OpenRaiser/Intern-Atlas huggingface dataset:
FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras
Venue: arXiv cs.CV 2023
Year: 2023

Estimating the speed of vehicles using traffic cameras is a crucial task for traffic surveillance and management, enabling more optimal traffic flow, improved road safety, and lower environmental impact. Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. While there is prior research in this area reporting competitive accuracy levels, their solutions lack reproducibility and robustness across different datasets. To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness. Our model employs novel techniques to estimate the length of road segments via depth map prediction. Additionally, our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically. We compare our model to three well-known models in the field using their benchmark datasets. While our model does not set a new state of the art regarding prediction performance, the results are competitive on realistic CCTV videos. At the same time, our end-to-end pipeline offers more consistent results, an easier implementation, and better compatibility. Its modular structure facilitates reproducibility and future improvements.
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model
Venue: arXiv cs.CV 2023
Year: 2023

Image colorization is the process of colorizing grayscale images or recoloring an already-color image. This image manipulation can be used for grayscale satellite, medical and historical images making them more expressive. With the help of the increasing computation power of deep learning techniques, the colorization algorithms results are becoming more realistic in such a way that human eyes cannot differentiate between natural and colorized images. However, this poses a potential security concern, as forged or illegally manipulated images can be used illegally. There is a growing need for effective detection methods to distinguish between natural color and computer-colorized images. This paper presents a novel approach that combines the advantages of transfer and ensemble learning approaches to help reduce training time and resource requirements while proposing a model to classify natural color and computer-colorized images. The proposed model uses pre-trained branches VGG16 and Resnet50, along with Mobile Net v2 or Efficientnet feature vectors. The proposed model showed promising results, with accuracy ranging from 94.55% to 99.13% and very low Half Total Error Rate values. The proposed model outperformed existing state-of-the-art models regarding classification performance and generalization capabilities.
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Venue: arXiv cs.LG 2023
Year: 2023

Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.
"Can You Move It?": The Design and Evaluation of Moving VR Shots in Sport Broadcast
Venue: arXiv cs.HC 2023
Year: 2023

Virtual Reality (VR) broadcasting has seen widespread adoption in major sports events, attributed to its ability to generate a sense of presence, curiosity, and excitement among viewers. However, we have noticed that still shots reveal a limitation in the movement of VR cameras and hinder the VR viewing experience in current VR sports broadcasts. This paper aims to bridge this gap by engaging in a quantitative user analysis to explore the design and impact of dynamic VR shots on viewing experiences. We conducted two user studies in a digital hockey game twin environment and asked participants to evaluate their viewing experience through two questionnaires. Our findings suggested that the viewing experiences demonstrated no notable disparity between still and moving shots for single clips. However, when considering entire events, moving shots improved the viewer's immersive experience, with no notable increase in sickness compared to still shots. We further discuss the benefits of integrating moving shots into VR sports broadcasts and present a set of design considerations and potential improvements for future VR sports broadcasting.
Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors
Venue: arXiv cs.AI 2023
Year: 2023

Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset.
Dynamic Scene Graph Representation for Surgical Video
Venue: arXiv cs.CV 2023
Year: 2023

Surgical videos captured from microscopic or endoscopic imaging devices are rich but complex sources of information, depicting different tools and anatomical structures utilized during an extended amount of time. Despite containing crucial workflow information and being commonly recorded in many procedures, usage of surgical videos for automated surgical workflow understanding is still limited. In this work, we exploit scene graphs as a more holistic, semantically meaningful and human-readable way to represent surgical videos while encoding all anatomical structures, tools, and their interactions. To properly evaluate the impact of our solutions, we create a scene graph dataset from semantic segmentations from the CaDIS and CATARACTS datasets. We demonstrate that scene graphs can be leveraged through the use of graph convolutional networks (GCNs) to tackle surgical downstream tasks such as surgical workflow recognition with competitive performance. Moreover, we demonstrate the benefits of surgical scene graphs regarding the explainability and robustness of model decisions, which are crucial in the clinical setting.
ADESS: A Proof-of-Work Protocol to Deter Double-Spend Attacks
Venue: arXiv cs.CR 2023
Year: 2023

A principal vulnerability of a proof-of-work ("PoW") blockchain is that an attacker can re-write the history of transactions by forking a previously published block and build a new chain segment containing a different sequence of transactions. If the attacker's chain has the most cumulative mining puzzle difficulty, nodes will recognize it as canonical. We propose a modification to PoW protocols, called ADESS, that contains two novel features. The first modification enables a node to identify the attacker chain by comparing the temporal sequence of blocks on competing chains. The second modification penalizes the attacker by requiring it to apply exponentially increasing hashrate in order to make its chain canonical. We demonstrate two things; (i) the expected cost of carrying out a double-spend attack is weakly higher under ADESS compared to the current PoW protocols and (ii) for any value of transaction, there is a penalty setting in ADESS that renders the expected profit of a double-spend attack negative.
Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles
Venue: arXiv cs.RO 2023
Year: 2023

This paper addresses the advancements in on-road trajectory planning for Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a globally optimal route for APVs, considering various factors such as vehicle dynamics, constraints, and detected obstacles. Traditional techniques involve a combination of sampling methods followed by optimization algorithms, where the former ensures global awareness and the latter refines for local optima. Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR) optimization algorithm has recently emerged, adapted for APV systems, emphasizing improved safety and comfort. However, existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by incorporating higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk. This inclusion facilitates a richer representation in our cost and constraint design. We also address roadway compliance, emphasizing adherence to lane boundaries and directions, which past work often overlooked. Lastly, we adopt a relaxed logarithmic barrier function to address the CILQR's dependency on feasible initial trajectories. The proposed methodology is then validated through simulation and real-world experiment driving scenes in real time.
Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer
Venue: arXiv cs.SD 2023
Year: 2023

The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map. Investigating the link between weighting maps and speech acoustics can offer significant insights into the intricate process of speech production. To this end, in this work, we utilize two-dimensional spectrograms as a proxy representation, and develop an end-to-end deep learning framework for translating weighting maps to their corresponding audio waveforms. Our proposed plastic light transformer (PLT) framework is based on directional product relative position bias and single-level spatial pyramid pooling, thus enabling flexible processing of weighting maps with variable size to fixed-size spectrograms, without input information loss or dimension expansion. Additionally, our PLT framework efficiently models the global correlation of wide matrix input. To improve the realism of our generated spectrograms with relatively limited training samples, we apply pair-wise utterance consistency with Maximum Mean Discrepancy constraint and adversarial training. Experimental results on a dataset of 29 subjects speaking two utterances demonstrated that our framework is able to synthesize speech audio waveforms from weighting maps, outperforming conventional convolution and transformer models.
Progressive Text-to-3D Generation for Automatic 3D Prototyping
Venue: arXiv cs.CV 2023
Year: 2023

Text-to-3D generation is to craft a 3D object according to a natural language description. This can significantly reduce the workload for manually designing 3D models and provide a more natural way of interaction for users. However, this problem remains challenging in recovering the fine-grained details effectively and optimizing a large-size 3D output efficiently. Inspired by the success of progressive learning, we propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. As the name implies, the Multi-Scale Triplane Network consists of four triplanes transitioning from low to high resolution. The low-resolution triplane could serve as an initial shape for the high-resolution ones, easing the optimization difficulty. To further enable the fine-grained details, we also introduce the progressive learning strategy, which explicitly demands the network to shift its focus of attention from simple coarse-grained patterns to difficult fine-grained patterns. Our experiment verifies that the proposed method performs favorably against existing methods. For even the most challenging descriptions, where most existing methods struggle to produce a viable shape, our proposed method consistently delivers. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions.
Decarbonization of aviation via hydrogen propulsion: technology performance targets and energy system impacts
Venue: arXiv cs.CE 2023
Year: 2023

The aviation sector is challenging to decarbonize since aircraft require high power and energy per unit of weight. Liquid hydrogen is an interesting solution due to its high gravimetric energy density, minimal warming impact, and low-carbon production potential. We quantify the performance targets for fuel cell systems and on-board storage to enable hydrogen-powered regional aviation. We then explore the energy infrastructure impacts of meeting this additional H2 demand in the European context under deep decarbonization scenarios. We find that minimal payload reduction would be needed for powering regional aviation up to 1000 nmi if fuel cell system specific power of 2 kW/kg and tank gravimetric index of 50% can be achieved. The energy systems analysis highlights the importance of utilizing multiple technology options: such as nuclear expansion and natural gas reforming with CCS for hydrogen production. Levelized cost of liquid hydrogen as low as 3.5 Euros/kg demonstrates pathways for Europe to achieve cost-competitive production.
On The Effects of The Variations In Network Characteristics In Cyber Physical Systems
Venue: arXiv cs.RO 2023
Year: 2023

The popular robotic simulator, Gazebo, lacks the feature of simulating the effects of control latency that would make it a fully-fledged cyber-physical system (CPS) simulator. The CPS that we address to measure is a robotic arm (UR5) controlled remotely with velocity commands. The main goal is to measure Quality of Control (QoC) related KPIs during various network conditions in a simulated environment. We propose a Gazebo plugin to make the above measurement feasible by making Gazebo capable to delay internal control and status messages and also to interface with external network simulators to derive even more advanced network effects. Our preliminary evaluation shows that there is certainly an effect on the behavior of the robotic arm with the introduced network latency in line with our expectations, but a more detailed further study is needed.
On Deterministically Approximating Total Variation Distance
Venue: arXiv cs.DS 2023
Year: 2023

Total variation distance (TV distance) is an important measure for the difference between two distributions. Recently, there has been progress in approximating the TV distance between product distributions: a deterministic algorithm for a restricted class of product distributions (Bhattacharyya, Gayen, Meel, Myrisiotis, Pavan and Vinodchandran 2023) and a randomized algorithm for general product distributions (Feng, Guo, Jerrum and Wang 2023). We give a deterministic fully polynomial-time approximation algorithm (FPTAS) for the TV distance between product distributions. Given two product distributions $\mathbb{P}$ and $\mathbb{Q}$ over $[q]^n$, our algorithm approximates their TV distance with relative error $\varepsilon$ in time $O\bigl( \frac{qn^2}{\varepsilon} \log q \log \frac{n}{\varepsilon Δ_{\text{TV}}(\mathbb{P},\mathbb{Q}) } \bigr)$. Our algorithm is built around two key concepts: 1) The likelihood ratio as a distribution, which captures sufficient information to compute the TV distance. 2) We introduce a metric between likelihood ratio distributions, called the minimum total variation distance. Our algorithm computes a sparsified likelihood ratio distribution that is close to the original one w.r.t. the new metric. The approximated TV distance can be computed from the sparsified likelihood ratio. Our technique also implies deterministic FPTAS for the TV distance between Markov chains.
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement
Venue: arXiv cs.CV 2023
Year: 2023

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance.
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Venue: arXiv cs.LG 2023
Year: 2023

Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. Code will be made available at https://github.com/yuhuixu1993/qa-lora.
Optimizing delegation between human and AI collaborative agents
Venue: arXiv cs.AI 2023
Year: 2023

In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.
Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots
Venue: arXiv cs.RO 2023
Year: 2023

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.
SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices
Venue: arXiv cs.CR 2023
Year: 2023

Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operating System (Trusted OS) is the primary component. It enables the TEE to use security-based design techniques, such as data encryption and identity authentication. Once a Trusted OS has been exploited, the TEE can no longer ensure security. However, Trusted OSes for IoT devices have received little security analysis, which is challenging from several perspectives: (1) Trusted OSes are closed-source and have an unfavorable environment for sending test cases and collecting feedback. (2) Trusted OSes have complex data structures and require a stateful workflow, which limits existing vulnerability detection tools. To address the challenges, we present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices as well as tracking state and code coverage non-invasively. SyzTrust utilizes composite feedback to guide the fuzzer to effectively explore more states as well as to increase the code coverage. We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud. These systems run on Cortex M23/33 MCUs, which provide the necessary abstraction for embedded TEEs. We discovered 70 previously unknown vulnerabilities in their Trusted OSes, receiving 10 new CVEs so far. Furthermore, compared to the baseline, SyzTrust has demonstrated significant improvements, including 66% higher code coverage, 651% higher state coverage, and 31% improved vulnerability-finding capability. We report all discovered new vulnerabilities to vendors and open source SyzTrust.