OpenRaiser/Intern-Atlas huggingface dataset:
An Enhanced Sampling-Based Method With Modified Next-Best View Strategy For 2D Autonomous Robot Exploration
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous exploration is a new technology in the field of robotics that has found widespread application due to its objective to help robots independently localize, scan maps, and navigate any terrain without human control. Up to present, the sampling-based exploration strategies have been the most effective for aerial and ground vehicles equipped with depth sensors producing three-dimensional point clouds. Those methods utilize the sampling task to choose random points or make samples based on Rapidly-exploring Random Trees (RRT). Then, they decide on frontiers or Next Best Views (NBV) with useful volumetric information. However, most state-of-the-art sampling-based methodology is challenging to implement in two-dimensional robots due to the lack of environmental knowledge, thus resulting in a bad volumetric gain for evaluating random destinations. This study proposed an enhanced sampling-based solution for indoor robot exploration to decide Next Best View (NBV) in 2D environments. Our method makes RRT until have the endpoints as frontiers and evaluates those with the enhanced utility function. The volumetric information obtained from environments was estimated using non-uniform distribution to determine cells that are occupied and have an uncertain probability. Compared to the sampling-based Frontier Detection and Receding Horizon NBV approaches, the methodology executed performed better in Gazebo platform-simulated environments, achieving a significantly larger explored area, with the average distance and time traveled being reduced. Moreover, the operated proposed method on an author-built 2D robot exploring the entire natural environment confirms that the method is effective and applicable in real-world scenarios.
Computation of Rate-Distortion-Perception Function under f-Divergence Perception Constraints
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of f-divergences. For that, we leverage the fact that RDPF, assuming mild regularity conditions on the perception constraint, forms a convex programming problem. We first develop parametric characterizations of the optimal solution and utilize them in an alternating minimization approach for which we prove convergence guarantees. The resulting structure of the iterations of the alternating minimization approach renders the implementation of a generalized Blahut-Arimoto (BA) type of algorithm infeasible. To overcome this difficulty, we propose a relaxed formulation of the structure of the iterations in the alternating minimization approach, which allows for the implementation of an approximate iterative scheme. This approximation is shown, via the derivation of necessary and sufficient conditions, to guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and the perception constraints which guarantee that our algorithm converges exponentially fast. We corroborate our theoretical results with numerical simulations, and we draw connections with existing results.
$t$-PIR Schemes with Flexible Parameters via Star Products of Berman Codes
Venue: arXiv cs.IT 2023
Year: 2023

We present a new class of private information retrieval (PIR) schemes that keep the identity of the file requested private in the presence of at most $t$ colluding servers, based on the recent framework developed for such $t$-PIR schemes using star products of transitive codes. These $t$-PIR schemes employ the class of Berman codes as the storage-retrieval code pairs. Berman codes, which are binary linear codes of length $n^m$ for any $n\geq 2$ and $m\geq 1$ being positive integers, were recently shown to achieve the capacity of the binary erasure channel. We provide a complete characterization of the star products of the Berman code pairs, enabling us to calculate the PIR rate of the star product-based schemes that employ these codes. The schemes we present have flexibility in the number of servers, the PIR rate, the storage rate, and the collusion parameter $t$, owing to numerous codes available in the class of Berman codes.
A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit
Venue: arXiv cs.LG 2023
Year: 2023

The issue of over-limit during passenger aircraft flights has drawn increasing attention in civil aviation due to its potential safety risks. To address this issue, real-time automated warning systems are essential. In this study, a real-time warning model for civil aviation over-limit is proposed based on QAR data monitoring. Firstly, highly correlated attributes to over-limit are extracted from a vast QAR dataset using the Spearman rank correlation coefficient. Because flight over-limit poses a binary classification problem with unbalanced samples, this paper incorporates cost-sensitive learning in the LSTM model. Finally, the time step length, number of LSTM cells, and learning rate in the LSTM model are optimized using a grid search approach. The model is trained on a real dataset, and its performance is evaluated on a validation set. The experimental results show that the proposed model achieves an F1 score of 0.991 and an accuracy of 0.978, indicating its effectiveness in real-time warning of civil aviation over-limit.
ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration
Venue: arXiv cs.RO 2023
Year: 2023

Human-robot collaboration (HRC) has become increasingly relevant in industrial, household, and commercial settings. However, the effectiveness of such collaborations is highly dependent on the human and robots' situational awareness of the environment. Improving this awareness includes not only aligning perceptions in a shared workspace, but also bidirectionally communicating intent and visualizing different states of the environment to enhance scene understanding. In this paper, we propose ARDIE (Augmented Reality with Dialogue and Eye Gaze), a novel intelligent agent that leverages multi-modal feedback cues to enhance HRC. Our system utilizes a decision theoretic framework to formulate a joint policy that incorporates interactive augmented reality (AR), natural language, and eye gaze to portray current and future states of the environment. Through object-specific AR renders, the human can visualize future object interactions to make adjustments as needed, ultimately providing an interactive and efficient collaboration between humans and robots.
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields
Venue: arXiv cs.CV 2023
Year: 2023

Vision transformers (ViTs) that model an image as a sequence of partitioned patches have shown notable performance in diverse vision tasks. Because partitioning patches eliminates the image structure, to reflect the order of patches, ViTs utilize an explicit component called positional embedding. However, we claim that the use of positional embedding does not simply guarantee the order-awareness of ViT. To support this claim, we analyze the actual behavior of ViTs using an effective receptive field. We demonstrate that during training, ViT acquires an understanding of patch order from the positional embedding that is trained to be a specific pattern. Based on this observation, we propose explicitly adding a Gaussian attention bias that guides the positional embedding to have the corresponding pattern from the beginning of training. We evaluated the influence of Gaussian attention bias on the performance of ViTs in several image classification, object detection, and semantic segmentation experiments. The results showed that proposed method not only facilitates ViTs to understand images but also boosts their performance on various datasets, including ImageNet, COCO 2017, and ADE20K.
Random Linear Network Coding for Non-Orthogonal Multiple Access in Multicast Optical Wireless Systems
Venue: arXiv cs.IT 2023
Year: 2023

Optical Wireless Communication networks (OWC) has emerged as a promising technology that enables high-speed and reliable communication bandwidth for a variety of applications. In this work, we investigated applying Random Linear Network Coding (RLNC) over NOMA-based OWC networks to improve the performance of the proposed high density indoor optical wireless network where users are divided into multicast groups, and each group contains users that slightly differ in their channel gains. Moreover, a fixed power allocation strategy is considered to manage interference among these groups and to avoid complexity. The performance of the proposed RLNC-NOMA scheme is evaluated in terms of average bit error rate and ergodic sum rate versus the power allocation ratio factor. The results show that the proposed scheme is more suitable for the considered network compared to the traditional NOMA and orthogonal transmission schemes.
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
Venue: arXiv cs.CV 2023
Year: 2023

We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-efficiently fine-tuned from OpenFlamingo, with Low-rank Adapter (LoRA) added both in the cross-attention part and the self-attention part of the language model. We first construct instruction templates with vision and language data for multi-modality instruction tuning to make the model understand and follow human instructions. We find the quality of training data is vital for the dialogue performance, where few data containing short answers can lead the model to respond shortly to any instructions. To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly. The joint training of language-only and visual-language instructions with the \emph{same} instruction template effectively improves dialogue performance. Various demos show the ability of continuous dialogue of MultiModal-GPT with humans. Code, dataset, and demo are at https://github.com/open-mmlab/Multimodal-GPT
Proof of principle for a self-governing prediction and forecasting reward algorithm
Venue: arXiv cs.SI 2023
Year: 2023

We use Monte Carlo techniques to simulate an organized prediction competition between a group of a scientific experts acting under the influence of a ``self-governing'' prediction reward algorithm. Our aim is to illustrate the advantages of a specific type of reward distribution rule that is designed to address some of the limitations of traditional forecast scoring rules. The primary extension of this algorithm as compared with standard forecast scoring is that it incorporates measures of both group consensus and question relevance directly into the reward distribution algorithm. Our model of the prediction competition includes parameters that control both the level of bias from prior beliefs and the influence of the reward incentive. The Monte Carlo simulations demonstrate that, within the simplifying assumptions of the the model, experts collectively approach belief in objectively true facts, so long as reward influence is high and the bias stays below a critical threshold. The purpose of this work is to motivate further research into prediction reward algorithms that combine standard forecasting measures with factors like bias and consensus.
Reinforcement Learning for Topic Models
Venue: arXiv cs.CL 2023
Year: 2023

We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We train the system with a policy gradient algorithm REINFORCE. Additionally, we introduced several modifications: modernize the neural network architecture, weight the ELBO loss, use contextual embeddings, and monitor the learning process via computing topic diversity and coherence for each training step. Experiments are performed on 11 data sets. Our unsupervised model outperforms all other unsupervised models and performs on par with or better than most models using supervised labeling. Our model is outperformed on certain data sets by a model using supervised labeling and contrastive learning. We have also conducted an ablation study to provide empirical evidence of performance improvements from changes we made to ProdLDA and found that the reinforcement learning formulation boosts performance.
Steam Recommendation System
Venue: arXiv cs.IR 2023
Year: 2023

We aim to leverage the interactions between users and items in the Steam community to build a game recommendation system that makes personalized suggestions to players in order to boost Steam's revenue as well as improve the users' gaming experience. The whole project is built on Apache Spark and deals with Big Data. The final output of the project is a recommendation system that gives a list of the top 5 items that the users will possibly like.6
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
Venue: arXiv cs.IR 2023
Year: 2023

Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditional methods typically learn fixed feature representations without dynamically refining feature representations according to the context information, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components: (I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context; (II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-art performance among current CTR methods.
Information Mutation and Spread of Misinformation in Timely Gossip Networks
Venue: arXiv cs.IT 2023
Year: 2023

We consider a network of $n$ user nodes that receives updates from a source and employs an age-based gossip protocol for faster dissemination of version updates to all nodes. When a node forwards its packet to another node, the packet information gets mutated with probability $p$ during transmission, creating misinformation. The receiver node does not know whether an incoming packet information is different from the packet information originally at the sender node. We assume that truth prevails over misinformation, and therefore, when a receiver encounters both accurate information and misinformation corresponding to the same version, the accurate information gets chosen for storage at the node. We study the expected fraction of nodes with correct information in the network and version age at the nodes in this setting using stochastic hybrid systems (SHS) modelling and study their properties. We observe that very high or very low gossiping rates help curb misinformation, and misinformation spread is higher with moderate gossiping rates. We support our theoretical findings with simulation results which shed further light on the behavior of above quantities.
The Computational Complexity of Equilibria with Strategic Constraints
Venue: arXiv cs.CC 2023
Year: 2023

Computational aspects of solution notions such as Nash equilibrium have been extensively studied, including settings where the ultimate goal is to find an equilibrium that possesses some additional properties. Furthermore, in order to address issues of tractability, attention has been given to approximate versions of these problems. Our work extends this direction by considering games with constraints in which players are subject to some form of restrictions on their strategic choices. We also consider the relationship between Nash equilibria and so-called constrained or social equilibria in this context, with particular attention to how they are related with respect to totality and complexity. Our results demonstrate that the computational complexity of finding an equilibrium varies significantly between games with slightly different strategic constraints. In addition to examining the computational aspects of such strategic constraints, we also demonstrate that these constraints are useful for modeling problems involving strategic resource allocation and also are of interest from the perspective of behavioral game theory.
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
Venue: arXiv cs.LG 2023
Year: 2023

Knowledge distillation is a popular technique to transfer knowledge from large teacher models to a small student model. Typically, the student learns to imitate the teacher by minimizing the KL divergence of its output distribution with the teacher's output distribution. In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution. Therefore, forcing the student to blindly imitate the unreliable teacher output distribution leads to inferior performance. To this end, we propose a novel knowledge distillation objective PTLoss by first representing the vanilla KL-based distillation loss function via a Maclaurin series and then perturbing the leading-order terms in this series. This perturbed loss implicitly transforms the original teacher into a proxy teacher with a distribution closer to the ground truth distribution. We establish the theoretical connection between this "distribution closeness" and the student model generalizability, which enables us to select the PTLoss's perturbation coefficients in a principled way. Extensive experiments on five datasets demonstrate PTLoss can significantly improve the distillation effectiveness for teachers of various scales.
A Survey on AI/ML-Driven Intrusion and Misbehavior Detection in Networked Autonomous Systems: Techniques, Challenges and Opportunities
Venue: arXiv cs.NI 2023
Year: 2023

AI/ML-based intrusion detection systems (IDSs) and misbehavior detection systems (MDSs) have shown great potential in identifying anomalies in the network traffic of networked autonomous systems. Despite the vast research efforts, practical deployments of such systems in the real world have been limited. Although the safety-critical nature of autonomous systems and the vulnerability of learning-based techniques to adversarial attacks are among the potential reasons, the lack of objective evaluation and feasibility assessment metrics is one key reason behind the limited adoption of these systems in practical settings. This survey aims to address the aforementioned limitation by presenting an in-depth analysis of AI/ML-based IDSs/MDSs and establishing baseline metrics relevant to networked autonomous systems. Furthermore, this work thoroughly surveys recent studies in this domain, highlighting the evaluation metrics and gaps in the current literature. It also presents key findings derived from our analysis of the surveyed papers and proposes guidelines for providing AI/ML-based IDS/MDS solution approaches suitable for vehicular network applications. Our work provides researchers and practitioners with the needed tools to evaluate the feasibility of AI/ML-based IDS/MDS techniques in real-world settings, with the aim of facilitating the practical adoption of such techniques in emerging autonomous vehicular systems.
Crack Detection of Asphalt Concrete Using Combined Fracture Mechanics and Digital Image Correlation
Venue: arXiv cs.CV 2023
Year: 2023

Cracking is a common failure mode in asphalt concrete (AC) pavements. Many tests have been developed to characterize the fracture behavior of AC. Accurate crack detection during testing is crucial to describe AC fracture behavior. This paper proposed a framework to detect surface cracks in AC specimens using two-dimensional digital image correlation (DIC). Two significant drawbacks in previous research in this field were addressed. First, a multi-seed incremental reliability-guided DIC was proposed to solve the decorrelation issue due to large deformation and discontinuities. The method was validated using synthetic deformed images. A correctly implemented analysis could accurately measure strains up to 450\%, even with significant discontinuities (cracks) present in the deformed image. Second, a robust method was developed to detect cracks based on displacement fields. The proposed method uses critical crack tip opening displacement ($δ_c$) to define the onset of cleavage fracture. The proposed method relies on well-developed fracture mechanics theory. The proposed threshold $δ_c$ has a physical meaning and can be easily determined from DIC measurement. The method was validated using an extended finite element model. The framework was implemented to measure the crack propagation rate while conducting the Illinois-flexibility index test on two AC mixes. The calculated rates could distinguish mixes based on their cracking potential. The proposed framework could be applied to characterize AC cracking phenomenon, evaluate its fracture properties, assess asphalt mixture testing protocols, and develop theoretical models.
Atmospheric Turbulence Correction via Variational Deep Diffusion
Venue: arXiv cs.CV 2023
Year: 2023

Atmospheric Turbulence (AT) correction is a challenging restoration task as it consists of two distortions: geometric distortion and spatially variant blur. Diffusion models have shown impressive accomplishments in photo-realistic image synthesis and beyond. In this paper, we propose a novel deep conditional diffusion model under a variational inference framework to solve the AT correction problem. We use this framework to improve performance by learning latent prior information from the input and degradation processes. We use the learned information to further condition the diffusion model. Experiments are conducted in a comprehensive synthetic AT dataset. We show that the proposed framework achieves good quantitative and qualitative results.