best performance over the state-of-the-art methods. Also, SKU-Patch yields an average of nearly 100% grasping success rate on more than 50 unseen SKUs in a robot-aided auto-store logistic pipeline, showing its effectiveness and practicality. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Commissioning Development: Grantmaking, Community Voices, and their Implications for ICTD 2022

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Commissioning Development: Grantmaking, Community Voices, an...

International Conference on Information & Communication Technologies and Development 2022

作者： Manika Saha Tom Bartindale Sharifa Sultana Gillian Oliver Dan Richardson Shakuntala Haraksingh Thilsted Syed Ishtiaque Ahmed Patrick Olivier Action Lab Human-centric computing Faculty of IT Monash University Australia Department of Information Science Cornell University USA Faculty of IT Monash University Australia Global Public Health Nutrition WorldFish Malaysia Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781450397872

While information and communication technology for development (ICTD) researchers have prioritized advocating for community voices in innovation design and development, we have limited insights into how community voices are incorporated by the high-level decision-makers who fund and initiate development projects and programs in the Global South. Indeed, understanding local communities’ voices (expressions of needs, challenges, and priorities) in tailoring effective development projects for sustainable development is widely considered an unmet goal. Using a qualitative survey of eight decision-makers (including grantmaking donors, central governments and INGOs) we explored a number of key factors, including national and global political climates, insider-outsider interactions, and evidence-based approaches that influence the high-level decision making process, workflows, and perceptions of community voice in project commissioning within Bangladesh’s public health nutrition development arena. Our findings reveal the tensions that arise among high-level decision-makers, and highlight the challenges associated with connecting with communities during development project design and implementation. We suggest broader implications and design opportunities for inventive project commissioning approaches to bridge the gap between communities and decision-makers. Our findings are of potential value for ICTD and HCI4D researchers interested in sustainable innovation and understanding and participating in the complex workflows of the project commissioning process in sustainable global development.

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Automatic detection and classification algorithm of thyroid nodules in CT images based on YOLOv5s

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Automatic detection and classification algorithm of thyroid ...

International Conference on computer and Communications (ICCC)

作者： Ruifeng Guo Lei Xu Jiahui Guo Hongliang Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Department of Endocrinology and Metabolism The First Hospital of China Medical University Shenyang China

Thyroid nodular lesions are one of the most common diseases in the population. As the number of patients increases, it is clinically important to provide physicians with an accurate early aid to determine the benignity and malignancy of thyroid nodules in order to avoid unnecessary surgery and reduce patient burden. In this paper, we propose an improved Yolov5s-based target detection algorithm for benign and malignant thyroid nodules for thyroid nodule classification and identification. Replacing the backbone network of Yolov5s with ResNet18 to reduce the complexity of the model and the number of parameters; introducing the CBAM attention mechanism into the Yolov5s algorithm to improve the ability to extract features; Modify the spatial pyramid pooling module SPP to SimSPPF to improve the training speed of the algorithm; replace GIoU Loss with CIoU-Focal Loss as the loss function of the algorithm to improve the localization accuracy while increasing the regression rate of the bounding box. The experimental results show that the improved Yolov5s achieved an average accuracy of 92.9%, which is 2.9 percentage points higher than the average accuracy of the original algorithm, and can effectively improve the accuracy and efficiency of nodule detection.

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