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A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

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IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

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Reuse contracts as component interface descriptions

Reuse contracts as component interface descriptions

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Workshops on Object-Oriented technology, ECOOP 1997

作者： Hondt, Koen De Lucas, Carine Steyaert, Patrick Programming Technology Lab Computer Science Department Vrije Universiteit Brussel Pleinlaan 2 BrusselB-1050 Belgium

ISBN: (纸本)3540640398

Current interface descriptions are poor in describing components, because they only provide an external view on a component and they do not lay down how components interact with each other. Suggestions to improve component interface descriptions at last year’s workshop are reconsidered and reuse contracts are put forward as a solution that goes one step further. © Springer-Verlag Berlin Heidelberg 1998.

关键词： computers

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Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control

arXiv

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arXiv 2025年

作者： Gu, Hankang Wang, Shangbo Jia, Dongyao Zhang, Yuli Luo, Yanrong Mao, Guoqiang Wang, Jianping Lim, Eng Gee School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3GJ United Kingdom Department of Engineering and Design University of Sussex BrightonBN1 9RH United Kingdom School of Advanced Technology China International Business School Suzhou Xi’an Jiaotong-Liverpool University Suzhou215123 China ISN State Key Lab Xidian University Xi’an710126 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute China

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules—GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks—RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Fairness without Demographics through Learning Graph of Gradients 25

Fairness without Demographics through Learning Graph of Grad...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yingtao Luo Zhixun Li Qiang Liu Jun Zhu Carnegie Mellon University Pittsburgh PA USA The Chinese University of Hong Kong Hong Kong China CRIPAC MAIS Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Institute for AI THBI Lab BNRist Center Tsinghua-Bosch Joint ML Center Tsinghua University Beijing China

ISBN: (纸本)9798400712456

Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data quality problems, some demographic information may not be available in the training data and the complex interaction of different demographics can lead to a lot of unknown minority subpopulations, which all limit the applicability of group fairness. Many existing works on fairness without demographics assume the correlation between groups and features. However, we argue that the model gradients are also valuable for fairness without demographics. In this paper, we show that the correlation between gradients and groups can help identify and improve group fairness. With an adversarial weighting architecture, we construct a graph where samples with similar gradients are connected and learn the weights of different samples from it. Unlike the surrogate grouping methods that cluster groups from features and labels as proxy sensitive attribute, our method leverages the graph structure as a soft grouping mechanism, which is much more robust to noises. The results show that our method is robust to noise and can improve fairness significantly without decreasing the overall accuracy too much.

关键词： jun zhu

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Big Learning with Bayesian methods

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National science Review 2017年第4期4卷 627-651页

作者： Jun Zhu Jianfei Chen Wenbo Hu Bo Zhang TNList Lab State Key Lab for Intelligent Technology and Systems CBICR Center Department of Computer Science and Technology Tsinghua University

The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms,systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.

关键词： Big Bayesian Learning Bayesian non-parametrics regularized Bayesian inference scalable algorithms

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Nested mixin-methods in Agora 7th

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7th European Conference on Object-Oriented programming, ECOOP 1993

作者： Steyaert, Patrick Codenie, Wim D’Hondt, Theo De Hondt, Koen Lucas, Carine Van Limberghen, Marc Programming Technology Lab Computer Science Department Vrije Universiteit Brussel Pieinlaan 2 BrusselsB-1050 Belgium

ISBN: (纸本)9783540571209

Mixin-based inheritance is an inheritance technique that has been shown to subsume a variety of different inheritance mechanisms. It is based directly upon an incremental modification model of inheritance. This paper addresses the question of how mixins can be seen as named attributes of classes the same way that objects, methods, and also classes in their own right, are seen as named attributes of classes. The general idea is to let a class itself have control over how it is extended. This results in a powerful abstraction mechanism to control the construction of inheritance hierarchies in two ways. Firstly, by being able to constrain the inheritance hierarchy;secondly, by being able to extend a class in a way that is specific for that class. Nested mixins are a direct consequence of having mixins as attributes. The scope rules for nested mixins are discussed, and shown to preserve the encapsulation of objects. © Springer-Verlag Berlin Heidelberg 1993.

关键词： Artificial intelligence

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Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-trained BERT

arXiv

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arXiv 2025年

作者： Dai, Dongyang Wu, Zhiyong Kang, Shiyin Wu, Xixin Jia, Jia Su, Dan Yu, Dong Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin N.T. Hong Kong Tencent AI Lab. Tencent Shenzhen China

Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end-to-end framework to predict the pronunciation of a polyphonic character, which accepts sentence containing polyphonic character as input in the form of Chinese character sequence without the necessity of any preprocessing. The proposed method consists of a pre-trained bidirectional encoder representations from Transformers (BERT) model and a neural network (NN) based classifier. The pre-trained BERT model extracts semantic features from a raw Chinese character sequence and the NN based classifier predicts the polyphonic character’s pronunciation according to BERT output. In out experiments, we implemented three classifiers, a fully-connected network based classifier, a long short-term memory (LSTM) network based classifier and a Transformer block based classifier. The experimental results compared with the baseline approach based on LSTM demonstrate that, the pre-trained model extracts effective semantic features, which greatly enhances the performance of polyphone disambiguation. In addition, we also explored the impact of contextual information on polyphone disambiguation. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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TReB: Task dependency aware-Resource allocation for Internet of Things using Binary offloading

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Ad Hoc Networks 2025年 177卷

作者： Priyanka Soni Ajay Gajanan Hajare Keerthan Kumar T.G. Sourav Kanti Addya Cloud & Smart System Services Lab Department of Computer Science & Engineering National Institute of Technology Karnataka Surathkal India Department of Information Science & Engineering Siddaganga Institute of Technology Tumkur India

The rapid growth of Internet of Things (IoT) applications in domains such as healthcare, smart homes, and autonomous vehicles has led to an exponential increase in data generated by compute intensive tasks. Efficiently offloading these tasks to nearby computational resources in fog environments remains a significant challenge due to the inherent heterogeneity and constrained resources of Fog Nodes (FNs). Most of the existing approaches fail to address the trade-offs between latency, energy, and resource utilization, particularly when managing dependent and independent task workloads. Moreover, establishing an offloading strategy within a densely interconnected IoT network is known to be N P -hard. To overcome these limitations, in this work, we propose a T ask dependency-Aware Re source allocation for IoT using B inary offloading ( TReB ) framework by considering both independent and dependent tasks of IoT applications. The TReB utilizes the Analytic Hierarchy Process (AHP) technique to generate the preferences of FNs and tasks by considering diverse attributes. With preferences established, a binary offloading is handled through a one-to-many matching procedure, utilizing a Deferred Acceptance Algorithm (DAA). It allows TReB to jointly minimize system energy consumption, latency, and the number of outages in an IoT network. We evaluated the effectiveness of TReB through simulation experiments, and results show that the proposed approach achieves a 49.1%, 62.4%, and 41.7% minimization in overall system latency, energy, and outages compared to the existing baselines.

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A First Look at the Performance Enhancement Potential of Fluid Reconfigurable Intelligent Surface

arXiv

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arXiv 2025年

作者： Salem, Abdelhamid Wong, Kai-Kit Alexandropoulos, George Chae, Chan-Byoung Murch, Ross Department of Electronic and Electrical Engineering University College London London United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Benghazi University Benghazi Libya Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States School of Integrated Technology Yonsei University Seoul03722 Korea Republic of The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

The fluid antenna concept represents shape-flexible and position-flexible antenna technologies designed to enhance wireless communication applications. In this paper, we apply this concept to reconfigurable intelligent surfaces (RISs), introducing fluid RIS (FRIS), where each tunably reflecting element becomes a fluid element with additional position reconfigurability. This new paradigm is referred to as fluid RIS (FRIS). We investigate an FRIS-programmable wireless channel, where the fluid metasurface is divided into non-overlapping subareas, each acting as a fluid element that can dynamically adjust both its position and phase shift of the reflected signal. We first analyze the single-user, single-input single-output (SU-SISO) channel, in which a single-antenna transmitter communicates with a single-antenna receiver via an FRIS. The achievable rate is maximized by optimizing the fluid elements using a particle swarm optimization (PSO)based approach. Next, we extend our analysis to the multi-user, multiple-input single-output (MU-MISO) case, where a multiantenna base station (BS) transmits individual data streams to multiple single-antenna users via an FRIS. In this case, the joint optimization of the positions and phase shifts of the FRIS element, as well as the BS precoding to maximize the sum-rate is studied. To solve the problem, a combination of techniques including PSO, semi-definite relaxation (SDR), and minimum mean square error (MMSE) is proposed. Numerical results demonstrate that the proposed FRIS approach significantly outperforms conventional RIS configurations in terms of achievable rate performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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