OpenRaiser/Intern-Atlas huggingface dataset:
Space-Efficient Huffman Codes Revisited
Venue: arXiv cs.DS 2021
Year: 2021

Canonical Huffman code is an optimal prefix-free compression code whose codewords enumerated in the lexicographical order form a list of binary words in non-decreasing lengths. Gagie et al. (2015) gave a representation of this coding capable to encode or decode a symbol in constant worst case time. It uses $σ\lg \ell_{\text{max}} + o(σ) + O(\ell_{\text{max}}^2)$ bits of space, where $σ$ and $\ell_{\text{max}}$ are the alphabet size and maximum codeword length, respectively. We refine their representation to reduce the space complexity to $σ\lg \ell_{\text{max}} (1 + o(1))$ bits while preserving the constant encode and decode times. Our algorithmic idea can be applied to any canonical code.
Silhouette based View embeddings for Gait Recognition under Multiple Views
Venue: arXiv cs.CV 2021
Year: 2021

Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information of view angle is ignored to some extent. Instead of direct view estimation and training view-specific recognition models, we propose a compatible framework that can embed view information into existing architectures of gait recognition. The embedding is simply achieved by a selective projection layer. Experimental results on two large public datasets show that the proposed framework is very effective.
Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases
Venue: arXiv cs.RO 2021
Year: 2021

This work presents the most recent advances of the Robotic Testbed for Rendezvous and Optical Navigation (TRON) at Stanford University - the first robotic testbed capable of validating machine learning algorithms for spaceborne optical navigation. The TRON facility consists of two 6 degrees-of-freedom KUKA robot arms and a set of Vicon motion track cameras to reconfigure an arbitrary relative pose between a camera and a target mockup model. The facility includes multiple Earth albedo light boxes and a sun lamp to recreate the high-fidelity spaceborne illumination conditions. After the overview of the facility, this work details the multi-source calibration procedure which enables the estimation of the relative pose between the object and the camera with millimeter-level position and millidegree-level orientation accuracies. Finally, a comparative analysis of the synthetic and TRON simulated imageries is performed using a Convolutional Neural Network (CNN) pre-trained on the synthetic images. The result shows a considerable gap in the CNN's performance, suggesting the TRON simulated images can be used to validate the robustness of any machine learning algorithms trained on more easily accessible synthetic imagery from computer graphics.
A Survey of Static Formal Methods for Building Dependable Industrial Automation Systems
Venue: arXiv cs.SE 2021
Year: 2021

Industrial automation systems (IAS) need to be highly dependable; they should not merely function as expected but also do so in a reliable, safe, and secure manner. Formal methods are mathematical techniques that can greatly aid in developing dependable systems and can be used across all phases of the system development life cycle (SDLC), including requirements engineering, system design and implementation, verification and validation (testing), maintenance, and even documentation. This state-of-the-art survey reports existing formal approaches for creating more dependable IAS, focusing on static formal methods that are used before a system is completely implemented. We categorize surveyed works based on the phases of the SDLC, allowing us to identify research gaps and promising future directions for each phase.
Combinatorial Resources Auction in Decentralized Edge-Thing Systems Using Blockchain and Differential Privacy
Venue: arXiv cs.NI 2021
Year: 2021

With the continuous expansion of Internet of Things (IoT) devices, edge computing mode has emerged in recent years to overcome the shortcomings of traditional cloud computing mode, such as high delay, network congestion, and large resource consumption. Thus, edge-thing systems will replace the classic cloud-thing/cloud-edge-thing systems and become mainstream gradually, where IoT devices can offload their tasks to neighboring edge nodes. A common problem is how to utilize edge computing resources. For the sake of fairness, double auction can be used in the edge-thing system to achieve an effective resource allocation and pricing mechanism. Due to the lack of third-party management agencies and mutual distrust between nodes, in our edge-thing systems, we introduce blockchains to prevent malicious nodes from tampering with transaction records and smart contracts to act as an auctioneer to realize resources auction. Since the auction results stored in this blockchain-based system are transparent, they are threatened with inference attacks. Thus in this paper, we design a differentially private combinatorial double auction mechanism by exploring the exponential mechanism such that maximizing the revenue of edge computing platform, in which each IoT device requests a resource bundle and edge nodes compete with each other to provide resources. It can not only guarantee approximate truthfulness and high revenue, but also ensure privacy security. Through necessary theoretical analysis and numerical simulations, the effectiveness of our proposed mechanisms can be validated.
A Contract Theory based Incentive Mechanism for Federated Learning
Venue: arXiv cs.DC 2021
Year: 2021

Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server and FL server offloads the task to the contributing FL clients. It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients. This paper aims to propose a contract theory based FL task training model towards minimizing incentive budget subject to clients being individually rational (IR) and incentive compatible (IC) in each FL training round. We design a two-dimensional contract model by formally defining two private types of clients, namely data quality and computation effort. To effectively aggregate the trained models, a contract-based aggregator is proposed. We analyze the feasible and optimal contract solutions to the proposed contract model. %Experimental results demonstrate that the proposed framework and contract model can effective improve the generation accuracy of FL tasks. Experimental results show that the generalization accuracy of the FL tasks can be improved by the proposed incentive mechanism where contract-based aggregation is applied.
Common Investigation Process Model for Internet of Things Forensics
Venue: arXiv cs.CY 2021
Year: 2021

Internet of Things Forensics (IoTFs) is a new discipline in digital forensics science used in the detection, acquisition, preservation, rebuilding, analyzing, and the presentation of evidence from IoT environments. IoTFs discipline still suffers from several issues and challenges that have in the recent past been documented. For example, heterogeneity of IoT infrastructures has mainly been a key challenge. The heterogeneity of the IoT infrastructures makes the IoTFs very complex, and ambiguous among various forensic domain. This paper aims to propose a common investigation processes for IoTFs using the metamodeling method called Common Investigation Process Model (CIPM) for IoTFs. The proposed CIPM consists of four common investigation processes: i) preparation process, ii) collection process, iii) analysis process and iv) final report process. The proposed CIPM can assist IoTFs users to facilitate, manage, and organize the investigation tasks.
perf4sight: A toolflow to model CNN training performance on Edge GPUs
Venue: arXiv cs.LG 2021
Year: 2021

The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence. In the domain of vision, the ability to adapt a Convolutional Neural Network's (CNN) structure and parameters to the input data distribution leads to systems with lower memory footprint, latency and power consumption. However, due to the limited compute resources and memory budget on edge devices, it is necessary for the system to be able to predict the latency and memory footprint of the training process in order to identify favourable training configurations of the network topology and device combination for efficient network adaptation. This work proposes perf4sight, an automated methodology for developing accurate models that predict CNN training memory footprint and latency given a target device and network. This enables rapid identification of network topologies that can be retrained on the edge device with low resource consumption. With PyTorch as the framework and NVIDIA Jetson TX2 as the target device, the developed models predict training memory footprint and latency with 95% and 91% accuracy respectively for a wide range of networks, opening the path towards efficient network adaptation on edge GPUs.
Viewing Vision Videos Online: Opportunities for Distributed Stakeholders
Venue: arXiv cs.SE 2021
Year: 2021

Creating shared understanding between stakeholders is essential for the success of software projects. Conflicting requirements originating from misaligned mental models can hinder the development process. The use of videos to present abstract system visions is one approach to counteract this problem. These videos are usually shown in in-person meetings. However, face-to-face meetings are not suited to every situation and every stakeholder, for example due to scheduling constraints. Methods for the use of vision videos in online settings are necessary. Furthermore, methods enabling an asynchronous use of vision videos are needed for cases when conjoined meetings are impossible even in an online setting. In this paper, we compare synchronous and asynchronous viewings of vision videos in online settings. The two methods are piloted in a preliminary experiment. The results show a difference in the amount of arguments regarding the presented visions. On average, participants who took part in asynchronous meetings stated more arguments. Our results point to multiple advantages and disadvantages as well as use cases for each type. For example, a synchronous meeting could be chosen when all involved stakeholders can attend the appointment to discuss the vision and to quickly resolve ambiguities. An asynchronous meeting could be held if a joint meeting is not feasible due to time constraints. We also discuss how our findings can be applied to the elicitation of requirements from a crowd of stakeholders.
Cascade Bagging for Accuracy Prediction with Few Training Samples
Venue: arXiv cs.CV 2021
Year: 2021

Accuracy predictor is trained to predict the validation accuracy of an network from its architecture encoding. It can effectively assist in designing networks and improving Neural Architecture Search(NAS) efficiency. However, a high-performance predictor depends on adequate trainning samples, which requires unaffordable computation overhead. To alleviate this problem, we propose a novel framework to train an accuracy predictor under few training samples. The framework consists ofdata augmentation methods and an ensemble learning algorithm. The data augmentation methods calibrate weak labels and inject noise to feature space. The ensemble learning algorithm, termed cascade bagging, trains two-level models by sampling data and features. In the end, the advantages of above methods are proved in the Performance Prediciton Track of CVPR2021 1st Lightweight NAS Challenge. Our code is made public at: https://github.com/dlongry/Solutionto-CVPR2021-NAS-Track2.
FreaAI: Automated extraction of data slices to test machine learning models
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning (ML) solutions are prevalent. However, many challenges exist in making these solutions business-grade. One major challenge is to ensure that the ML solution provides its expected business value. In order to do that, one has to bridge the gap between the way ML model performance is measured and the solution requirements. In previous work (Barash et al, "Bridging the gap...") we demonstrated the effectiveness of utilizing feature models in bridging this gap. Whereas ML performance metrics, such as the accuracy or F1-score of a classifier, typically measure the average ML performance, feature models shed light on explainable data slices that are too far from that average, and therefore might indicate unsatisfied requirements. For example, the overall accuracy of a bank text terms classifier may be very high, say $98\% \pm 2\%$, yet it might perform poorly for terms that include short descriptions and originate from commercial accounts. A business requirement, which may be implicit in the training data, may be to perform well regardless of the type of account and length of the description. Therefore, the under-performing data slice that includes short descriptions and commercial accounts suggests poorly-met requirements. In this paper we show the feasibility of automatically extracting feature models that result in explainable data slices over which the ML solution under-performs. Our novel technique, IBM FreaAI aka FreaAI, extracts such slices from structured ML test data or any other labeled data. We demonstrate that FreaAI can automatically produce explainable and statistically-significant data slices over seven open datasets.
Generation Challenges: Results of the Accuracy Evaluation Shared Task
Venue: arXiv cs.CL 2021
Year: 2021

The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this task, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).
Correlation Clustering with Asymmetric Classification Errors
Venue: arXiv cs.DS 2021
Year: 2021

In the Correlation Clustering problem, we are given a weighted graph $G$ with its edges labeled as "similar" or "dissimilar" by a binary classifier. The goal is to produce a clustering that minimizes the weight of "disagreements": the sum of the weights of "similar" edges across clusters and "dissimilar" edges within clusters. We study the correlation clustering problem under the following assumption: Every "similar" edge $e$ has weight $\mathbf{w}_e\in[α\mathbf{w}, \mathbf{w}]$ and every "dissimilar" edge $e$ has weight $\mathbf{w}_e\geq α\mathbf{w}$ (where $α\leq 1$ and $\mathbf{w}>0$ is a scaling parameter). We give a $(3 + 2 \log_e (1/α))$ approximation algorithm for this problem. This assumption captures well the scenario when classification errors are asymmetric. Additionally, we show an asymptotically matching Linear Programming integrality gap of $Ω(\log 1/α)$.
An Approach to Partial Observability in Games: Learning to Both Act and Observe
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) is successful at learning to play games where the entire environment is visible. However, RL approaches are challenged in complex games like Starcraft II and in real-world environments where the entire environment is not visible. In these more complex games with more limited visual information, agents must choose where to look and how to optimally use their limited visual information in order to succeed at the game. We verify that with a relatively simple model the agent can learn where to look in scenarios with a limited visual bandwidth. We develop a method for masking part of the environment in Atari games to force the RL agent to learn both where to look and how to play the game in order to study where the RL agent learns to look. In addition, we develop a neural network architecture and method for allowing the agent to choose where to look and what action to take in the Pong game. Further, we analyze the strategies the agent learns to better understand how the RL agent learns to play the game.
To Rate or Not To Rate: Investigating Evaluation Methods for Generated Co-Speech Gestures
Venue: arXiv cs.HC 2021
Year: 2021

While automatic performance metrics are crucial for machine learning of artificial human-like behaviour, the gold standard for evaluation remains human judgement. The subjective evaluation of artificial human-like behaviour in embodied conversational agents is however expensive and little is known about the quality of the data it returns. Two approaches to subjective evaluation can be largely distinguished, one relying on ratings, the other on pairwise comparisons. In this study we use co-speech gestures to compare the two against each other and answer questions about their appropriateness for evaluation of artificial behaviour. We consider their ability to rate quality, but also aspects pertaining to the effort of use and the time required to collect subjective data. We use crowd sourcing to rate the quality of co-speech gestures in avatars, assessing which method picks up more detail in subjective assessments. We compared gestures generated by three different machine learning models with various level of behavioural quality. We found that both approaches were able to rank the videos according to quality and that the ranking significantly correlated, showing that in terms of quality there is no preference of one method over the other. We also found that pairwise comparisons were slightly faster and came with improved inter-rater reliability, suggesting that for small-scale studies pairwise comparisons are to be favoured over ratings.
Exploration of lane-changing duration for heavy vehicles and passenger cars: a survival analysis approach
Venue: arXiv cs.RO 2021
Year: 2021

Lane-changing (LC) behavior describes the lateral movement of the vehicle from the current-lane to the target-lane while proceeding forward. Among the many research directions, LC duration (LCD) measures the total time it takes for a vehicle to travel from the current lane to the target lane, which is an indispensable indicator to characterize the LC behavior. Although existing research has made some achievements, less attention has been paid to the research of heavy vehicles' LCD. Therefore, this paper aims to further explore the LCD between heavy vehicles and passenger cars. LC trajectories are extracted from the newly-released HighD dataset, which contains of 16.5 hours of measurement and over 11,000 vehicles. The survival function of LCD has been estimated, and the characteristic has been analyzed. Thereafter, the Accelerated Failure Time model is introduced to explore the influencing factors. Results demonstrate that the MST value of passenger cars and heavy vehicles is about 5.51s and 6.08s. The heavy vehicles would maintain a longer time-headway and distance-headway with preceding vehicle when performing LC. Nevertheless, these two factors do not significantly affect the LCD of heavy vehicles. Finally, the results and the modeling implications have been discussed. We hope this paper could contribute to our further understanding of the LC behaviors for heavy vehicles and passenger cars.
Hierarchical automatic lane-changing motion planning: from self-optimum to local-optimum
Venue: arXiv cs.RO 2021
Year: 2021

In order to minimize the impact of lane change (LC) maneuver on surrounding traffic environment, a hierarchical automatic LC algorithm that could realize local optimum has been proposed. This algorithm consists of a tactical layer planner and an operational layer controller. The former generates a local-optimum trajectory. The comfort, efficiency, and safety of the LC vehicle and its surrounding vehicles are simultaneously satisfied in the optimization objective function. The later is designed based on vehicle kinematics model and the Model Predictive Control (MPC), which aims to minimize the tracking error and control increment. Combining macro-level and micro-level analysis, we verify the effectiveness of the proposed algorithm. Our results demonstrate that our proposed algorithm could greatly reduce the impact of LC maneuver on traffic flow. This is reflected in the decrease of total loss for nearby vehicles (such as discomfort and speed reduction), and the increase of traffic speed and throughput within the LC area. In addition, in order to guide the practical application of our algorithm, we employ the HighD dataset to validate the algorithm. This research could also be regarded as a preliminary foundational work to develop locally-optimal automatic LC algorithm. We anticipate that this research could provide valuable insights into autonomous driving technology.
COVINS: Visual-Inertial SLAM for Centralized Collaboration
Venue: arXiv cs.RO 2021
Year: 2021

Collaborative SLAM enables a group of agents to simultaneously co-localize and jointly map an environment, thus paving the way to wide-ranging applications of multi-robot perception and multi-user AR experiences by eliminating the need for external infrastructure or pre-built maps. This article presents COVINS, a novel collaborative SLAM system, that enables multi-agent, scalable SLAM in large environments and for large teams of more than 10 agents. The paradigm here is that each agent runs visual-inertial odomety independently onboard in order to ensure its autonomy, while sharing map information with the COVINS server back-end running on a powerful local PC or a remote cloud server. The server back-end establishes an accurate collaborative global estimate from the contributed data, refining the joint estimate by means of place recognition, global optimization and removal of redundant data, in order to ensure an accurate, but also efficient SLAM process. A thorough evaluation of COVINS reveals increased accuracy of the collaborative SLAM estimates, as well as efficiency in both removing redundant information and reducing the coordination overhead, and demonstrates successful operation in a large-scale mission with 12 agents jointly performing SLAM.
Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture
Venue: arXiv cs.CV 2021
Year: 2021

We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This comprehensive database allows to filter the datasets under user-defined specifications such as for example the crop-type or the age of the plant. Furthermore, the indoor dataset contains images of plants taken from a wide variety of angles, including profile shots, top-down shots, and angled perspectives. The images taken from plants in fields are all from a top-down perspective and contain usually multiple plants per image. For these images metadata is also available. In this paper we describe both datasets' characteristics with respect to plant variety, plant age, and number of images. We further introduce an open-access sample of the indoor-dataset that contains 1,000 images of each species covered in our dataset. These, in total 14,000 images, had been selected, such that they form a representative sample with respect to plant age and ndividual plants per species. This sample serves as a quick entry point for new users to the dataset, allowing them to explore the data on a small scale and find the parameters of data most useful for their application without having to deal with hundreds of thousands of individual images.