pwc-archive/papers-with-abstracts huggingface dataset:
Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
Authors: Yilin Sai, Qin Wang, Guangsheng Yu, H. M. N. Dilum Bandara, Shiping Chen
Date: 2024-04-09 00:00:00
Tasks: Management

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount. AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treats metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Our framework addresses concerns regarding data and model provenance and copyright compliance. \textsc{IBis} enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.
Optimal Controller Realizations against False Data Injections in Cooperative Driving
Authors: Mischa Huisman, Carlos Murguia, Erjen Lefeber, Nathan van de Wouw
Date: 2024-04-08 00:00:00

To enhance the robustness of cooperative driving to cyberattacks, we study a controller-oriented approach to mitigate the effect of a class of False-Data Injection (FDI) attacks. By reformulating a given dynamic Cooperative Adaptive Cruise Control scheme (the base controller), we show that a class of new but equivalent controllers (base controller realizations) can represent the base controller. This controller class exhibits the same platooning behavior in the absence of attacks, but in the presence of attacks, their robustness varies with the realization. We propose a prescriptive synthesis framework where the base controller and the system dynamics are written in new coordinates via an invertible coordinate transformation on the controller state. Because the input-output behavior is invariant under coordinate transformations, the input-output behavior is unaffected (so controller realizations do not change the system's closed-loop performance). However, each controller realization may require a different combination of sensors. Subsequently, we obtain the optimal combination of sensors that minimizes the effect of FDI attacks by solving a linear matrix inequality while quantifying the FDI's attack impact through reachability analysis. Through simulation studies, we demonstrate that this approach enhances the robustness of cooperative driving without relying on a detection scheme and maintaining all system properties.
Automatic Authorities: Power and AI
Authors: Seth Lazar
Date: 2024-04-09 00:00:00

As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning and related computational technologies now underpin vital government services. They connect consumers and producers in new algorithmic markets. They determine how we find out about everything from how to vote to where to get vaccinated, and whose speech is amplified, reduced, or restricted. And a new wave of products based on Large Language Models (LLMs) will further transform our economic and political lives. Automatic Authorities are automated computational systems used to exercise power over us by determining what we may know, what we may have, and what our options will be. In response to their rise, scholars working on the societal impacts of AI and related technologies have advocated shifting attention from how to make AI systems beneficial or fair towards a critical analysis of these new power relations. But power is everywhere, and is not necessarily bad. On what basis should we object to new or intensified power relations, and what can be done to justify them? This paper introduces the philosophical materials with which to formulate these questions, and offers preliminary answers. It starts by pinning down the concept of power, focusing on the ability that some agents have to shape others' lives. It then explores how AI enables and intensifies the exercise of power so understood, and sketches three problems with power and three ways to solve those problems. It emphasises, in particular, that justifying power requires more than satisfying substantive justificatory criteria; standards of proper authority and procedural legitimacy must also be met. We need to know not only what power may be used for, but how it may be used, and by whom.
Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data
Authors: Nan Jiang, Haitao Yuan, Jianing Si, Minxiao Chen, Shangguang Wang
Date: 2024-03-22 00:00:00
Tasks: Prediction

The next point-of-interest (POI) prediction is a significant task in location-based services, yet its complexity arises from the consolidation of spatial and semantic intent. This fusion is subject to the influences of historical preferences, prevailing location, and environmental factors, thereby posing significant challenges. In addition, the uneven POI distribution further complicates the next POI prediction procedure. To address these challenges, we enrich input features and propose an effective deep-learning method within a two-step prediction framework. Our method first incorporates remote sensing data, capturing pivotal environmental context to enhance input features regarding both location and semantics. Subsequently, we employ a region quad-tree structure to integrate urban remote sensing, road network, and POI distribution spaces, aiming to devise a more coherent graph representation method for urban spatial. Leveraging this method, we construct the QR-P graph for the user's historical trajectories to encapsulate historical travel knowledge, thereby augmenting input features with comprehensive spatial and semantic insights. We devise distinct embedding modules to encode these features and employ an attention mechanism to fuse diverse encodings. In the two-step prediction procedure, we initially identify potential spatial zones by predicting user-preferred tiles, followed by pinpointing specific POIs of a designated type within the projected tiles. Empirical findings from four real-world location-based social network datasets underscore the remarkable superiority of our proposed approach over competitive baseline methods.
Opinion Dynamics for Utility Maximizing Agents: Exploring the Impact of the Resource Penalty
Authors: Prashil Wankhede, Nirabhra Mandal, Sonia Martínez, Pavankumar Tallapragada
Date: 2024-04-07 00:00:00

We propose a continuous-time nonlinear model of opinion dynamics with utility-maximizing agents connected via a social influence network. A distinguishing feature of the proposed model is the inclusion of an opinion-dependent resource-penalty term in the utilities, which limits the agents from holding opinions of large magnitude. This model is applicable in scenarios where the opinions pertain to the usage of resources, such as money, time, computational resources etc. Each agent myopically seeks to maximize its utility by revising its opinion in the gradient ascent direction of its utility function, thus leading to the proposed opinion dynamics. We show that, for any arbitrary social influence network, opinions are ultimately bounded. For networks with weak antagonistic relations, we show that there exists a globally exponentially stable equilibrium using contraction theory. We establish conditions for the existence of consensus equilibrium and analyze the relative dominance of the agents at consensus. We also conduct a game-theoretic analysis of the underlying opinion formation game, including on Nash equilibria and on prices of anarchy in terms of satisfaction ratios. Additionally, we also investigate the oscillatory behavior of opinions in a two-agent scenario. Finally, simulations illustrate our findings.
A many-to-one job market: more about the core and the competitive salaries
Authors: Ata Atay, Marina Núñez, Tamás Solymosi
Date: 2024-04-07 00:00:00

This paper studies many-to-one assignment markets, or matching markets with wages. Although it is well-known that the core of this model is non-empty, the structure of the core has not been fully investigated. To the known dissimilarities with the one-to-one assignment game, we add that the bargaining set does not coincide with the core and the kernel may not be included in the core. Besides, not all extreme core allocations can be obtained by means of a lexicographic maximization or a lexicographic minimization procedure, as it is the case in the one-to-one assignment game. The maximum and minimum competitive salaries are characterized in two ways: axiomatically and by means of easily verifiable properties of an associated directed graph. Regarding the remaining extreme core allocations of the many-to-one assignment game, we propose a lexicographic procedure that, for each order on the set of workers, sequentially maximizes or minimizes each worker's competitive salary. This procedure provides all extreme vectors of competitive salaries, that is all extreme core allocations.
Some Characterizations of TTC in Multiple-Object Reallocation Problems
Authors: Jacob Coreno, Di Feng
Date: 2024-04-07 00:00:00

This paper considers reallocation of indivisible objects when agents are endowed with and can consume any bundles. We obtain characterizations of generalized versions of the Top Trading Cycles (TTC) rule on several preference domains. On the lexicographic domain, the TTC rule is uniquely determined by balancedness, Pareto efficiency, the worst endowment lower bound, and either truncation-proofness or drop strategy-proofness. On the more general responsive domain, the TTC rule is the unique individual-good-based rule that satisfies balancedness, individual-good efficiency, truncation-proofness, and either individual rationality or the worst endowment lower bound. On the conditionally lexicographic domain, the augmented TTC rule is characterized by balancedness, Pareto efficiency, the worst endowment lower bound, and drop strategy-proofness. The conditionally lexicographic domain is a maximal domain on which Pareto efficiency coincides with individual-good efficiency. For the housing market introduced by Shapley and Scarf (1974), the TTC rule is characterized by Pareto efficiency, individual rationality, and truncation-proofness.
Algorithmic Fairness and Social Welfare
Authors: Annie Liang, Jay Lu
Date: 2024-04-05 00:00:00
Tasks: Fairness

Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms share two features: First, they prioritize the role of a pre-defined group identity (e.g., race or gender) by focusing on how the algorithm's impact differs systematically across groups. Second, they are statistical in nature; for example, comparing false positive rates, or assessing whether group identity is independent of the decision (where both are viewed as random variables). These notions are facially distinct from a social welfare approach to fairness, in particular one based on ``veil of ignorance'' thought experiments in which individuals choose how to structure society prior to the realization of their social identity. In this paper, we seek to understand and organize the relationship between these different approaches to fairness. Can the optimization criteria proposed in the algorithmic fairness literature also be motivated as the choices of someone from behind the veil of ignorance? If not, what properties distinguish either approach to fairness?
Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems
Authors: Christos N. Efrem, Ioannis Krikidis
Date: 2024-04-09 00:00:00

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at both the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables. Then, we develop and analyze two optimization algorithms with different performance-complexity tradeoffs. The first algorithm is based on JCR and reduced exhaustive search (JCR&RES), while the second on JCR and alternating optimization (JCR&AO).
Intelligent Reflecting Surface Assisted Integrated Sensing and Communications for mmWave Channels
Authors: Zhengyu Zhu, Zheng Li, Zheng Chu, Gangcan Sun, Wanming Hao, Pei Xiao, Inkyu Lee
Date: 2022-01-05 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the desired radar waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is employed to determine the communication beamforming vector and the IRS phase shift. For computing the IRS phase shift, we adopt both the majorization minimization (MM) and the manifold optimization (MO). Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Furthermore, a trade-off factor is introduced to balance the performance of communication and sensing. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.
Jammer-Resilient Time Synchronization in the MIMO Uplink
Authors: Gian Marti, Flurin Arquint, Christoph Studer
Date: 2024-04-08 00:00:00

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate smart jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for resilient time synchronization in the presence of smart jammers. To remedy this situation, we propose JASS, the first method that enables reliable time synchronization for the single-user MIMO uplink while mitigating smart jamming attacks. JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer. We underscore the efficacy of the proposed optimization problem by proving that it ensures successful time synchronization under certain intuitive conditions. We then derive an efficient algorithm for approximately solving our optimization problem. Finally, we use simulations to demonstrate the effectiveness of JASS against a wide range of different jammer types.
Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
Authors: Jie Zhang, Jun Li, Long Shi, Zhe Wang, Shi Jin, Wen Chen, H. Vincent Poor
Date: 2024-04-08 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Management, Stochastic Optimization

As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable environments. In particular, deep reinforcement learning (DRL) is an important tool for addressing stochastic optimization issues of resource allocation. However, DRL has to start each new training process from the beginning once the state and action spaces change, causing low sample efficiency and poor generalization ability. Moreover, each DRL training process may take a large number of epochs to converge, which is unacceptable for time-sensitive scenarios. In this paper, we adopt an alternative AI technology, namely, Decision Transformer (DT), and propose a DT-based adaptive decision architecture for wireless resource management. This architecture innovates through constructing pre-trained models in the cloud and then fine-tuning personalized models at the edges. By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL. We then design DT frameworks for two typical communication scenarios: intelligent reflecting surfaces-aided communications and unmanned aerial vehicle-aided mobile edge computing. Simulations demonstrate that the proposed DT frameworks achieve over $3$-$6$ times speedup in convergence and better performance relative to the classic DRL method, namely, proximal policy optimization.
Self-organizing Multiagent Target Enclosing under Limited Information and Safety Guarantees
Authors: Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
Date: 2024-04-06 00:00:00
Tasks: Collision Avoidance

This paper introduces an approach to address the target enclosing problem using non-holonomic multiagent systems, where agents self-organize on the enclosing shape around a fixed target. In our approach, agents independently move toward the desired enclosing geometry when apart and activate the collision avoidance mechanism when a collision is imminent, thereby guaranteeing inter-agent safety. Our approach combines global enclosing behavior and local collision avoidance mechanisms by devising a special potential function and sliding manifold. We rigorously show that an agent does not need to ensure safety with every other agent and put forth a concept of the nearest colliding agent (for any arbitrary agent) with whom ensuring safety is sufficient to avoid collisions in the entire swarm. The proposed control eliminates the need for a fixed or pre-established agent arrangement around the target and requires only relative information between an agent and the target. This makes our design particularly appealing for scenarios with limited global information, hence significantly reducing communication requirements. We finally present simulation results to vindicate the efficacy of the proposed method.
Unravelling the Power of Single-Pass Look-Ahead in Modern Codecs for Optimized Transcoding Deployment
Authors: Vibhoothi Vibhoothi, Julien Zouein, François Pitié, Anil Kokaram
Date: 2024-04-08 00:00:00

Modern video encoders have evolved into sophisticated pieces of software in which various coding tools interact with each other. In the past, singlepass encoding was not considered for Video-On-Demand (VOD) use cases. In this work, we evaluate production-ready encoders for H.264 (x264), H.265 (HEVC), AV1 (SVT-AV1) along with direct comparisons to the latest AV1 encoder inside NVIDIA GPUs (40 series), and AWS Mediaconvert's AV1 implementation. Our experimental results demonstrate single pass encoding inside modern encoder implementations can give us very good quality at a reasonable compute cost. The results are presented as three different scenarios targeting High, Medium, and Low complexity accounting quality/bitrate/compute load. Finally, a set of recommendations is presented for end-users to help decide which encoder/preset combination might be more suited to their use case.
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations
Authors: Georgy Tyukin
Date: 2024-04-02 00:00:00
Tasks: Model Compression

Large Language Models are growing in size, and we expect them to continue to do so, as larger models train quicker. However, this increase in size will severely impact inference costs. Therefore model compression is important, to retain the performance of larger models, but with a reduced cost of running them. In this thesis we explore the methods of model compression, and we empirically demonstrate that the simple method of skipping latter attention sublayers in Transformer LLMs is an effective method of model compression, as these layers prove to be redundant, whilst also being incredibly computationally expensive. We observed a 21% speed increase in one-token generation for Llama 2 7B, whilst surprisingly and unexpectedly improving performance over several common benchmarks.
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework
Authors: Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Kunio Kashino
Date: 2024-04-09 00:00:00
Tasks: Audio Classification, Denoising, Environment Sound Classification, Linear evaluation, Self-Supervised Learning, Speaker Identification

Self-supervised learning (SSL) using masked prediction has made great strides in general-purpose audio representation. This study proposes Masked Modeling Duo (M2D), an improved masked prediction SSL, which learns by predicting representations of masked input signals that serve as training signals. Unlike conventional methods, M2D obtains a training signal by encoding only the masked part, encouraging the two networks in M2D to model the input. While M2D improves general-purpose audio representations, a specialized representation is essential for real-world applications, such as in industrial and medical domains. The often confidential and proprietary data in such domains is typically limited in size and has a different distribution from that in pre-training datasets. Therefore, we propose M2D for X (M2D-X), which extends M2D to enable the pre-training of specialized representations for an application X. M2D-X learns from M2D and an additional task and inputs background noise. We make the additional task configurable to serve diverse applications, while the background noise helps learn on small data and forms a denoising task that makes representation robust. With these design choices, M2D-X should learn a representation specialized to serve various application needs. Our experiments confirmed that the representations for general-purpose audio, specialized for the highly competitive AudioSet and speech domain, and a small-data medical task achieve top-level performance, demonstrating the potential of using our models as a universal audio pre-training framework. Our code is available online for future studies at https://github.com/nttcslab/m2d