Reviewerly/exHarmony huggingface dataset:
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement

In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order to remedy the information loss from box annotations to centers, our method, namely Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak labels into fully-annotated virtual scenes as stronger supervision, and in turn utilizes the perfect virtual labels to complement and refine the real labels. Specifically, we first assemble 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we go back to reality by applying a virtual-to-real domain adaptation method, which refine the weak labels and additionally supervise the training of detector with the virtual scenes. Furthermore, we propose a more challenging benckmark for indoor 3D object detection with more diversity in object sizes for better evaluation. With less than 5% of the labeling labor, we achieve comparable detection performance with some popular fully-supervised approaches on the widely used ScanNet dataset. Code is available at: https://github.com/wyf-ACCEPT/BackToReality.
Depth measurement based on a convolutional neural network and structured light

Abstract Depth measurement methods based on structured light are popular due to their advantages of low cost, good portability and ease of implementation. Depth information for the object is obtained based on the geometric relationship of the imaging system and triangulation theory, which usually requires local stereo matching operations. However, this is computationally intensive, resulting in reduced depth accuracy and worse depth maps. To address these problems, this paper proposes a novel depth measurement method based on a convolutional neural network (DMCNN), which is cast as a pixel-wise classification–regression task without matching. Firstly, the DMCNN network is designed as an encoder–decoder structure. A feature pyramid is adopted in the encoder to extract multi-scale fusion features, and parallel classification and regression branches are constructed at the end of the decoder to achieve depth prediction from coarse to fine. Secondly, we use a four-step phase shift algorithm to generate ground truth depth maps and build a dataset containing a large number of speckle distortion images and their corresponding depth maps to train our network. The algorithm runs on an RTX 2080Ti graphics processing unit (GPU) using 20 000 training images. Experimental results show that our method can achieve higher accuracy than alternative depth measurement methods.
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today's methods cannot achieve without sacrificing performance on other safety axes. For instance, adversarial training improves adversarial robustness but sharply degrades other classifier performance metrics. Similarly, strong data augmentation and regularization techniques often improve OOD robustness but harm anomaly detection, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and roundly improves safety measures.
Chaotic Map-Based Authentication Scheme Using Physical Unclonable Function for Internet of Autonomous Vehicle

Autonomous Vehicles (AVs) are a highly discussed topic owing to their great performance and convenience. However, some requirements limit their wider deployment. Specifically, AV should be controlled by remote users during emergencies. It may lead to AV’s system facing the risk of being intruded on by a malicious party, resulting in unreasonable decisions. We, therefore, design the Internet of autonomous vehicle (IoAV) model to mitigate the problems arising from these limitations. To promote a secure remote control of the AV, a reliable authentication scheme, which can be used in IoAV, must be performed. Our proposed chaotic map-based authenticated key agreement (CMAKA) method provides secure remote control features for AVs. In this method, users, data centers, and AV establish a secure communication channel through the negotiation of three independent session keys. Furthermore, a physical unclonable function (PUF) is employed to produce a trusted private key during the authentication. The security of our scheme is evaluated using game hopping through the widespread Real-or-Random (ROR) model. Compared with other existing three-factor authentication schemes, the performance of our protocol is higher in both security requirements and total cost.
Graph Computing Systems and Partitioning Techniques: A Survey

Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains.Graphs with many vertices and edges have become quite prevalent in recent years.Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains.However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs.Hence, industry and academia have recently proposed graph partitioning and computing systems to efficiently process and analyze large-scale graphs.The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity.This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems.The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented.Finally, we discuss future challenges and research directions in graph partitioning and computing systems.
Towards Better Understanding Attribution Methods

Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose three novel evaluation schemes to more reliably measure the faithfulness of those methods, to make comparisons between them more fair, and to make visual inspection more systematic. To address faithfulness, we propose a novel evaluation setting (DiFull) in which we carefully control which parts of the input can influence the output in order to distinguish possible from impossible attributions. To address fairness, we note that different methods are applied at different layers, which skews any comparison, and so evaluate all methods on the same layers (ML-Att) and discuss how this impacts their performance on quantitative metrics. For more systematic visualizations, we propose a scheme (AggAtt) to qualitatively evaluate the methods on complete datasets. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods. Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.
CoopEdge+: Enabling Decentralized, Secure and Cooperative Multi-Access Edge Computing Based on Blockchain

Multi-access Edge Computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes CoopEdge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On CoopEdge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, CoopEdge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement CoopEdge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of CoopEdge+ and demonstrate its performance.
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers

Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability tools have become available for NLP models, internal mechanisms of vision and multimodal transformers remain largely opaque. With the success of these transformers, it is increasingly critical to understand their inner workings, as unraveling these black-boxes will lead to more capable and trustworthy models. To contribute to this quest, we propose VL-InterpreT, which provides novel interactive visualizations for interpreting the attentions and hidden representations in multimodal transformers. VL-InterpreT is a task agnostic and integrated tool that (1) tracks a variety of statistics in attention heads throughout all layers for both vision and language components, (2) visualizes cross-modal and intra-modal attentions through easily readable heatmaps, and (3) plots the hidden representations of vision and language tokens as they pass through the transformer layers. In this paper, we demonstrate the functionalities of VL-InterpreT through the analysis of KD-VLP, an end-to-end pretraining vision-language multimodal transformer-based model, in the tasks of Visual Commonsense Reasoning (VCR) and WebQA, two visual question answering benchmarks. Furthermore, we also present a few interesting findings about multimodal transformer behaviors that were learned through our tool.
Attention Diversification for Domain Generalization

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of "simulate, divide and assemble": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization .
Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

Automated parking is a typical function in a self-driving car. The trajectory planning module directly reflects the intelligence level of an automated parking system. Although many competitions have been launched for autonomous driving, most of them focused on on-road driving scenarios. However, driving on a structured road greatly differs from parking in an unstructured environment. In addition, previous competitions typically competed on the overall driving performance instead of the trajectory planning performance. A trajectory planning competition of automated parking (TPCAP) has been recently organized. This event competed on parking-oriented planners without involving other modules, such as localization, perception, or tracking control. This study reports the TPCAP benchmarks, achievements, experiences, and future perspectives.
Multi-input Attribute Based Encryption and Predicate Encryption

Motivated by several new and natural applications, we initiate the study of multi-input predicate encryption ( $$\textsf{miPE}$$ ) and further develop multi-input attribute based encryption ( $$\textsf{miABE}$$ ). Our contributions are: Our constructions of multi-input $${\textsf{ABE}}$$ provide the first improvement to the compression factor of non-trivially exponentially efficient Witness Encryption defined by Brakerski et al. (SCN 2018) without relying on compact functional encryption or indistinguishability obfuscation. We believe that the unexpected connection between succinct single-input ciphertext-policy $${\textsf{ABE}}$$ and multi-input key-policy $${\textsf{ABE}}$$ may lead to a new pathway for witness encryption. We remark that our constructions provide the first candidates for a nontrivial class of $${\textsf{miFE}}$$ without needing $$\textsf{LPN}$$ or low depth $$\textsf{PRG}$$ .
Efficient Task Allocation for Computation Offloading in Vehicular Edge Computing

In vehicular edge computing, the edge servers may be overloaded once too many vehicles request the task offloading service, which will cause task offloading failure or the high service delay. To provide high-quality VEC service, the processing capabilities of the vehicles, the edge servers, and the cloud should be utilized simultaneously in task offloading. In this article, we focus on task offloading allocation for the requesting vehicle and the pricing schemes for the edge server and the cloud. From a market perspective, we model the competition and cooperation among the requesting vehicle, the edge server, and the cloud as a Stackelberg game. Then, based on the backward induction method, we transform the game problem into a convex optimization problem and theoretically prove that the game has a unique Nash equilibrium, thereby the optimal task allocation for the requesting vehicle can be obtained. Meanwhile, a genetic algorithm-based searching algorithm is proposed to find the optimal pricing schemes for the edge server and the cloud, and the proposed algorithm has a rapid convergence due to the convex feature of the objective problem. Simulation results demonstrate that the proposed task allocation strategy has better performance than other solutions in terms of task offloading delay and cost, thus, can make the existing resources fully used to undertake more offloading tasks.
Blockchain Technology for Embracing Healthcare 4.0

Nowadays, health data are fragmented and scattered across various systems and technologies. Complex support infrastructures, data system silos, and administrative bureaucracy have led to inefficiencies and inefficacies. This article aims to propose a blockchain platform to share big health data in real time between the authorized actors while ensuring a high level of health information protection. The proposed platform can offer a highly innovative approach to administering benefits and keeping all parties in sync. Implementing a blockchain for managing health data is a valuable support for diagnosing and monitoring the progress of the therapies of individual patients under treatment. In addition, it helps to reduce the time required for the exchange of information and keep the healthcare operations under control. Healthcare organizations and ecosystems will benefit from having a complete picture of a patient's health condition and reaching a global audience through the blockchain platform.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-boosting Attention Mechanism

The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale labeled dataset, a similar method might become less effective when only a few annotations are available. In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data. To tackle this issue, this paper proposes the self-boosting attention mechanism, a novel method for regularizing the network to focus on the key regions shared across samples and classes. Specifically, the proposed method first generates an attention map for each training image, highlighting the discriminative part for identifying the ground-truth object category. Then the generated attention maps are used as pseudo-annotations. The network is enforced to fit them as an auxiliary task. We call this approach the self-boosting attention mechanism (SAM). We also develop a variant by using SAM to create multiple attention maps to pool convolutional maps in a style of bilinear pooling, dubbed SAM-Bilinear. Through extensive experimental studies, we show that both methods can significantly improve fine-grained visual recognition performance on low data regimes and can be incorporated into existing network architectures. The source code is publicly available at: https://github.com/GANPerf/SAM .
A Hybrid Quantum-Classical Algorithm for Robust Fitting

Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source code: https://github.com/dadung/HQC-robust-fitting.
Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training

Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of con-volutional neural networks (CNNs) in the realm of computer vision (CV). With the general-purpose Transformer architecture replacing the hard-coded inductive biases of convolution, ViTs have surpassed CNNs, especially in data-sufficient circumstances. However, ViTs are prone to over-fit on small datasets and thus rely on large-scale pre-training, which expends enormous time. In this paper, we strive to liberate ViTs from pre-training by introducing CNNs' in- ductive biases back to ViTs while preserving their network architectures for higher upper bound and setting up more suitable optimization objectives. To begin with, an agent CNN is designed based on the given ViT with inductive bi-ases. Then a bootstrapping training algorithm is proposed to jointly optimize the agent and ViT with weight sharing, during which the ViT learns inductive biases from the intermediate features of the agent. Extensive experiments on CIFAR-10/100 and ImageNet-1k with limited training data have shown encouraging results that the inductive biases help ViTs converge significantly faster and outperform conventional CNNs with even fewer parameters. Our code is publicly available at https://github.com/zhfeing/Bootstrapping-ViTs-pytorch.
Application of AR/VR Technology in Industry 4.0.

This article explores how advanced AR/VR technologies can be used in Industry 4.0. to integrate smart glasses and mobile device users into the augmented reality industry to accelerate work and data transfers in manufacturing, warehousing, and transportation companies in the United States. The aim of the article was to illustrate the potential of AR/VR technologies and modern IT systems that are increasingly being applied in the global market in the different sectors of the TSL sector. The introduction of such solutions raises the question: Will AR/VR technologies and IT systems introduced in companies be useful for every company in the US? Studies show a differentiated approach by US entrepreneurs to the opportunities.
A Receiver-Driven Transport Protocol With High Link Utilization Using Anti-ECN Marking in Data Center Networks

Existing reactive or proactive congestion control protocols are hard to simultaneously achieve ultra-low latency and high link utilization across all workloads ranging from delay-sensitive flows to bandwidth-hungry ones in datacenter networks. We present an Anti-ECN (Explicit Congestion Notification) Marking Receiver-driven Transport protocol called AMRT, which achieves both near-zero queueing delay and full link utilization by reasonably increasing sending rate in the case of under-utilization. Specifically, switches mark the ECN bit of data packets once detecting spare bandwidth. When receiving the anti-ECN marked packet, the receiver generates the corresponding marked grant to trigger more data packets. The testbed and simulation experiments show that AMRT effectively reduces the average flow completion time (AFCT) by up to 42% and improves the link utilization by up to 38% over the state-of-the-art receiver-driven transmission schemes.