@article {2026,
	title = {Formalizing Semi-Structured Interviews for Design Requirement Discovery: A Multi-Agent Framework for Clarification and Empathic Interaction},
	journal = {Available at SSRN 7199726},
	year = {2026},
	abstract = {Accurate requirement discovery remains a central challenge in interactive system design because users often express concrete solutions rather than the latent needs that should guide design. Semi-structured interviews can reveal these latent needs, but they are difficult for human designers to conduct at scale, while monolithic large language model (LLM) interviewers may struggle in long conversations to maintain interview structure, detect vague responses, and balance analytical control with empathic interaction. We propose a methodology-aligned multi-agent framework for AI-supported semi-structured requirement discovery. The framework separates planning, interpretation, dialogue control, and user-facing expression, allowing interview decisions and conversational delivery to be coordinated without collapsing them into a single generation loop. A dual-track evaluation, combining a simulation track against a monolithic LLM baseline with a human track involving academic researchers, suggests that this design improves structural adherence, clarification behavior, terminology reuse, and requirement-evidence extraction while remaining usable in human research contexts. These findings indicate that multi-agent design can support more scalable requirement-discovery interviews while making the interview process easier to inspect, but careful calibration is required before human-facing deployment.},
	doi = {https://dx.doi.org/10.2139/ssrn.7199726},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7199726},
	author = {Zhong, C. and Wu, S. and Yu, J. and Wu, W. and D. Herremans and K. Zhang}
}
@conference {2026,
	title = {Scaffolded Vulnerability: Chatbot-Mediated Reciprocal Self-Disclosure and Need-Supportive Interaction in Couples},
	booktitle = {Proceedings of CHI},
	year = {2026},
	publisher = {ACM},
	organization = {ACM},
	address = {Barcelona, Spain},
	abstract = {Reciprocal self-disclosure and need-supportive behavior are essential for close relationships, yet prior systems rarely engage the motivational underpinnings, autonomy, competence, and relatedness, that help partners internalize supportive behaviors. We introduce a Self-Determination Theory{\textendash}guided chatbot that mediates self-disclosure between romantic partners by scaffolding these needs through structured questions and reflection follow-ups. In a randomized study (N=72; 36 couples), we compared three conditions: Partner Support (PS: chatbot support + partner-reflection scaffolds), Direct Support (DS: chatbot support only), and Basic Prompt (BP: questions only). PS conversations were longest and most engaged; PS and DS elicited deeper disclosures and stronger relatedness support than BP. Within PS, reflection phases concentrated partner-provided need support. Controlled motivation decreased across conditions, closeness increased only in PS, and vitality declined in BP. We contribute empirical evidence that SDT-guided mediation amplifies support and closeness, a design blueprint for relatedness technologies, and an SDT framework for advancing AI-mediated conversation design.},
	author = {Z. Jiang and S. Yeo and D. Herremans and S. Perrault}
}
@article {2024,
	title = {BandCondiNet: Parallel Transformers-based Conditional Popular Music Generation with Multi-View Features},
	journal = {Expert Systems with Applications},
	volume = {130059},
	year = {2025},
	abstract = {Conditional music generation offers significant advantages in terms of user convenience and control, presenting great potential in AI-generated content research. However, building conditional generative systems for multitrack popular songs presents three primary challenges: insufficient fidelity of input conditions, poor structural modeling, and inadequate inter-track harmony learning in generative models. To address these issues, we propose BandCondiNet, a conditional model based on parallel Transformers, designed to process the multiple music sequences and generate high-quality multitrack samples. Specifically, we propose multi-view features across time and instruments as high-fidelity conditions. Moreover, we propose two specialized modules for BandCondiNet: Structure Enhanced Attention (SEA) to strengthen the musical structure, and Cross-Track Transformer (CTT) to enhance inter-track harmony. We conducted both objective and subjective evaluations on two popular music datasets with different sequence lengths. Objective results on the shorter dataset show that BandCondiNet outperforms other conditional models in 9 out of 10 metrics related to fidelity and inference speed, with the exception of Chord Accuracy. On the longer dataset, BandCondiNet surpasses all conditional models across all 10 metrics. Subjective evaluations across four criteria reveal that BandCondiNet trained on the shorter dataset performs best in Richness and performs comparably to state-of-the-art models in the other three criteria, while significantly outperforming them across all criteria when trained on the longer dataset. To further expand the application scope of BandCondiNet, future work should focus on developing an advanced conditional model capable of adapting to more user-friendly input conditions and supporting flexible instrumentation.},
	url = {https://arxiv.org/abs/2407.10462},
	author = {J. Luo and X. Yang and D. Herremans}
}
@article {2022,
	title = {Single Image Video Prediction with Auto-Regressive GANs},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {3533},
	abstract = {In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use \&ldquo;one shot\&rdquo; generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a \&ldquo;complementary mask\&rdquo; module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.},
	issn = {1424-8220},
	doi = {10.3390/s22093533},
	url = {https://www.mdpi.com/1424-8220/22/9/3533},
	author = {Huang, Jiahui and Chia, Yew Ken and Yu, Samson and Yee, Kevin and K{\"u}ster, Dennis and Krumhuber, Eva G. and Herremans, D and Roig, G.}
}
@article {2021,
	title = {Underwater Acoustic Communication Receiver Using Deep Belief Network},
	journal = {IEEE Transactions on Communications},
	year = {2021},
	pages = {1-1},
	abstract = {Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique{\textendash}Deep Belief Network (DBN){\textendash} to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10-3 Bit Error Rate (BER).},
	doi = {10.1109/TCOMM.2021.3063353},
	url = {https://ieeexplore.ieee.org/document/9367188},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@conference {2019,
	title = {Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks},
	booktitle = {16th IEEE Asia Pacific Wireless Communications Symposium (APWCS)},
	year = {2019},
	abstract = {Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods {\textemdash} (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. Next, the classification model correlates the images to a corresponding binary representative. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@conference {2019,
	title = {A Hybrid Fuzzy Logic-Neural Network Approach For Multi-path Separation Of Underwater Acoustic Signals},
	booktitle = {89th IEEE Vehicular Technology Conference},
	year = {2019},
	month = {04/2019},
	address = {Kuala Lumpur, Malaysia},
	abstract = {Abstract{\textemdash}Underwater acoustic channels are generally recognized as one of the most difficult communication media in use today. One of the most important constraints of underwater communications is the acoustic propagation of the signals. The aim of a multi-path separator is to extract individual correlated signals from their mixtures. We present an algorithm, Tag Receiver, that is similar to the RAKE receiver, but interpreted from the neural network viewpoint. First, the received signal is split into frames. Next, the frames are {\textquotedblleft}tagged{\textquotedblright} with a predicted number of multi-path in the framed segment via fuzzy logic and feature extraction. Finally, the frame and {\textquotedblleft}tag{\textquotedblright} number are inputted into a neural network, which separates the signals. The analysis of the proposed algorithm shows an improvement from the conventional RAKE receiver in terms of BER.},
	doi = {10.1109/VTCSpring.2019.8746614},
	url = {https://ieeexplore.ieee.org/abstract/document/8746614},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@proceedings {86,
	title = {IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations},
	year = {2017},
	month = {08/2017},
	address = {London, UK},
	author = {D. Herremans and S. Yang and C.-H. Chuan and M. Barthet and E. Chew}
}
