OpenRaiser/Intern-Atlas huggingface dataset:
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.
Using Artificial Intelligence and IoT for Constructing a Smart Trash Bin
Venue: arXiv cs.CV 2022
Year: 2022

The research reported in this paper transforms a normal trash bin into a smarter one by applying computer vision technology. With the support of sensors and actuator devices, the trash bin can automatically classify garbage. In particular, a camera on the trash bin takes pictures of trash, then the central processing unit analyzes and makes decisions regarding which bin to drop trash into. The accuracy of our trash bin system achieves 90%. Besides, our model is connected to the Internet to update the bin status for further management. A mobile application is developed for managing the bin.
Multi-modal Transformer Path Prediction for Autonomous Vehicle
Venue: arXiv cs.RO 2022
Year: 2022

Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out. In addition, consecutive lane chunks are combined to ensure the lane input to be long enough for path prediction. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body
Venue: arXiv cs.CV 2022
Year: 2022

One of the possible dangers that older people face in their daily lives is falling. Occlusion is one of the biggest challenges of vision-based fall detection systems and degrades their detection performance considerably. To tackle this problem, we synthesize specifically-designed occluded videos for training fall detection systems using existing datasets. Then, by defining a new cost function, we introduce a framework for weighted training of fall detection models using occluded and un-occluded videos, which can be applied to any learnable fall detection system. Finally, we use both a non-deep and deep model to evaluate the effect of the proposed weighted training method. Experiments show that the proposed method can improve the classification accuracy by 36% for a non-deep model and 55% for a deep model in occlusion conditions. Moreover, it is shown that the proposed training framework can also significantly improve the detection performance of a deep network on normal un-occluded samples.
A Simulation Study of Passing Drivers' Responses to the Autonomous Truck-Mounted Attenuator System in Road Maintenance
Venue: arXiv cs.RO 2022
Year: 2022

The Autonomous Truck-Mounted Attenuator (ATMA) system is a lead-follower vehicle system based on autonomous driving and connected vehicle technologies. The lead truck performs maintenance tasks on the road, and the unmanned follower truck alerts passing vehicles about the moving work zone and protects workers and the equipment. While the ATMA has been under testing by transportation maintenance and operations agencies recently, a simulator-based testing capability is a supplement, especially if human subjects are involved. This paper aims to discover how passing drivers perceive, understand, and react to the ATMA system in road maintenance. With the driving simulator developed for this ATMA study, the paper performed a simulation study wherein a screen-based eye tracker collected sixteen subjects' gaze points and pupil diameters. Data analysis evidenced the change in subjects' visual attention patterns while passing the ATMA. On average, the ATMA starts to attract subjects' attention from 500 ft behind the follower truck. Most (87.50%) understood the follower truck's protection purpose, and many (60%) reasoned the association between the two trucks. Nevertheless, nearly half of the participants (43.75%) did not recognize that ATMA is a connected autonomous vehicle system. While all subjects safely changed lanes and attempted to pass the slow-moving ATMA, their inadequate understanding of the ATMA is a potential risk, like cutting into the ATAM. Results implied that transportation maintenance and operations agencies should consider this in establishing the deployment guidance.
Signed Graph Neural Networks: A Frequency Perspective
Venue: arXiv cs.LG 2022
Year: 2022

Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs containing only positive links. Many existing GCNs have been derived from the spectral domain analysis of signals lying over (unsigned) graphs and in each convolution layer they perform low-pass filtering of the input features followed by a learnable linear transformation. Their extension to signed graphs with positive as well as negative links imposes multiple issues including computational irregularities and ambiguous frequency interpretation, making the design of computationally efficient low pass filters challenging. In this paper, we address these issues via spectral analysis of signed graphs and propose two different signed graph neural networks, one keeps only low-frequency information and one also retains high-frequency information. We further introduce magnetic signed Laplacian and use its eigendecomposition for spectral analysis of directed signed graphs. We test our methods for node classification and link sign prediction tasks on signed graphs and achieve state-of-the-art performances.
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
Venue: arXiv cs.RO 2022
Year: 2022

Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other state-of-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.
Combining Predictions under Uncertainty: The Case of Random Decision Trees
Venue: arXiv cs.LG 2022
Year: 2022

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the "uncertainty about the uncertainty"). More generally, much remains unknown about how to best combine probabilistic estimates from multiple sources. In this paper, we investigate a number of alternative prediction methods. Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation. Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined. Somewhat unexpectedly, we found that taking the average over the probabilities is actually hard to beat. However, evidence accumulation showed consistently better results on all but very small leafs.
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Venue: arXiv cs.AI 2022
Year: 2022

Dental disease is one of the most common chronic diseases despite being largely preventable. However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of the current action on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been made simple in order to run stably and autonomously in a constrained, real-world setting (i.e., highly noisy, sparse data). We address this challenge by designing a quality reward which maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient engagement in oral hygiene practices.
Entity Anchored ICD Coding
Venue: arXiv cs.LG 2022
Year: 2022

Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output space. We limit our model inputs to a small window around medical entities found in our documents. From those local contexts, we build contextualized representations of both ICD codes and entities, and aggregate over these representations to form document-level predictions. In contrast to existing methods which use a representation fixed either in size or by codes seen in training, we represent ICD codes by encoding the code description with local context. We discuss metrics appropriate to deploying coding systems in practice. We show that our approach is superior to existing methods in both standard and deployable measures, including performance on rare and unseen codes.
Temporal Action Localization with Multi-temporal Scales
Venue: arXiv cs.CV 2022
Year: 2022

Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions on a feature space of a single-temporal scale. However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries. To address this issue, we propose to predict actions on a feature space of multi-temporal scales. Specifically, we use refined feature pyramids of different scales to pass semantics from high-level scales to low-level scales. Besides, to establish the long temporal scale of the entire video, we use a spatial-temporal transformer encoder to capture the long-range dependencies of video frames. Then the refined features with long-range dependencies are fed into a classifier for the coarse action prediction. Finally, to further improve the prediction accuracy, we propose to use a frame-level self attention module to refine the classification and boundaries of each action instance. Extensive experiments show that the proposed method can outperform state-of-the-art approaches on the THUMOS14 dataset and achieves comparable performance on the ActivityNet1.3 dataset. Compared with A2Net (TIP20, Avg\{0.3:0.7\}), Sub-Action (CSVT2022, Avg\{0.1:0.5\}), and AFSD (CVPR21, Avg\{0.3:0.7\}) on the THUMOS14 dataset, the proposed method can achieve improvements of 12.6\%, 17.4\% and 2.2\%, respectively
MSE-Based Transceiver Designs for RIS-Aided Communications With Hardware Impairments
Venue: arXiv cs.IT 2022
Year: 2022

It is challenging to precisely configure the phase shifts of the reflecting elements at the reconfigurable intelligent surface (RIS) due to inherent hardware impairments (HIs). In this paper, the mean square error (MSE) performance is investigated in an RIS-aided single-user multiple-input multipleoutput (MIMO) communication system with transceiver HIs and RIS phase noise. We aim to jointly optimize the transmit precoder, linear received equalizer, and RIS reflecting matrices to minimize the MSE. To tackle this problem, an iterative algorithm is proposed, wherein the beamforming matrices are alternately optimized. Specifically, for the beamforming optimization subproblem, we derive the closed-form expression of the optimal precoder and equalizer matrices. Then, for the phase shift optimization subproblem, an efficient algorithm based on the majorization-minimization (MM) method is proposed. Simulation results show that the proposed MSE-based RIS-aided transceiver scheme dramatically outperforms the conventional system algorithms that do not consider HIs at both the transceiver and the RIS.
The Moment Passing Method for Wireless Channel Capacity Estimation
Venue: arXiv cs.IT 2022
Year: 2022

Wireless network capacity can be regarded as the most important performance metric for wireless communication systems. With the fast development of wireless communication technology, future wireless systems will become more and more complicated. As a result, the channel gain matrix will become a large-dimensional random matrix, leading to an extremely high computational cost to obtain the capacity. In this paper, we propose a moment passing method (MPM) to realize the fast and accurate capacity estimation for future ultra-dense wireless systems. It can determine the capacity with quadratic complexity, which is optimal considering that the cost of a single matrix operation is not less than quadratic complexity. Moreover, it has high accuracy. The simulation results show that the estimation error of this method is below 2 percent. Finally, our method is highly general, as it is independent of the distributions of BSs and users, and the shape of network areas. More importantly, it can be applied not only to the conventional multi-user multiple input and multiple output (MU-MIMO) networks, but also to the capacity-centric networks designed for B5G/6G.
Manual-Guided Dialogue for Flexible Conversational Agents
Venue: arXiv cs.CL 2022
Year: 2022

How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In this paper, we propose a novel manual-guided dialogue scheme to alleviate these problems, where the agent learns the tasks from both dialogue and manuals. The manual is an unstructured textual document that guides the agent in interacting with users and the database during the conversation. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains. We then contribute a fully-annotated multi-domain dataset MagDial to support our scheme. It introduces three dialogue modeling subtasks: instruction matching, argument filling, and response generation. Modeling these subtasks is consistent with the human agent's behavior patterns. Experiments demonstrate that the manual-guided dialogue scheme improves data efficiency and domain scalability in building dialogue systems. The dataset and benchmark will be publicly available for promoting future research.
Don't Reinvent the Wheel: Towards Automatic Replacement of Custom Implementations with APIs
Venue: arXiv cs.SE 2022
Year: 2022

Reusing code is a common practice in software development: It helps developers speedup the implementation task while also reducing the chances of introducing bugs, given the assumption that the reused code has been tested, possibly in production. Despite these benefits, opportunities for reuse are not always in plain sight and, thus, developers may miss them. We present our preliminary steps in building RETIWA, a recommender able to automatically identify custom implementations in a given project that are good candidates to be replaced by open source APIs. RETIWA relies on a ``knowledge base'' consisting of real examples of custom implementation-to-API replacements. In this work, we present the mining strategy we tailored to automatically and reliably extract replacements of custom implementations with APIs from open source projects. This is the first step towards building the envisioned recommender.
Educating Reflective Systems Developers at Scale: Towards productive feedback in a semi-capstone large-scale software engineering course
Venue: arXiv cs.SE 2022
Year: 2022

Feedback is critical in education. This Innovative Practice Full Paper reports lessons learned from improving the quality of feedback in a semi-capstone software engineering course, with particular focus on how to deliver productive feedback in large scale during project work. The bachelor-level introduction to software engineering course is taken by about 500 students from eight study programs, organised into 72 project teams. The course aims to educate reflective systems developers. The teaching staff includes 29 teaching assistants as supervisor and product owners for teams. Project teams get feedback on seven deliverables as part of formative portfolio assessment. Students expressed frustration on feedback not being aligned, that they got critique on topics not stated in assignments and that teaching assistants were reluctant to discuss the feedback. This article provides a description of the course design, an assessment of the quality of feedback and lessons learned from three main changes: Revising assignments and rubrics, reorganising the teaching staff and increasing training of teaching assistants. In discussing the changes, we draw on a survey to students with 142 respondents, a survey to teaching assistants with 18 respondents, meeting minutes from a student reference group and experience reports from teaching assistants as well as literature and own experience. The article concludes with three actionable lessons learned for large-scale semi-capstone courses.
The Weakest Failure Detector for Genuine Atomic Multicast (Extended Version)
Venue: arXiv cs.DC 2022
Year: 2022

Atomic broadcast is a group communication primitive to order messages across a set of distributed processes. Atomic multicast is its natural generalization where each message $m$ is addressed to $dst(m)$, a subset of the processes called its destination group. A solution to atomic multicast is genuine when a process takes steps only if a message is addressed to it. Genuine solutions are the ones used in practice because they have better performance. Let $G$ be all the destination groups and $F$ be the cyclic families in it, that is the subsets of $G$ whose intersection graph is hamiltonian. This paper establishes that the weakest failure detector to solve genuine atomic multicast is $μ=(\wedge_{g,h \in G}~Σ_{g \cap h}) \wedge (\wedge_{g \in G}~Ω_g) \wedge γ$, where (i) $Σ_P$ and $Ω_P$ are the quorum and leader failure detectors restricted to the processes in $P$, and (ii) $γ$ is a new failure detector that informs the processes in a cyclic family $f \in F$ when $f$ is faulty. We also study two classical variations of atomic multicast. The first variation requires that message delivery follows the real-time order. In this case, $μ$ must be strengthened with $1^{g \cap h}$, the indicator failure detector that informs each process in $g \cup h$ when $g \cap h$ is faulty. The second variation requires a message to be delivered when the destination group runs in isolation. We prove that its weakest failure detector is at least $μ\wedge (\wedge_{g, h \in G}~Ω_{g \cap h})$. This value is attained when $F=\varnothing$.
Matching Multiple Perspectives for Efficient Representation Learning
Venue: arXiv cs.CV 2022
Year: 2022

Representation learning approaches typically rely on images of objects captured from a single perspective that are transformed using affine transformations. Additionally, self-supervised learning, a successful paradigm of representation learning, relies on instance discrimination and self-augmentations which cannot always bridge the gap between observations of the same object viewed from a different perspective. Viewing an object from multiple perspectives aids holistic understanding of an object which is particularly important in situations where data annotations are limited. In this paper, we present an approach that combines self-supervised learning with a multi-perspective matching technique and demonstrate its effectiveness on learning higher quality representations on data captured by a robotic vacuum with an embedded camera. We show that the availability of multiple views of the same object combined with a variety of self-supervised pretraining algorithms can lead to improved object classification performance without extra labels.