arXiv subjects

Jing Deng

Publications and source records attributed to Jing Deng.

At least 19 recordsLinked to original sources

arXiv2025

Personality-Enhanced Multimodal Depression Detection in the Elderly

This paper presents our solution to the Multimodal Personality-aware Depression Detection (MPDD) challenge at ACM MM 2025. We propose a multimodal depression detection model in the Elderly that incorporates personality characteristics. We introduce a multi-feature fusion approach based on a co-attention mechanism to effectively integrate LLDs, MFCCs, and Wav2Vec features in the audio modality. For the video modality, we combine representations extracted from OpenFace, ResNet, and DenseNet to construct a comprehensive visual feature set. Recognizing the critical role of personality in depression detection, we design an interaction module that captures the relationships between personality traits and multimodal features. Experimental results from the MPDD Elderly Depression Detection track demonstrate that our method significantly enhances performance, providing valuable insights for future research in multimodal depression detection among elderly populations.

cs.SD↗

arXiv2025

Electrically controlled topological interface modes in graphene-based photonic superlattices

We demonstrate the electrical control of topological interface modes at the interface between a graphene-based photonic superlattice and a uniform dielectric medium. Specifically, by integrating graphene sheets into the unit cell of metallodielectric superlattices, the presence or absence of topological interface modes can be dynamically controlled by tuning the permittivity of graphene via electrical gating. These topological modes emerge when the spatial average of the permittivity of the superlattices is negative and vanish as the chemical potential of graphene is adjusted to render the averaged permittivity positive. The dependence of the existence of topological interface modes on the sign of the spatial average of the permittivity is fundamentally related to the emergence of a Dirac point, which arises when the averaged permittivity of the superlattices reaches zero and is accompanied by the Zak phase transition, thus resulting in the appearance and disappearance of topological interface modes. Furthermore, we find that the propagation constant of topological interface modes decreases when increasing the chemical potential of graphene. The robustness of such topological interface modes is also demonstrated. Our work provides clear physical insights and offers a promising approach to the dynamic control of topological interface modes.

physics.optics↗

arXiv2025

LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT

Internet of Things (IoT) devices constantly generate heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated learning and lifelong learning. However, the extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks. This problem is exacerbated by the single point of failure. Furthermore, the single point of trust created by the central server hinders reliable auditing for long-term threats. Blockchain technology provides a tamper-proof foundation for trustworthy FLL. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on resource-constrained IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning with minimal on-chain disclosure and bidirectional verification. LiFeChain is the first blockchain tailored for FLL. It incorporates two complementary mechanisms: the Proof-of-Model-Correlation (PoMC) consensus on the server, which couples learning and unlearning mechanisms to mitigate negative transfer; and Segmented Zero-knowledge Arbitration (Seg-ZA) at the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is a plug-and-play component that can be seamlessly integrated into existing FLL algorithms for IoT applications. To demonstrate its practicality and performance, we implement LiFeChain in representative FLL algorithms with Hyperledger Fabric under 6 attacks. Theoretical analysis and extensive evaluations demonstrate that LiFeChain effectively mitigates long-term attacks, and significantly reduces latency and storage overhead compared to state-of-the-art blockchain solutions.

cs.CR↗

arXiv2025

Enhancing Speech Emotion Recognition with Multi-Task Learning and Dynamic Feature Fusion

This study investigates fine-tuning self-supervised learn ing (SSL) models using multi-task learning (MTL) to enhance speech emotion recognition (SER). The framework simultane ously handles four related tasks: emotion recognition, gender recognition, speaker verification, and automatic speech recog nition. An innovative co-attention module is introduced to dy namically capture the interactions between features from the primary emotion classification task and auxiliary tasks, en abling context-aware fusion. Moreover, We introduce the Sam ple Weighted Focal Contrastive (SWFC) loss function to ad dress class imbalance and semantic confusion by adjusting sam ple weights for difficult and minority samples. The method is validated on the Categorical Emotion Recognition task of the Speech Emotion Recognition in Naturalistic Conditions Chal lenge, showing significant performance improvements.

cs.SD↗

arXiv2025

Mitigating Category Imbalance: Fosafer System for the Multimodal Emotion and Intent Joint Understanding Challenge

This paper presents Fosafer approach to the Track 2 Mandarin in the Multimodal Emotion and Intent Joint Understandingchallenge, which focuses on achieving joint recognition of emotion and intent in Mandarin, despite the issue of category imbalance. To alleviate this issue, we use a variety of data augmentation techniques across text, video, and audio modalities. Additionally, we introduce the SampleWeighted Focal Contrastive loss, designed to address the challenges of recognizing minority class samples and those that are semantically similar but difficult to distinguish. Moreover, we fine-tune the Hubert model to adapt the emotion and intent joint recognition. To mitigate modal competition, we introduce a modal dropout strategy. For the final predictions, a plurality voting approach is used to determine the results. The experimental results demonstrate the effectiveness of our method, which achieves the second-best performance in the Track 2 Mandarin challenge.

cs.SD↗

arXiv2025

Speech Emotion Recognition Using Fine-Tuned DWFormer:A Study on Track 1 of the IERPChallenge 2024

The field of artificial intelligence has a strong interest in the topic of emotion recognition. The majority of extant emotion recognition models are oriented towards enhancing the precision of discrete emotion label prediction. Given the direct relationship between human personality and emotion, as well as the significant inter-individual differences in subjective emotional expression, the IERP Challenge 2024 incorporates personality traits into emotion recognition research. This paper presents the Fosafer submissions to the Track 1 of the IERP Challenge 2024. This task primarily concerns the recognition of emotions in audio, while also providing text and audio features. In Track 1, we utilized exclusively audio-based features and fine-tuned a pre-trained speech emotion recognition model, DWFormer, through the integration of data augmentation and score fusion strategies, thereby achieving the first place among the participating teams.

cs.SD↗

arXiv2025

Low-loss terahertz negative curvature suspended-core fiber

Inspired by the design concept of negative curvature hollow-core fibers, this paper presents an innovative negative curvature suspended-core THz fiber. Compared to traditional suspended-core fibers, all structural units of this fiber are designed with circular boundaries, effectively avoiding the issues of insufficient mechanical strength and manufacturing difficulties associated with the wide and thin rectangular support arms in traditional structures. The numerical simulation using the full-vector finite element method shows that the optical fiber loss is as low as 0.02cm-1 in 0.66-1.09THz, and the low loss bandwidth is 0.43THz. In addition, near-zero flat dispersion of -0.08-0.74 ps/THz/cm can be achieved. The fiber exhibits excellent characteristics of low loss, wide bandwidth, and low dispersion, theoretically opening a new research path for the design of low-loss THz fibers.

physics.optics↗

arXiv2025

Leveraging LLM for Stuttering Speech: A Unified Architecture Bridging Recognition and Event Detection

The performance bottleneck of Automatic Speech Recognition (ASR) in stuttering speech scenarios has limited its applicability in domains such as speech rehabilitation. This paper proposed an LLM-driven ASR-SED multi-task learning framework that jointly optimized the ASR and Stuttering Event Detection (SED) tasks. We proposed a dynamic interaction mechanism where the ASR branch leveraged CTC-generated soft prompts to assist LLM context modeling, while the SED branch output stutter embeddings to enhance LLM comprehension of stuttered speech. We incorporated contrastive learning to strengthen the discriminative power of stuttering acoustic features and applied Focal Loss to mitigate the long-tailed distribution in stuttering event categories. Evaluations on the AS-70 Mandarin stuttering dataset demonstrated that our framework reduced the ASR character error rate (CER) to 5.45% (-37.71% relative reduction) and achieved an average SED F1-score of 73.63% (+46.58% relative improvement).

cs.SD↗

arXiv2025

Overlap-Adaptive Hybrid Speaker Diarization and ASR-Aware Observation Addition for MISP 2025 Challenge

This paper presents the system developed to address the MISP 2025 Challenge. For the diarization system, we proposed a hybrid approach combining a WavLM end-to-end segmentation method with a traditional multi-module clustering technique to adaptively select the appropriate model for handling varying degrees of overlapping speech. For the automatic speech recognition (ASR) system, we proposed an ASR-aware observation addition method that compensates for the performance limitations of Guided Source Separation (GSS) under low signal-to-noise ratio conditions. Finally, we integrated the speaker diarization and ASR systems in a cascaded architecture to address Track 3. Our system achieved character error rates (CER) of 9.48% on Track 2 and concatenated minimum permutation character error rate (cpCER) of 11.56% on Track 3, ultimately securing first place in both tracks and thereby demonstrating the effectiveness of the proposed methods in real-world meeting scenarios.

cs.SD↗

arXiv2025

The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning

Federated Learning (FL) has been introduced as a way to keep data local to clients while training a shared machine learning model, as clients train on their local data and send trained models to a central aggregator. It is expected that FL will have a huge implication on Mobile Edge Computing, the Internet of Things, and Cross-Silo FL. In this paper, we focus on the widely used FedAvg algorithm to explore the effect of the number of clients in FL. We find a significant deterioration of learning accuracy for FedAvg as the number of clients increases. To address this issue for a general application, we propose a method called Knowledgeable Client Insertion (KCI) that introduces a very small number of knowledgeable clients to the MEC setting. These knowledgeable clients are expected to have accumulated a large set of data samples to help with training. With the help of KCI, the learning accuracy of FL increases much faster even with a normal FedAvg aggregation technique. We expect this approach to be able to provide great privacy protection for clients against security attacks such as model inversion attacks. Our code is available at https://github.com/Eleanor-W/KCI_for_FL.

cs.LG↗

arXiv2025

SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models

Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in a so-called speculation phase, which are subsequently verified by the LLM in a verification phase. However, current state-of-the-art speculative decoding approaches have three key limitations: handling requests with varying difficulty using homogeneous SSMs, lack of robust support for batch processing, and insufficient holistic optimization for both speculation and verification phases. In this paper, we introduce SPIN, an efficient LLM inference serving system based on speculative decoding, designed to address these challenges through three main innovations. First, SPIN improves token speculation by using multiple heterogeneous SSMs, with a learning-based algorithm for SSM selection that operates without prior knowledge of request difficulty. Second, SPIN employs a request decomposition method to minimize batching overhead during LLM verification. Finally, SPIN orchestrates speculation and verification phases by pipelining their executions on GPUs to achieve further acceleration. Experimental results demonstrate that SPIN significantly outperforms state-of-the-art methods, achieving a performance increase of approximately 2.28X.

cs.DC↗

arXiv2024

Large enhancement of nonlinear optical response of graphene nanoribbon heterojunctions with multiple topological interface states

We investigate the nonlinear optical response of graphene nanoribbon (GNR) heterojunctions both without and with one or multiple topological interface states. By implementing a distant-neighbor quantum-mechanical (DNQM) method, we demonstrate a pronounced enhancement of the nonlinear optical response of GNR heterojunctions as the number of topological states at their interfaces increases. Specifically, we find that GNR heterojunctions with multiple topological interface states exhibit a notably stronger third-order nonlinear optical response in comparison with the similarly sized counterparts with a single topological interface state or without such states. Furthermore, we observe that the presence of topological interface states in GNR heterojunctions can induce a significant red-shift in their quantum plasmon frequency. Our results reveal the potential to enhance the nonlinear optical response at the nanoscale by increasing the number of topological interface states in graphene nanostructures or other topological systems.

cond-mat.mes-hall↗

arXiv2023

6G Non-Terrestrial Networks Enabled Low-Altitude Economy: Opportunities and Challenges

The unprecedented development of non-terrestrial networks (NTN) utilizes the low-altitude airspace for commercial and social flying activities. The integration of NTN and terrestrial networks leads to the emergence of low-altitude economy (LAE). A series of LAE application scenarios are enabled by the sensing, communication, and transportation functionalities of the aircrafts. The prerequisite technologies supporting LAE are introduced in this paper, including the network coverage and aircrafts detection. The LAE functionalities assisted by aircrafts with respect to sensing and communication are then summarized, including the terrestrial and non-terrestrial targets sensing, ubiquitous coverage, relaying, and traffic offloading. Finally, several future directions are identified, including aircrafts collaboration, energy efficiency, and artificial intelligence enabled LAE.

cs.IT↗

arXiv2023

HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data

The correlation between children's personal and family characteristics (e.g., demographics and socioeconomic status) and their physical and mental health status has been extensively studied across various research domains, such as public health, medicine, and data science. Such studies can provide insights into the underlying factors affecting children's health and aid in the development of targeted interventions to improve their health outcomes. However, with the availability of multiple data sources, including context data (i.e., the background information of children) and motion data (i.e., sensor data measuring activities of children), new challenges have arisen due to the large-scale, heterogeneous, and multimodal nature of the data. Existing statistical hypothesis-based and learning model-based approaches have been inadequate for comprehensively analyzing the complex correlation between multimodal features and multi-dimensional health outcomes due to the limited information revealed. In this work, we first distill a set of design requirements from multiple levels through conducting a literature review and iteratively interviewing 11 experts from multiple domains (e.g., public health and medicine). Then, we propose HealthPrism, an interactive visual and analytics system for assisting researchers in exploring the importance and influence of various context and motion features on children's health status from multi-level perspectives. Within HealthPrism, a multimodal learning model with a gate mechanism is proposed for health profiling and cross-modality feature importance comparison. A set of visualization components is designed for experts to explore and understand multimodal data freely. We demonstrate the effectiveness and usability of HealthPrism through quantitative evaluation of the model performance, case studies, and expert interviews in associated domains.

cs.HC↗

arXiv2023

FedIN: Federated Intermediate Layers Learning for Model Heterogeneity

Federated learning (FL) facilitates edge devices to cooperatively train a global shared model while maintaining the training data locally and privately. However, a common assumption in FL requires the participating edge devices to have similar computation resources and train on an identical global model architecture. In this study, we propose an FL method called Federated Intermediate Layers Learning (FedIN), supporting heterogeneous models without relying on any public dataset. Instead, FedIN leverages the inherent knowledge embedded in client model features to facilitate knowledge exchange. The training models in FedIN are partitioned into three distinct components: an extractor, intermediate layers, and a classifier. We capture client features by extracting the outputs of the extractor and the inputs of the classifier. To harness the knowledge from client features, we propose IN training for aligning the intermediate layers based on features obtained from other clients. IN training only needs minimal memory and communication overhead by utilizing a single batch of client features. Additionally, we formulate and address a convex optimization problem to mitigate the challenge of gradient divergence caused by conflicts between IN training and local training. The experiment results demonstrate the superior performance of FedIN in heterogeneous model environments compared to state-of-the-art algorithms. Furthermore, our ablation study demonstrates the effectiveness of IN training and the proposed solution for alleviating gradient divergence.

cs.LG↗

arXiv2022

The Story of $1/e$: ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communications

Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, and cyber-physical systems, supporting delay-constrained traffic becomes critical for such systems. In delay-constrained traffic, each packet has a hard deadline; when it is not delivered before its deadline is up, it becomes useless and will be removed from the system. In this work, we focus on designing random access schemes for delay-constrained wireless communications. We first investigate three ALOHA-based schemes and prove that the system timely throughput of all three schemes under corresponding optimal transmission probabilities asymptotically converges to $1/e$, same as the well-known throughput limit for delay-unconstrained ALOHA systems. The fundamental reason why ALOHA-based schemes cannot achieve asymptotical system timely throughput beyond $1/e$ is that all active ALOHA stations access the channel with the same probability in any slot. To go beyond $1/e$, we propose a reinforcement-learning-based scheme for delay-constrained wireless communications, called RLRA-DC, under which different stations collaboratively attain different transmission probabilities by only interacting with the access point. Our numerical result shows that the system timely throughput of RLRA-DC can be as high as 0.8 for tens of stations and can still reach 0.6 even for thousands of stations, much larger than $1/e$.

cs.IT↗

arXiv2020

Overhead Control with Reliable Transmission of Popular Packets in Ad-Hoc Social Networks

Reliable social connectivity and transmission of data for popular nodes is vital in multihop Ad-hoc Social Networks (ASNETs). In this networking paradigm, transmission unreliability could be caused by multiple social applications running on a single node. This leads to contentions among nodes and connection paths. In addition, congestions can be the result of multiple senders transmitting data to a single receiver and every sender waiting for a positive acknowledgment to move on. Therefore, traditional Transmission Control Protocol (TCP) performs poorly in ASNETs, due to the fact that the available bandwidth is shared among nodes using round trip time and the acknowledgment is provided individually to every data packet. To solve these issues, we propose a technique, called Overhead Control with Reliable Transmission of Popular Packets in Ad-Hoc Social Networks (RTPS), which improves transmission reliability by assigning bandwidth to users based on their popularity levels: extra bandwidth is assigned to the nodes with higher popularity and their acknowledgments are sent with higher priority. In addition, RTPS further reduces contentions and packet losses by delaying acknowledgment packet transmissions. Our detailed investigations demonstrate the excellent performance of RTPS in terms of throughput latency and overhead with different hop-distances and different numbers of concurrent TCP flows.

cs.NI↗

arXiv2017

Layered Synthesis of Latent Gaussian Trees

A new synthesis scheme is proposed to generate a random vector with prescribed joint density that induces a (latent) Gaussian tree structure. The quality of synthesis is shown by vanishing total variation distance between the synthesized and desired statistics. The proposed layered and successive synthesis scheme relies on the learned structure of tree to use sufficient number of common random variables to synthesize the desired density. We characterize the achievable rate region for the rate tuples of multi-layer latent Gaussian tree, through which the number of bits needed to synthesize such Gaussian joint density are determined. The random sources used in our algorithm are the latent variables at the top layer of tree, the additive independent Gaussian noises, and the Bernoulli sign inputs that capture the ambiguity of correlation signs between the variables. We have shown that such ambiguity can further help in reducing the synthesis rates for the underlying Gaussian trees.

cs.IT↗