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Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback

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Proceedings of the ACM on Human-computer Interaction 2025年第1期9卷 1-32页

作者： Daryanto, Taufiq Ding, Xiaohan Wilhelm, Lance T. Stil, Sophia Knutsen, Kirk McInnis Rho, Eugenia H. Computer Science Virginia Tech Blacksburg United States

Job interviews play a critical role in shaping one's career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback, a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user's responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent's guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using

关键词： Artificial intelligence

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A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets

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ACM Transactions on Storage 2025年第2期21卷 1-32页

作者： Yang, Jin Yoon, Heejin Yun, Gyeongchan Noh, Sam H. Choi, Young-Ri Computer Science and Engineering Unist Ulsan Korea Republic of Department of Computer Science Virginia Tech Blacksburg United States

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this article, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, although based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic datasets index structure key distribution

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BAAP-FIoT: Blockchain Assisted Authentication Protocol for Fog-enabled Internet of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15681-15696页

作者： Ponnuru, Raveendra Babu Kumar, Sathish A.P. Azab, Mohamed Alavalapati, Goutham Reddy Virginia Tech Virginia Military Institute and Computer Science Department of Computer and Information Science BlacksburgVA United States Cleveland State University Department of Electrical Engineering and Computer Science ClevelandOH44115 United States Virginia Military Institute Department of Computer Science and Information Sciences LexingtonVA24450 United States University of Illinois Springfield One University Plaza Department of Computer Science SpringfieldIL62703 United States

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users,IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user's identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications. © 2014 IEEE.

关键词： Authentication Protocol

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Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues 25

Enhancing Immersive Sensemaking with Gaze-Driven Recommendat...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Tahmid, Ibrahim Asadullah North, Chris Davidson, Kylie Whitley, Kirsten Bowman, Doug Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States Department of Defense College ParkMD United States

ISBN: (纸本)9798400713064

Sensemaking is a complex task that places a heavy cognitive demand on individuals. With the recent surge in data availability, making sense of vast amounts of information has become a significant challenge for many professionals, such as intelligence analysts. Immersive technologies such as mixed reality offer a potential solution by providing virtually unlimited space to organize data. However, the difficulty of processing, filtering relevant information, and synthesizing insights remains. We proposed using eye-tracking data from mixed reality head-worn displays to derive the analyst's perceived interest in documents and words, and convey that part of the mental model to the analyst. The global interest of the documents is reflected in their color, and their order on the list, while the local interest of the documents is used to generate focused recommendations for a document. To evaluate these recommendation cues, we conducted a user study with two conditions: a gaze-aware system, EyeST, and a "Freestyle"system without gaze-based visual cues. Our findings reveal that the EyeST helped analysts stay on track by reading more essential information while avoiding distractions. However, this came at the cost of reduced focused attention and perceived system performance. The results of our study highlight the need for explainable AI in human-AI collaborative sensemaking to build user trust and encourage the integration of AI outputs into the immersive sensemaking process. Based on our findings, we offer a set of guidelines for designing gaze-driven recommendation cues in an immersive environment. © 2025 Copyright held by the owner/author(s).

关键词： Information filtering

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Towards Securing Water Systems Communications from Adversarial Attacks using AI Assurance 5

Towards Securing Water Systems Communications from Adversari...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Yardimci, Mehmet Oguz Batarseh, Feras A. Virginia Tech Department of Computer Science ArlingtonVA United States Virginia Tech Department of Biological Systems Engineering ArlingtonVA United States

ISBN: (纸本)9798331519957

Water systems are increasingly susceptible to cyberattacks due to their reliance on networked communications for monitoring and control. This paper introduces an AI-Assured approach to detect anomalies in water distribution systems communication layers using Long Short-Term Memory models, which are trained, tuned, and validated through four different scenarios targeting disruptions in communication patterns. Using the Secure Water Treatment and anomalies and malicious acts datasets, and by simulating both operational and malicious scenarios, Long Short-Term Memory model is trained on normal data and tested on both regular (idle) and attack situations. The LSTM model employs a sliding time-window technique to capture sequential dependencies in communication data;it identifies anomalies by comparing predicted and actual sequence-significant deviations that signal potential cyber threats. Our scenarios demonstrate an anomaly detection accuracy of 94%, with a false positive rate of 2.5%, indicating the model's capacity to identify both overt and subtle attacks. Further, we evaluate the intentionality of those outliers, whether they are cyber threats or merely a human operator error. The anomalies and malicious acts dataset scenario successfully detects multiple intentional sabotage scenarios, further validating the model's potential in real-world cyber-physical security settings. The findings suggest that integrating AI-Assured, LSTM-based anomaly detection into water systems can enhance security, enabling early threat detection and reducing the overall risk to critical infrastructures communications. © 2025 IEEE.

关键词： Artificial intelligence

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Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality 7

Generative AI for Context-Aware 3D Object Creation Using Vis...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Behravan, Majid Matković, Krešimir Gračanin, Denis Virginia Tech Department of Computer Science VA United States VRVis Research Vienna Austria

ISBN: (纸本)9798331521578

We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 IEEE.

关键词： Generative adversarial networks

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CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications 25

CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analy...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Chen, Chaoran Zhou, Daodao Ye, Yanfang Li, Toby Jia-Jun Yao, Yaxing Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States Department of Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9798400713064

The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain unaware of the risks. This paper explores methods to increase user awareness of privacy risks associated with LLMs in end-user applications. We conducted five co-design workshops to uncover user privacy concerns and their demand for contextual privacy information within LLMs. Based on these insights, we developed CLEAR (Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation), a just-in-time contextual assistant designed to help users identify sensitive information, summarize relevant privacy policies, and highlight potential risks when sharing information with LLMs. We evaluated the usability and usefulness of CLEAR across two example domains: ChatGPT and the Gemini plugin in Gmail. Our findings demonstrated that CLEAR is easy to use and improves users' understanding of data practices and privacy risks. We also discussed LLM's duality in posing and mitigating privacy risks, offering design and policy implications. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans 12th

A 3D Deep Learning Architecture for Denoising Low-Dose CT S...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Kasparian, Armen Cao, Guohua Feng, Wu-Chun Department of Computer Science Virginia Tech Blacksburg United States School of Biomedical Engineering ShanghaiTech University Shanghai China

ISBN: (纸本)9783031827679

Low-dose computed tomography (LDCT) scans reduce the radiation dose of computed tomography (CT) scans but come at the expense of image quality. Deep-learning (DL) image denoising techniques can enhance these LDCT images to match the quality of their regular-dose CT counterparts. To achieve better denoising performance than the current state of the art, we present a novel 3D DL architecture for LDCT image denoising called 3D-DDnet. The architecture leverages the inter-slice correlation in volumetric CT scans to obtain better denoising performance and employs distributed data parallel (DDP) strategies along with transfer learning to achieve faster training. The DDP training strategy enables a scalable multi-GPU approach on Nvidia A100 GPUs, which allows the training of previously prohibitively large volumetric samples. Our results show that 3D-DDnet achieves 10% better mean square error (MSE) on LDCT scans than its 2D predecessor (i.e., 2D-DDnet). In addition, the transfer learning in 3D-DDnet leverages existing trained 2D models to "jump start" the weights and biases of our 3D DL model and reduces training time by 50% while maintaining accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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OSINT Clinic: Co-designing AI-Augmented Collaborative OSINT Investigations for Vulnerability Assessment 25

OSINT Clinic: Co-designing AI-Augmented Collaborative OSINT ...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Anirban Mukhopadhyay Kurt Luther Department of Computer Science Virginia Tech Blacksburg Virginia USA Department of Computer Science Virginia Tech Arlington Virginia USA

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Perceptions of AI Ethics Policies Among Scientists and Engineers in Policy-Related Roles: An Exploratory Investigation

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Digital Society 2025年第1期4卷 1-25页

作者： Weichert, James Zhu, Qin Kim, Dayoung Eldardiry, Hoda Department of Computer Science Virginia Tech Blacksburg USA Department of Engineering Education Virginia Tech Blacksburg USA

The explosive growth of artificial intelligence (AI) over the past few years has focused attention on how diverse stakeholders regulate these technologies to ensure their safe and ethical use. Increasingly, governmental bodies, corporations, and nonprofit organizations are developing strategies and policies for AI governance. While existing literature on ethical AI has focused on the various principles and guidelines that have emerged as a result of these efforts, just how these principles are operationalized and translated to broader policy is still the subject of current research. Specifically, there is a gap in our understanding of how policy practitioners actively engage with, contextualize, or reflect on existing AI ethics policies in their daily professional activities. The perspectives of these policy experts towards AI regulation generally are not fully understood. To this end, this paper explores the perceptions of scientists and engineers in policy-related roles in the US public and nonprofit sectors towards AI ethics policy, both in the US and abroad. We interviewed 15 policy experts and found that although these experts were generally familiar with AI governance efforts within their domains, overall knowledge of guiding frameworks and critical regulatory policies was still limited. There was also a general perception among the experts we interviewed that the US lagged behind other comparable countries in regulating AI, a finding that supports the conclusion of existing literature. Lastly, we conducted a preliminary comparison between the AI ethics policies identified by the policy experts in our study and those emphasized in existing literature, identifying both commonalities and areas of divergence.

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