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1. Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities

   https://doi.org/10.1145/3715097  

   Deng, Yang; Liao, Lizi; Lei, Wenqiang; Yang, Grace Hui; Lam, Wai; Chua, Tat-Seng (May 2025, ACM Transactions on Information Systems)

   Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains. 

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2. Progressive Regularization of Satellite-Based 3D Buildings for Interactive Rendering

   https://doi.org/10.1145/3384382.3384526  

   Zhang, X.; May, C.; Nishida, G.; Aliaga, D. (May 2020, ACM Symposium on Interactive 3D Graphics)

   null (Ed.)

   Automatic creation of lightweight 3D building models from satellite image data enables large and widespread 3D interactive urban rendering. Towards this goal, we present an inverse procedural modeling method to automatically create building envelopes from satellite imagery. Our key observation is that buildings exhibit regular properties. Hence, we can overcome the low-resolution, noisy, and partial building data obtained from satellite by using a two stage inverse procedural modeling technique. Our method takes in point cloud data obtained from multi-view satellite stereo processing and produces a crisp and regularized building envelope suitable for fast rendering and optional projective texture mapping. Further, our results show highly complete building models with quality superior to that of other compared-to approaches. 

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3. Secure Archival is Hard... Really Hard

   https://doi.org/10.1145/3655038.3666093  

   Smith, Christopher; Tabassum, Maliha; Daruru, Soumya Chowdary; Kulhare, Gaurav; Wang, Arvin; Miller, Ethan L; Zadok, Erez (July 2024, ACM)

   Archival systems are often tasked with storing highly valuable data that may be targeted by malicious actors. When the lifetime of the secret data is on the order of decades to centuries, the threat of improved cryptanalysis casts doubt on the long-term security of cryptographic techniques, which rely on hardness assumptions that are hard to prove over archival time scales. This threat makes the design of secure archival systems exceptionally difficult. Some archival systems turn a blind eye to this issue, hoping that current cryptographic techniques will not be broken; others often use techniques--—such as secret sharing—that are impractical at scale. This position paper sheds light on the core challenges behind building practically viable secure long-term archives; we identify promising research avenues towards this goal. 

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4. Spin-torque devices with hard axis initialization as Stochastic Binary Neurons

   https://doi.org/10.1038/s41598-018-34996-2  

   Ostwal, Vaibhav; Debashis, Punyashloka; Faria, Rafatul; Chen, Zhihong; Appenzeller, Joerg (November 2018, Scientific Reports)

   Abstract Employing the probabilistic nature of unstable nano-magnet switching has recently emerged as a path towards unconventional computational systems such as neuromorphic or Bayesian networks. In this letter, we demonstrate proof-of-concept stochastic binary operation using hard axis initialization of nano-magnets and control of their output state probability (activation function) by means of input currents. Our method provides a natural path towards addition of weighted inputs from various sources, mimicking the integration function of neurons. In our experiment, spin orbit torque (SOT) is employed to “drive” nano-magnets with perpendicular magnetic anisotropy (PMA) -to their metastable state, i.e. in-plane hard axis. Next, the probability of relaxing into one magnetization state (+m_i) or the other (−m_i) is controlled using an Oersted field generated by an electrically isolated current loop, which acts as a “charge” input to the device. The final state of the magnet is read out by the anomalous Hall effect (AHE), demonstrating that the magnetization can be probabilistically manipulated and output through charge currents, closing the loop from charge-to-spin and spin-to-charge conversion. Based on these building blocks, a two-node directed network is successfully demonstrated where the status of the second node is determined by the probabilistic output of the previous node and a weighted connection between them. We have also studied the effects of various magnetic properties, such as magnet size and anisotropic field on the stochastic operation of individual devices through Monte Carlo simulations of Landau Lifshitz Gilbert (LLG) equation. The three-terminal stochastic devices demonstrated here are a critical step towards building energy efficient spin based neural networks and show the potential for a new application space. 

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5. Towards Enhancing Health Coaching Dialogue in Low-Resource Settings

   Zhou, Yue; Di Eugenio, Barbara; Ziebart, Brian; Sharp, Lisa; Liu, Bing; Gerber, Ben; Agadakos, Nikolaos; Yadav, Shweta (October 2022, International Committee on Computational Linguistics)

   Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems. 

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