OpenRaiser/Intern-Atlas huggingface dataset:
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception
Venue: arXiv cs.CV 2023
Year: 2023

Reliable object detection using cameras plays a crucial role in enabling autonomous vehicles to perceive their surroundings. However, existing camera-based object detection approaches for autonomous driving lack the ability to provide comprehensive feedback on detection performance for individual frames. To address this limitation, we propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms and provides frame-by-frame feedback on detection quality. The DQI is generated by combining the intensity of the fine-grained saliency map with the output results of the object detection algorithm. Additionally, we have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric. To validate our approach, we conducted experiments on three open-source datasets. The results demonstrate that the proposed evaluation metric accurately assesses the detection quality of camera-based systems in autonomous driving environments. Furthermore, the proposed SPA-NET outperforms other popular image-based quality regression models. This highlights the effectiveness of the DQI in evaluating a camera's ability to perceive visual scenes. Overall, our work introduces a valuable self-evaluation tool for camera-based object detection in autonomous vehicles.
Sustainable Cloud Services for Verbal Interaction with Embodied Agents
Venue: arXiv cs.RO 2023
Year: 2023

This article presents the design and the implementation of a cloud system for knowledge-based autonomous interaction devised for Social Robots and other conversational agents. The system is particularly convenient for low-cost robots and devices: it can be used as a stand-alone dialogue system or as an integration to provide "background" dialogue capabilities to any preexisting Natural Language Processing ability that the robot may already have as part of its basic skills. By connecting to the cloud, developers are provided with a sustainable solution to manage verbal interaction through a network connection, with about 3,000 topics of conversation ready for "chit-chatting" and a library of pre-cooked plans that only needs to be grounded into the robot's physical capabilities. The system is structured as a set of REST API endpoints so that it can be easily expanded by adding new APIs to improve the capabilities of the clients connected to the cloud. Another key feature of the system is that it has been designed to make the development of its clients straightforward: in this way, multiple robots and devices can be easily endowed with the capability of autonomously interacting with the user, understanding when to perform specific actions, and exploiting all the information provided by cloud services. The article outlines and discusses the results of the experiments performed to assess the system's performance in terms of response time, paving the way for its use both for research and market solutions. Links to repositories with clients for ROS and popular robots such as Pepper and NAO are available on request.
OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
Venue: arXiv cs.RO 2023
Year: 2023

Place recognition is an important capability for autonomously navigating vehicles operating in complex environments and under changing conditions. It is a key component for tasks such as loop closing in SLAM or global localization. In this paper, we address the problem of place recognition based on 3D LiDAR scans recorded by an autonomous vehicle. We propose a novel lightweight neural network exploiting the range image representation of LiDAR sensors to achieve fast execution with less than 2 ms per frame. We design a yaw-angle-invariant architecture exploiting a transformer network, which boosts the place recognition performance of our method. We evaluate our approach on the KITTI and Ford Campus datasets. The experimental results show that our method can effectively detect loop closures compared to the state-of-the-art methods and generalizes well across different environments. To evaluate long-term place recognition performance, we provide a novel dataset containing LiDAR sequences recorded by a mobile robot in repetitive places at different times. The implementation of our method and dataset are released here: https://github.com/haomo-ai/OverlapTransformer
Metaverse: Security and Privacy Concerns
Venue: arXiv cs.CY 2023
Year: 2023

The term "metaverse", a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is possible to realize the metaverse with the continuous emergence and progress of various technologies, and thus it has attracted extensive attention again. It may bring a lot of benefits to human society such as reducing discrimination, eliminating individual differences, and socializing. However, everything has security and privacy concerns, which is no exception for the metaverse. In this article, we firstly analyze the concept of the metaverse and propose that it is a super virtual-reality (VR) ecosystem compared with other VR technologies. Then, we carefully analyze and elaborate on possible security and privacy concerns from four perspectives: user information, communication, scenario, and goods, and immediately, the potential solutions are correspondingly put forward. Meanwhile, we propose the need to take advantage of the new buckets effect to comprehensively address security and privacy concerns from a philosophical perspective, which hopefully will bring some progress to the metaverse community.
Betweenness Approximation for Edge Computing with Hypergraph Neural Network
Venue: arXiv cs.SI 2023
Year: 2023

Edge computing is highly demanded to achieve their full potentials Internet of Things (IoT), since various IoT systems have been generating big data facilitating modern latency-sensitive applications. As a basic problem, network dismantling tries to find an optimal set of nodes of which will maximize the connectivity degradation in a network. However, current approaches mainly focus on simple networks modeling only pairwise interactions between two nodes, while higher order groupwise interactions among arbitrary number of nodes are ubiquitous in real world which can be better modeled as hypernetwork. The structural difference between simple network and hypernetwork restricts the direct application of simple network dismantling methods to hypernetwork. Even though some hypernetwork centrality measures such as betweenness can be used for hypernetwork dismantling, they face the problem of balancing effectiveness and efficiency. Therefore, we propose a betweenness approximation-based hypernetwork dismantling method with hypergraph neural network, namely HND. HND trains a transferable hypergraph neural network-based regression model on plenty of generated small-scale synthetic hypernetwork in a supervised way, and utilizes the well-trained model to approximate nodes' betweenness. Extensive experiments on five real hypernetworks demonstrate the effectiveness and efficiency of HND comparing with various baselines.
A Practical AoI Scheduler in IoT Networks with Relays
Venue: arXiv cs.IT 2023
Year: 2023

Internet of Things (IoT) networks have become ubiquitous as autonomous computing, communication and collaboration among devices become popular for accomplishing various tasks. The use of relays in IoT networks further makes it convenient to deploy IoT networks as relays provide a host of benefits, like increasing the communication range and minimizing power consumption. Existing literature on traditional AoI schedulers for such two-hop relayed IoT networks are limited because they are designed assuming constant/non-changing channel conditions and known (usually, generate-at-will) packet generation patterns. Deep reinforcement learning (DRL) algorithms have been investigated for AoI scheduling in two-hop IoT networks with relays, however, they are only applicable for small-scale IoT networks due to exponential rise in action space as the networks become large. These limitations discourage the practical utilization of AoI schedulers for IoT network deployments. This paper presents a practical AoI scheduler for two-hop IoT networks with relays that addresses the above limitations. The proposed scheduler utilizes a novel voting mechanism based proximal policy optimization (v-PPO) algorithm that maintains a linear action space, enabling it be scale well with larger IoT networks. The proposed v-PPO based AoI scheduler adapts well to changing network conditions and accounts for unknown traffic generation patterns, making it practical for real-world IoT deployments. Simulation results show that the proposed v-PPO based AoI scheduler outperforms both ML and traditional (non-ML) AoI schedulers, such as, Deep Q Network (DQN)-based AoI Scheduler, Maximal Age First-Maximal Age Difference (MAF-MAD), MAF (Maximal Age First) , and round-robin in all considered practical scenarios.
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Venue: arXiv cs.LG 2023
Year: 2023

Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to finish training by the same deadline dictated by the server. Our results show that training a smaller subset of the neural network (NN) at constrained devices, i.e., dropping neurons/filters as proposed by state of the art, is inefficient, preventing these devices to make an effective contribution to the model. This causes unfairness w.r.t the achievable accuracies of constrained devices, especially in cases with a skewed distribution of class labels across devices. We present a novel FL technique, CoCoFL, which maintains the full NN structure on all devices. To adapt to the devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers, reducing communication, computation, and memory requirements, whereas other layers are still trained in full precision, enabling to reach a high accuracy. Thereby, CoCoFL efficiently utilizes the available resources on devices and allows constrained devices to make a significant contribution to the FL system, increasing fairness among participants (accuracy parity) and significantly improving the final accuracy of the model.
Towards Less Constrained Macro-Neural Architecture Search
Venue: arXiv cs.CV 2023
Year: 2023

Networks found with Neural Architecture Search (NAS) achieve state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, most NAS methods heavily rely on human-defined assumptions that constrain the search: architecture's outer-skeletons, number of layers, parameter heuristics and search spaces. Additionally, common search spaces consist of repeatable modules (cells) instead of fully exploring the architecture's search space by designing entire architectures (macro-search). Imposing such constraints requires deep human expertise and restricts the search to pre-defined settings. In this paper, we propose LCMNAS, a method that pushes NAS to less constrained search spaces by performing macro-search without relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces three components for the NAS pipeline: i) a method that leverages information about well-known architectures to autonomously generate complex search spaces based on Weighted Directed Graphs with hidden properties, ii) an evolutionary search strategy that generates complete architectures from scratch, and iii) a mixed-performance estimation approach that combines information about architectures at initialization stage and lower fidelity estimates to infer their trainability and capacity to model complex functions. We present experiments in 13 different data sets showing that LCMNAS is capable of generating both cell and macro-based architectures with minimal GPU computation and state-of-the-art results. More, we conduct extensive studies on the importance of different NAS components in both cell and macro-based settings. Code for reproducibility is public at https://github.com/VascoLopes/LCMNAS.
Private Non-Convex Federated Learning Without a Trusted Server
Venue: arXiv cs.LG 2023
Year: 2023

We study federated learning (FL) -- especially cross-silo FL -- with non-convex loss functions and data from people who do not trust the server or other silos. In this setting, each silo (e.g. hospital) must protect the privacy of each person's data (e.g. patient's medical record), even if the server or other silos act as adversarial eavesdroppers. To that end, we consider inter-silo record-level (ISRL) differential privacy (DP), which requires silo~$i$'s communications to satisfy record/item-level DP. We propose novel ISRL-DP algorithms for FL with heterogeneous (non-i.i.d.) silo data and two classes of Lipschitz continuous loss functions: First, we consider losses satisfying the Proximal Polyak-Lojasiewicz (PL) inequality, which is an extension of the classical PL condition to the constrained setting. In contrast to our result, prior works only considered unconstrained private optimization with Lipschitz PL loss, which rules out most interesting PL losses such as strongly convex problems and linear/logistic regression. Our algorithms nearly attain the optimal strongly convex, homogeneous (i.i.d.) rate for ISRL-DP FL without assuming convexity or i.i.d. data. Second, we give the first private algorithms for non-convex non-smooth loss functions. Our utility bounds even improve on the state-of-the-art bounds for smooth losses. We complement our upper bounds with lower bounds. Additionally, we provide shuffle DP (SDP) algorithms that improve over the state-of-the-art central DP algorithms under more practical trust assumptions. Numerical experiments show that our algorithm has better accuracy than baselines for most privacy levels. All the codes are publicly available at: https://github.com/ghafeleb/Private-NonConvex-Federated-Learning-Without-a-Trusted-Server.
Mixed Evidence for Gestalt Grouping in Deep Neural Networks
Venue: arXiv cs.AI 2023
Year: 2023

Gestalt psychologists have identified a range of conditions in which humans organize elements of a scene into a group or whole, and perceptual grouping principles play an essential role in scene perception and object identification. Recently, Deep Neural Networks (DNNs) trained on natural images (ImageNet) have been proposed as compelling models of human vision based on reports that they perform well on various brain and behavioral benchmarks. Here we test a total of 16 networks covering a variety of architectures and learning paradigms (convolutional, attention-based, supervised and self-supervised, feed-forward and recurrent) on dots (Experiment 1) and more complex shapes (Experiment 2) stimuli that produce strong Gestalts effects in humans. In Experiment 1 we found that convolutional networks were indeed sensitive in a human-like fashion to the principles of proximity, linearity, and orientation, but only at the output layer. In Experiment 2, we found that most networks exhibited Gestalt effects only for a few sets, and again only at the latest stage of processing. Overall, self-supervised and Vision-Transformer appeared to perform worse than convolutional networks in terms of human similarity. Remarkably, no model presented a grouping effect at the early or intermediate stages of processing. This is at odds with the widespread assumption that Gestalts occur prior to object recognition, and indeed, serve to organize the visual scene for the sake of object recognition. Our overall conclusion is that, albeit noteworthy that networks trained on simple 2D images support a form of Gestalt grouping for some stimuli at the output layer, this ability does not seem to transfer to more complex features. Additionally, the fact that this grouping only occurs at the last layer suggests that networks learn fundamentally different perceptual properties than humans.
Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia
Venue: arXiv cs.SI 2023
Year: 2023

On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from layman to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of over 63 million tweets, from February 22, 2022 through March 8, 2022 that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia and will be maintained and regularly updated as the war continues to unfold. Our preliminary analysis already shows evidence of public engagement with Russian state sponsored media and other domains that are known to push unreliable information; the former saw a spike in activity on the day of the Russian invasion. Our hope is that this public dataset can help the research community to further understand the ever evolving role that social media plays in information dissemination, influence campaigns, grassroots mobilization, and much more, during a time of conflict.
Distraction is All You Need for Fairness
Venue: arXiv cs.LG 2023
Year: 2023

Bias in training datasets must be managed for various groups in classification tasks to ensure parity or equal treatment. With the recent growth in artificial intelligence models and their expanding role in automated decision-making, ensuring that these models are not biased is vital. There is an abundance of evidence suggesting that these models could contain or even amplify the bias present in the data on which they are trained, inherent to their objective function and learning algorithms; Many researchers direct their attention to this issue in different directions, namely, changing data to be statistically independent, adversarial training for restricting the capabilities of a particular competitor who aims to maximize parity, etc. These methods result in information loss and do not provide a suitable balance between accuracy and fairness or do not ensure limiting the biases in training. To this end, we propose a powerful strategy for training deep learning models called the Distraction module, which can be theoretically proven effective in controlling bias from affecting the classification results. This method can be utilized with different data types (e.g., Tabular, images, graphs, etc.). We demonstrate the potency of the proposed method by testing it on UCI Adult and Heritage Health datasets (tabular), POKEC-Z, POKEC-N and NBA datasets (graph), and CelebA dataset (vision). Using state-of-the-art methods proposed in the fairness literature for each dataset, we exhibit our model is superior to these proposed methods in minimizing bias and maintaining accuracy.
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Venue: arXiv cs.CL 2023
Year: 2023

Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between hallucinations and training data, and find evidence that models hallucinate because they train on target summaries that are unsupported by the source. Based on our findings, we present PINOCCHIO, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations. Given the model states and outputs at a given step, PINOCCHIO detects likely model hallucinations based on various measures of attribution to the source text. PINOCCHIO backtracks to find more consistent output, and can opt to produce no summary at all when no consistent generation can be found. In experiments, we find that PINOCCHIO improves the consistency of generation (in terms of F1) by an average of~67% on two abstractive summarization datasets.
Stochastic Game with Interactive Information Acquisition: Pipelined Perfect Markov Bayesian Equilibrium
Venue: arXiv cs.GT 2023
Year: 2023

This paper studies a multi-player, general-sum stochastic game characterized by a dual-stage temporal structure per period. The agents face uncertainty regarding the time-evolving state that is realized at the beginning of each period. During the first stage, agents engage in information acquisition regarding the unknown state. Each agent strategically selects from multiple signaling options, each carrying a distinct cost. The selected signaling rule dispenses private information that determines the type of the agent. In the second stage, the agents play a Bayesian game by taking actions contingent on their private types. We introduce an equilibrium concept, Pipelined Perfect Markov Bayesian Equilibrium (PPME), which incorporates the Markov perfect equilibrium and the perfect Bayesian equilibrium. We propose a novel equilibrium characterization principle termed fixed-point alignment and deliver a set of verifiable necessary and sufficient conditions for any strategy profile to achieve PPME.
Should Users Trust Their Android Devices? A Scoring System for Assessing Security and Privacy Risks of Pre-Installed Applications
Venue: arXiv cs.CR 2023
Year: 2023

Android devices are equipped with many pre-installed applications which have the capability of tracking and monitoring users. Although applications coming pre-installed pose a great danger to user security and privacy, they have received little attention so far among researchers in the field. In this study, we collect a dataset comprising such applications and make it publicly available. Using this dataset, we analyze tracker SDKs, manifest files and the use of cloud services and report our results. We also conduct a user survey to understand concerns and perceptions of users. Last but not least, we present a risk scoring system which assigns scores for smart phones consolidating our findings based on carefully weighted criteria. With this scoring system, users could give their own trust decisions based on the available concise information about the security and privacy impacts of applications pre-installed on their Android devices.
FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
Venue: arXiv cs.CV 2023
Year: 2023

Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs depending on the sensor combinations or setups. In this work, we propose the first unified end-to-end sensor fusion framework for 3D detection, named FUTR3D, which can be used in (almost) any sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature Sampler (MAFS), together with a transformer decoder with a set-to-set loss for 3D detection, thus avoiding using late fusion heuristics and post-processing tricks. We validate the effectiveness of our framework on various combinations of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On NuScenes dataset, FUTR3D achieves better performance over specifically designed methods across different sensor combinations. Moreover, FUTR3D achieves great flexibility with different sensor configurations and enables low-cost autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D (58.0 mAP) achieves on par performance with state-of-the-art 3D detection model CenterPoint (56.6 mAP) using a 32-beam LiDAR.