OpenRaiser/Intern-Atlas huggingface dataset:
Driver Locations Harvesting Attack on pRide
Venue: arXiv cs.CR 2023
Year: 2023

Privacy preservation in Ride-Hailing Services (RHS) is intended to protect privacy of drivers and riders. pRide, published in IEEE Trans. Vehicular Technology 2021, is a prediction based privacy-preserving RHS protocol to match riders with an optimum driver. In the protocol, the Service Provider (SP) homomorphically computes Euclidean distances between encrypted locations of drivers and rider. Rider selects an optimum driver using decrypted distances augmented by a new-ride-emergence prediction. To improve the effectiveness of driver selection, the paper proposes an enhanced version where each driver gives encrypted distances to each corner of her grid. To thwart a rider from using these distances to launch an inference attack, the SP blinds these distances before sharing them with the rider. In this work, we propose a passive attack where an honest-but-curious adversary rider who makes a single ride request and receives the blinded distances from SP can recover the constants used to blind the distances. Using the unblinded distances, rider to driver distance and Google Nearest Road API, the adversary can obtain the precise locations of responding drivers. We conduct experiments with random on-road driver locations for four different cities. Our experiments show that we can determine the precise locations of at least 80% of the drivers participating in the enhanced pRide protocol.
Thermodynamics-informed neural networks for physically realistic mixed reality
Venue: arXiv cs.GR 2023
Year: 2023

The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Venue: arXiv cs.AI 2023
Year: 2023

Explainability plays an increasingly important role in machine learning. Furthermore, humans view the world through a causal lens and thus prefer causal explanations over associational ones. Therefore, in this paper, we develop a causal explanation mechanism that quantifies the causal importance of states on actions and such importance over time. We also demonstrate the advantages of our mechanism over state-of-the-art associational methods in terms of RL policy explanation through a series of simulation studies, including crop irrigation, Blackjack, collision avoidance, and lunar lander.
Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge about a nonlinear state equation with a learning method for finding an effective set of observables. In a lifted space, stable and unstable regions are separated into independent subspaces. Based on this property, we propose to find effective observables through neural net training where training data are separated into stable and unstable trajectories. The resultant learned observables are used for constructing a linear state transition matrix using method known as Direct Encoding, which transforms the nonlinear state equation to a state transition matrix through inner product computations with the observables. The proposed method shows a dramatic improvement over existing DMD and data-driven methods.
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
Venue: arXiv cs.AI 2023
Year: 2023

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.
Vitruvio: 3D Building Meshes via Single Perspective Sketches
Venue: arXiv cs.CV 2023
Year: 2023

Today's architectural engineering and construction (AEC) software require a learning curve to generate a three-dimension building representation. This limits the ability to quickly validate the volumetric implications of an initial design idea communicated via a single sketch. Allowing designers to translate a single sketch to a 3D building will enable owners to instantly visualize 3D project information without the cognitive load required. If previous state-of-the-art (SOTA) data-driven methods for single view reconstruction (SVR) showed outstanding results in the reconstruction process from a single image or sketch, they lacked specific applications, analysis, and experiments in the AEC. Therefore, this research addresses this gap, introducing the first deep learning method focused only on buildings that aim to convert a single sketch to a 3D building mesh: Vitruvio. Vitruvio adapts Occupancy Network for SVR tasks on a specific building dataset (Manhattan 1K). This adaptation brings two main improvements. First, it accelerates the inference process by more than 26% (from 0.5s to 0.37s). Second, it increases the reconstruction accuracy (measured by the Chamfer Distance) by 18%. During this adaptation in the AEC domain, we evaluate the effect of the building orientation in the learning procedure since it constitutes an important design factor. While aligning all the buildings to a canonical pose improved the overall quantitative metrics, it did not capture fine-grain details in more complex building shapes (as shown in our qualitative analysis). Finally, Vitruvio outputs a 3D-printable building mesh with arbitrary topology and genus from a single perspective sketch, providing a step forward to allow owners and designers to communicate 3D information via a 2D, effective, intuitive, and universal communication medium: the sketch.
Latency-aware End-to-end Multi-path Data Transmission for URLLC Services
Venue: arXiv cs.NI 2023
Year: 2023

5th Generation Mobile Communication Technology (5G) utilizes the Access Traffic Steering, Switching, and Splitting (ATSSS) rule to enable multi-path data transmission, which is currently being standardized. Recently, the 3rd Generation Partnership Project (3GPP) SA1 and SA2 have been working on the multi-path solution for possible improvement from different perspectives. However, the existing 3GPP multi-path solution has some limitations on ultra-reliable low-latency communication (URLLC) traffic in terms of reliability and latency requirements. In order to capture the potential gains of multi-path architecture in the context of URLLC services, this paper proposes a novel traffic splitting technique that can more efficiently enjoy the benefit of multi-path architecture in reducing user equipment (UE) uplink (UL) end-to-end (E2E) latency. In particular, we formulate an optimization framework that minimizes user's UL E2E latency via the joint optimization on the ratio of traffic assigned to each path and their corresponding transmit power. The performance of the proposed scheme is evaluated via well-designed simulations.
High-Throughput Flexible Belief Propagation List Decoder for Polar Codes
Venue: arXiv cs.IT 2023
Year: 2023

Owing to its high parallelism, belief propagation (BP) decoding is highly amenable to high-throughput implementations and thus represents a promising solution for meeting the ultra-high peak data rate of future communication systems. However, for polar codes, the error-correcting performance of BP decoding is far inferior to that of the widely used CRC-aided successive cancellation list (SCL) decoding algorithm. To close the performance gap to SCL, BP list (BPL) decoding expands the exploration of candidate codewords through multiple permuted factor graphs (PFGs). From an implementation perspective, designing a unified and flexible hardware architecture for BPL decoding that supports various PFGs and code configurations presents a big challenge. In this paper, we propose the first hardware implementation of a BPL decoder for polar codes and overcome the implementation challenge by applying a hardware-friendly algorithm that generates flexible permutations on-the-fly. First, we derive the graph selection gain and provide a sequential generation (SG) algorithm to obtain a near-optimal PFG set. We further prove that any permutation can be decomposed into a combination of multiple fixed routings, and we design a low-complexity permutation network to satisfy the decoding schedule. Our BPL decoder not only has a low decoding latency by executing the decoding and permutation generation in parallel, but also supports an arbitrary list size without any area overhead. Experimental results show that, for length-1024 polar codes with a code rate of one-half, our BPL decoder with 32 PFGs has a similar error-correcting performance to SCL with a list size of 4 and achieves a throughput of 25.63 Gbps and an area efficiency of 29.46 Gbps/mm$^{2}$ at SNR=4.0dB, which is 1.82$\times$ and 4.33$\times$ faster than the state-of-the-art BP flip and SCL decoders,~respectively
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Venue: arXiv cs.LG 2023
Year: 2023

Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost. This task is generally referred to as multi-fidelity Bayesian optimization (MFBO). However, MFBO algorithms can lead to higher optimization costs than their vanilla BO counterparts, especially when the low-fidelity sources are poor approximations of the objective function, therefore defeating their purpose. To address this issue, we propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog, with high controllable probability. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks, outperforming earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.
Exploring the impact of weather on Metro demand forecasting using machine learning method
Venue: arXiv cs.LG 2023
Year: 2023

Urban rail transit provides significant comprehensive benefits such as large traffic volume and high speed, serving as one of the most important components of urban traffic construction management and congestion solution. Using real passenger flow data of an Asian subway system from April to June of 2018, this work analyzes the space-time distribution of the passenger flow using short-term traffic flow prediction. Stations are divided into four types for passenger flow forecasting, and meteorological records are collected for the same period. Then, machine learning methods with different inputs are applied and multivariate regression is performed to evaluate the improvement effect of each weather element on passenger flow forecasting of representative metro stations on hourly basis. Our results show that by inputting weather variables the precision of prediction on weekends enhanced while the performance on weekdays only improved marginally, while the contribution of different elements of weather differ. Also, different categories of stations are affected differently by weather. This study provides a possible method to further improve other prediction models, and attests to the promise of data-driven analytics for optimization of short-term scheduling in transit management.
Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection
Venue: arXiv cs.HC 2023
Year: 2023

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of a smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper showing the variability of noise-related distractions, thermal comfort, and associated context. The results show people experience at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants' needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.
An adaptive multi-fidelity sampling framework for safety analysis of connected and automated vehicles
Venue: arXiv cs.RO 2023
Year: 2023

Testing and evaluation are expensive but critical steps in the development of connected and automated vehicles (CAVs). In this paper, we develop an adaptive sampling framework to efficiently evaluate the accident rate of CAVs, particularly for scenario-based tests where the probability distribution of input parameters is known from the Naturalistic Driving Data. Our framework relies on a surrogate model to approximate the CAV performance and a novel acquisition function to maximize the benefit (information to accident rate) of the next sample formulated through an information-theoretic consideration. In addition to the standard application with only a single high-fidelity model of CAV performance, we also extend our approach to the bi-fidelity context where an additional low-fidelity model can be used at a lower computational cost to approximate the CAV performance. Accordingly, for the second case, our approach is formulated such that it allows the choice of the next sample in terms of both fidelity level (i.e., which model to use) and sampling location to maximize the benefit per cost. Our framework is tested in a widely-considered two-dimensional cut-in problem for CAVs, where Intelligent Driving Model (IDM) with different time resolutions are used to construct the high and low-fidelity models. We show that our single-fidelity method outperforms the existing approach for the same problem, and the bi-fidelity method can further save half of the computational cost to reach a similar accuracy in estimating the accident rate.
The Monitor Model and its Misconceptions: A Clarification
Venue: arXiv cs.CL 2023
Year: 2023

Horizontal (automatic) and vertical (control) processes have been observed and reported for a long time in translation production. Schaeffer and Carl's Monitor Model integrates these two processes into one framework, assuming that priming mechanisms underlie horizontal/automatic processes, while vertical/monitoring processes implement consciously accessible control mechanisms. The Monitor Model has been criticized in various ways and several misconceptions have accumulated over the past years. In this chapter, I update the Monitor Model with additional evidence and argue that it is compatible with an enactivist approach to cognition. I address several misconceptions related to the Monitor Model.
Adversarial Purification with the Manifold Hypothesis
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis. This framework provides sufficient conditions for defending against adversarial examples. We develop an adversarial purification method with this framework. Our method combines manifold learning with variational inference to provide adversarial robustness without the need for expensive adversarial training. Experimentally, our approach can provide adversarial robustness even if attackers are aware of the existence of the defense. In addition, our method can also serve as a test-time defense mechanism for variational autoencoders.
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
Venue: arXiv cs.CV 2023
Year: 2023

This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified defense mechanism, where adversarial examples are either correctly classified or assigned to the "abstain" class. In this work, we show that such a provable framework can benefit by extension to networks with multiple explicit abstain classes, where the adversarial examples are adaptively assigned to those. We show that naively adding multiple abstain classes can lead to "model degeneracy", then we propose a regularization approach and a training method to counter this degeneracy by promoting full use of the multiple abstain classes. Our experiments demonstrate that the proposed approach consistently achieves favorable standard vs. robust verified accuracy tradeoffs, outperforming state-of-the-art algorithms for various choices of number of abstain classes.
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning
Venue: arXiv cs.LG 2023
Year: 2023

While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so typically assume that the requisite behavior demonstrations are provided by an expert that behaves optimally with respect to a task reward. If, however, suboptimal demonstrations are provided, a fundamental challenge appears in that the demonstration-matching objective of IL conflicts with the return-maximization objective of RL. This paper introduces D-Shape, a new method for combining IL and RL that uses ideas from reward shaping and goal-conditioned RL to resolve the above conflict. D-Shape allows learning from suboptimal demonstrations while retaining the ability to find the optimal policy with respect to the task reward. We experimentally validate D-Shape in sparse-reward gridworld domains, showing that it both improves over RL in terms of sample efficiency and converges consistently to the optimal policy in the presence of suboptimal demonstrations.
SentBS: Sentence-level Beam Search for Controllable Summarization
Venue: arXiv cs.CL 2023
Year: 2023

A wide range of control perspectives have been explored in controllable text generation. Structure-controlled summarization is recently proposed as a useful and interesting research direction. However, current structure-controlling methods have limited effectiveness in enforcing the desired structure. To address this limitation, we propose a sentence-level beam search generation method (SentBS), where evaluation is conducted throughout the generation process to select suitable sentences for subsequent generations. We experiment with different combinations of decoding methods to be used as subcomponents by SentBS and evaluate results on the structure-controlled dataset MReD. Experiments show that all explored combinations for SentBS can improve the agreement between the generated text and the desired structure, with the best method significantly reducing the structural discrepancies suffered by the existing model, by approximately 68%.
Unifying Data Perspectivism and Personalization: An Application to Social Norms
Venue: arXiv cs.CL 2023
Year: 2023

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most.
Quantifying the Loss of Acyclic Join Dependencies
Venue: arXiv cs.DB 2023
Year: 2023

Acyclic schemes posses known benefits for database design, speeding up queries, and reducing space requirements. An acyclic join dependency (AJD) is lossless with respect to a universal relation if joining the projections associated with the schema results in the original universal relation. An intuitive and standard measure of loss entailed by an AJD is the number of redundant tuples generated by the acyclic join. Recent work has shown that the loss of an AJD can also be characterized by an information-theoretic measure. Motivated by the problem of automatically fitting an acyclic schema to a universal relation, we investigate the connection between these two characterizations of loss. We first show that the loss of an AJD is captured using the notion of KL-Divergence. We then show that the KL-divergence can be used to bound the number of redundant tuples. We prove a deterministic lower bound on the percentage of redundant tuples. For an upper bound, we propose a random database model, and establish a high probability bound on the percentage of redundant tuples, which coincides with the lower bound for large databases.