pwc-archive/papers-with-abstracts huggingface dataset:
The Limits of Price Discrimination Under Privacy Constraints
Authors: Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian
Date: 2024-02-13 00:00:00

We study a producer's problem of selling a product to a continuum of privacy-conscious consumers, where the producer can implement third-degree price discrimination, offering different prices to different market segments. We consider a privacy mechanism that provides a degree of protection by probabilistically masking each market segment. We establish that the resultant set of all consumer-producer utilities forms a convex polygon, characterized explicitly as a linear mapping of a certain high-dimensional convex polytope into $\mathbb{R}^2$. This characterization enables us to investigate the impact of the privacy mechanism on both producer and consumer utilities. In particular, we establish that the privacy constraint always hurts the producer by reducing both the maximum and minimum utility achievable. From the consumer's perspective, although the privacy mechanism ensures an increase in the minimum utility compared to the non-private scenario, interestingly, it may reduce the maximum utility. Finally, we demonstrate that increasing the privacy level does not necessarily intensify these effects. For instance, the maximum utility for the producer or the minimum utility for the consumer may exhibit nonmonotonic behavior in response to an increase of the privacy level.
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations
Authors: Mario Truss, Marc Schmitt
Date: 2024-02-06 00:00:00
Tasks: AutoML, Decision Making

This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process. Through a comprehensive literature review, key challenges were identified and no-code AutoML was analyzed as a solution. The framework describes the seamless incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach of combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. Implications for academia and industry, emphasizing the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation, are discussed.
Abstracted Trajectory Visualization for Explainability in Reinforcement Learning
Authors: Yoshiki Takagi, Roderick Tabalba, Nurit Kirshenbaum, Jason Leigh
Date: 2024-02-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Explainable AI (XAI) has demonstrated the potential to help reinforcement learning (RL) practitioners to understand how RL models work. However, XAI for users who do not have RL expertise (non-RL experts), has not been studied sufficiently. This results in a difficulty for the non-RL experts to participate in the fundamental discussion of how RL models should be designed for an incoming society where humans and AI coexist. Solving such a problem would enable RL experts to communicate with the non-RL experts in producing machine learning solutions that better fit our society. We argue that abstracted trajectories, that depicts transitions between the major states of the RL model, will be useful for non-RL experts to build a mental model of the agents. Our early results suggest that by leveraging a visualization of the abstracted trajectories, users without RL expertise are able to infer the behavior patterns of RL.
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Authors: Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha
Date: 2024-02-05 00:00:00
Tasks: Prompt Engineering, Question Answering

Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across various applications, from question-answering to commonsense reasoning. However, there remains a lack of systematic organization and understanding of the diverse prompt engineering methods and techniques. This survey paper addresses the gap by providing a structured overview of recent advancements in prompt engineering, categorized by application area. For each prompting approach, we provide a summary detailing the prompting methodology, its applications, the models involved, and the datasets utilized. We also delve into the strengths and limitations of each approach and include a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique. This systematic analysis enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.
Point and Instruct: Enabling Precise Image Editing by Unifying Direct Manipulation and Text Instructions
Authors: Alec Helbling, Seongmin Lee, Polo Chau
Date: 2024-02-05 00:00:00
Tasks: Image Manipulation

Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For example, in an image with several dogs, it is difficult to select a particular dog and move it to a precise location. Doing this with text alone would require a complex prompt that disambiguates the target dog and describes the destination. However, direct manipulation is well suited to visual tasks like selecting objects and specifying locations. We introduce Point and Instruct, a system for seamlessly combining familiar direct manipulation and textual instructions to enable precise image manipulation. With our system, a user can visually mark objects and locations, and reference them in textual instructions. This allows users to benefit from both the visual descriptiveness of natural language and the spatial precision of direct manipulation.
A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring
Authors: David González-Ortega, Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez
Date: 2024-02-04 00:00:00
Tasks: Relation, Unity

In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, select, receive, process, represent graphically, and store the signals from electrocardiogram (ECG), electromyogram (EMG) and galvanic skin response (GSR) modules and accelerometers, a magnetometer and a gyroscope. The Android app is synchronized in two steps with a driving simulator that we previously developed using the Unity game engine to analyze driving security and efficiency. The Android app was tested with different sensors working simultaneously at various sampling rates and in different Android devices. We also tested the synchronized working of the driving simulator and the Android app with 25 people and analyzed the relation between data from the ECG, EMG, GSR, and gyroscope sensors and from the simulator. Among others, some significant correlations between a gyroscope-based feature calculated by the Android app and vehicle data and particular traffic offences were found. The Android app can be applied with minor adaptations to other different users such as patients with chronic diseases or athletes.
How Can Generative AI Enhance the Well-being of Blind?
Authors: Oliver Bendel
Date: 2024-02-02 00:00:00

This paper examines the question of how generative AI can improve the well-being of blind or visually impaired people. It refers to a current example, the Be My Eyes app, in which the Be My AI feature was integrated in 2023, which is based on GPT-4 from OpenAI. The author's tests are described and evaluated. There is also an ethical and social discussion. The power of the tool, which can analyze still images in an amazing way, is demonstrated. Those affected gain a new independence and a new perception of their environment. At the same time, they are dependent on the world view and morality of the provider or developer, who prescribe or deny them certain descriptions. An outlook makes it clear that the analysis of moving images will mean a further leap forward. It is fair to say that generative AI can fundamentally improve the well-being of blind and visually impaired people and will change it in various ways.
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence
Authors: Xueting Huang, Zhibo Zhang, Fusen Guo, Xianghao Wang, Kun Chi, Kexin Wu
Date: 2024-02-01 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

This paper proposed a comprehensive mixed-methods framework with varied samples of older adults, including user experience, usability assessments, and in-depth interviews with the integration of Explainable Artificial Intelligence (XAI) methods. The experience of older adults' interaction with the Ehealth interface is collected through interviews and transformed into operatable databases whereas XAI methods are utilized to explain the collected interview results in this research work. The results show that XAI-infused e-health interfaces could play an important role in bridging the age-related digital divide by investigating elders' preferences when interacting with E-health interfaces. Furthermore, the study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human Computer Interaction (HCI) tools among older users. Furthermore, this study emphasizes the revolutionary potential of XAI in e-health interfaces for older users, emphasizing the importance of transparency and understandability in HCI-driven healthcare solutions. This study's findings have far-reaching implications for the design and development of user-centric e-health technologies, intending to increase the overall well-being of older adults.
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners
Authors: Rui Xiao, Lu Han, Xiaoying Zhou, Jiong Wang, Na Zong, Pengyu Zhang
Date: 2024-01-30 00:00:00
Tasks: Question Answering

In online learning platforms, particularly in rapidly growing computer programming courses, addressing the thousands of students' learning queries requires considerable human cost. The creation of intelligent assistant large language models (LLMs) tailored for programming education necessitates distinct data support. However, in real application scenarios, the data resources for training such LLMs are relatively scarce. Therefore, to address the data scarcity in intelligent educational systems for programming, this paper proposes a new Chinese question-and-answer dataset for Python learners. To ensure the authenticity and reliability of the sources of the questions, we collected questions from actual student questions and categorized them according to various dimensions such as the type of questions and the type of learners. This annotation principle is designed to enhance the effectiveness and quality of online programming education, providing a solid data foundation for developing the programming teaching assists (TA). Furthermore, we conducted comprehensive evaluations of various LLMs proficient in processing and generating Chinese content, highlighting the potential limitations of general LLMs as intelligent teaching assistants in computer programming courses.
Spatial Computing: Concept, Applications, Challenges and Future Directions
Authors: Gokul Yenduri, Ramalingam M, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Rutvij H Jhaveri, Ajay Bandi, Junxin Chen, Wei Wang, Adarsh Arunkumar Shirawalmath, Raghav Ravishankar, Weizheng Wang
Date: 2024-01-30 00:00:00

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.
Applications, challenges and ethical issues of AI and ChatGPT in education
Authors: Dimitrios Sidiropoulos, Christos-Nikolaos Anagnostopoulos
Date: 2024-01-22 00:00:00

Artificial Intelligence (AI) in recent years has shown an unprecedentedly impressive development, tending to play a catalytic role in all aspects of life. The interest of the academic community, but also of governments, is huge in the dynamics of AI and is reflected by the truly explosive amount of investment and research that is underway. Enthusiastic opinions and statements about AI are made every day, but at the same time they also bring to the fore alarming predictions about its effects. This paper aims to describe the opportunities emerging from the use of artificial intelligence and ChatGPT to improve education, but also to identify the challenges and ethical issues that arise.
Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems
Authors: Jinseok Choi, Jeonghun Park, Namyoon Lee, Ahmed Alkhateeb
Date: 2024-02-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.
Joint Communication and Sensing for 6G -- A Cross-Layer Perspective
Authors: Henk Wymeersch, Sharief Saleh, Ahmad Nimr, Rreze Halili, Rafael Berkvens, Mohammad H. Moghaddam, José Miguel Mateos-Ramos, Athanasios Stavridis, Stefan Wänstedt, Sokratis Barmpounakis, Basuki Priyanto, Martin Beale, Jaap van de Beek, Zi Ye, Marvin Manalastas, Apostolos Kousaridas, Gerhard P. Fettweis
Date: 2024-02-14 00:00:00

As 6G emerges, cellular systems are envisioned to integrate sensing with communication capabilities, leading to multi-faceted communication and sensing (JCAS). This paper presents a comprehensive cross-layer overview of the Hexa-X-II project's endeavors in JCAS, aligning 6G use cases with service requirements and pinpointing distinct scenarios that bridge communication and sensing. This work relates to these scenarios through the lens of the cross-layer physical and networking domains, covering models, deployments, resource allocation, storage challenges, computational constraints, interfaces, and innovative functions.
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems
Authors: Shen Gao, Peihao Dong, Zhiwen Pan, Xiaohu You
Date: 2024-02-14 00:00:00
Tasks: Quantization

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effect, aggravating the computation load and the difficulty to acquire the prior knowledge for channel estimation. In this article, an XL-MIMO channel network (XLCNet) is developed to estimate the high-dimensional channel, which is a universal solution for both the near-field users and far-field users with different channel statistics. Furthermore, a compressed XLCNet (C-XLCNet) is designed via weight pruning and quantization to accelerate the model inference as well as to facilitate the model storage and transmission. Simulation results show the performance superiority and universality of XLCNet. Compared to XLCNet, C-XLCNet incurs the limited performance loss while reducing the computational complexity and model size by about $10 \times$ and $36 \times$, respectively.
Time preference, wealth and utility inequality: A microeconomic interaction and dynamic macroeconomic model connection approach
Authors: Takeshi Kato
Date: 2024-02-14 00:00:00

Based on interactions between individuals and others and references to social norms, this study reveals the impact of heterogeneity in time preference on wealth distribution and inequality. We present a novel approach that connects the interactions between microeconomic agents that generate heterogeneity to the dynamic equations for capital and consumption in macroeconomic models. Using this approach, we estimate the impact of changes in the discount rate due to microeconomic interactions on capital, consumption and utility and the degree of inequality. The results show that intercomparisons with others regarding consumption significantly affect capital, i.e. wealth inequality. Furthermore, the impact on utility is never small and social norms can reduce this impact. Our supporting evidence shows that the quantitative results of inequality calculations correspond to survey data from cohort and cross-cultural studies. This study's micro-macro connection approach can be deployed to connect microeconomic interactions, such as exchange, interest and debt, redistribution, mutual aid and time preference, to dynamic macroeconomic models.
Deep Learning-based Real-time Smartphone Pose Detection for Ultra-wideband Tagless Gate
Authors: Junyoung Choi, Sagnik Bhattacharya
Date: 2024-02-13 00:00:00

As commercial interest in proximity services increased, the development of various wireless localization techniques was promoted. In line with this trend, Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services thanks to centimeter-level localization accuracy. In addition, since the actual location of the mobile device (MD) on the human body, called pose, affects the localization accuracy, poses are also important to provide accurate proximity services, especially for the UWB tagless gate (UTG). In this paper, a real-time pose detector, termed D3, is proposed to estimate the pose of MD when users pass through UTG. D3 is based on line-of-sight (LOS) and non-LOS (NLOS) classification using UWB channel impulse response and utilizes the inertial measurement unit embedded in the smartphone to estimate the pose. D3 is implemented on Samsung Galaxy Note20 Ultra (i.e., SMN986B) and Qorvo UWB board to show the feasibility and applicability. D3 achieved an LOS/NLOS classification accuracy of 0.984, and ultimately detected four different poses of MD with an accuracy of 0.961 in real-time.
Is 3-(F)WL Enough to Distinguish All 3D Graphs?
Authors: Wanghan Xu
Date: 2024-01-24 00:00:00
Tasks: All, Graph Generation

The problem of graph isomorphism is an important but challenging problem in the field of graph analysis, for example: analyzing the similarity of two chemical molecules, or studying the expressive ability of graph neural networks. WL test is a method to judge whether two graphs are isomorphic, but it cannot distinguish all non-isomorphic graphs. As an improvement of WL, k-WL has stronger isomorphism discrimination ability, and as k increases, its discrimination ability is strictly increasing. However, whether the isomorphic discrimination power of k-WL is strictly increasing for more complex 3D graphs, or whether there exists k that can discriminate all 3D graphs, remains unexplored. This paper attempts to explore this problem from the perspective of graph generation.
MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech
Authors: Shengpeng Ji, Ziyue Jiang, Hanting Wang, Jialong Zuo, Zhou Zhao
Date: 2024-02-14 00:00:00
Tasks: Decoder, GPU, text-to-speech, Text to Speech, Voice Cloning

Zero-shot text-to-speech (TTS) has gained significant attention due to its powerful voice cloning capabilities, requiring only a few seconds of unseen speaker voice prompts. However, all previous work has been developed for cloud-based systems. Taking autoregressive models as an example, although these approaches achieve high-fidelity voice cloning, they fall short in terms of inference speed, model size, and robustness. Therefore, we propose MobileSpeech, which is a fast, lightweight, and robust zero-shot text-to-speech system based on mobile devices for the first time. Specifically: 1) leveraging discrete codec, we design a parallel speech mask decoder module called SMD, which incorporates hierarchical information from the speech codec and weight mechanisms across different codec layers during the generation process. Moreover, to bridge the gap between text and speech, we introduce a high-level probabilistic mask that simulates the progression of information flow from less to more during speech generation. 2) For speaker prompts, we extract fine-grained prompt duration from the prompt speech and incorporate text, prompt speech by cross attention in SMD. We demonstrate the effectiveness of MobileSpeech on multilingual datasets at different levels, achieving state-of-the-art results in terms of generating speed and speech quality. MobileSpeech achieves RTF of 0.09 on a single A100 GPU and we have successfully deployed MobileSpeech on mobile devices. Audio samples are available at \url{https://mobilespeech.github.io/} .