Reviewerly/exHarmony huggingface dataset:
Asynchronous Real-time Decentralized Multi-Robot Trajectory Planning

We present a novel overconstraining and constraint-discarding method for asynchronous, real-time, decentralized, multi-robot trajectory planning that ensures collision avoidance. Our approach utilizes communication between robots. The communication medium is best-effort: messages may be dropped, re-ordered or delayed. Robots conservatively constrain themselves against others assuming they may be working with outdated information, and discard constraints when they receive update messages from others. Our method can augment existing synchronized decentralized receding horizon planning algorithms that utilize separating hyperplanes for collision avoidance thereby making them applicable to asynchronous setups. As an example, we extend an existing model predictive control based, synchronized, decentralized multi-robot planner using our method. We show our method's effectiveness under asynchronous planning and imperfect communication by comparing our extension to the base version. Our extension does not result in any collisions or synchronization-induced deadlocks to which the base version is prone.
Incorporating thrust models for quadcopter wind estimation

We consider a nonlinear estimation problem where a quadcopter moves in a wind field. The accuracy of the thrust model used in an estimator for wind estimation is highly related to stability and accuracy of the estimator. In this paper, we establish invariance properties of various thrust models and incorporate them for invariant extended Kalman filters (IEKFs) designs. Through Monte-Carlo (MC) simulations, we show that the IEKF with the accurate thrust model improves the states and wind estimation performance (particularly in the vertical direction) for various wind fields.
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow

Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perform such an analysis, current attacks on optical flow methods focus on real-world attacking scenarios rather than a worst case robustness assessment. Hence, in this work, we propose a novel adversarial attack—the Perturbation-Constrained Flow Attack (PCFA)—that emphasizes destructivity over applicability as a real-world attack. PCFA is a global attack that optimizes adversarial perturbations to shift the predicted flow towards a specified target flow, while keeping the $$L_2$$ norm of the perturbation below a chosen bound. Our experiments demonstrate PCFA's applicability in white- and black-box settings, and show it finds stronger adversarial samples than previous attacks. Based on these strong samples, we provide the first joint ranking of optical flow methods considering both prediction quality and adversarial robustness, which reveals state-of-the-art methods to be particularly vulnerable. Code is available at https://github.com/cv-stuttgart/PCFA .
Theory of (1+1) ES on SPHERE Revisited

The theory of evolutionary algorithms on continuous space gravitates around the evolution strategy with one individual, adaptive mutation and elitist selection, optimizing the symmetric, quadratic SPHERE function. The classic, normal mutation theory consists of three main building-blocks: i) a two-term formula for the local (constant mutation) expected progress; ii) an exponential formula for the global behavior of the (adaptive mutation) algorithm; iii) linear convergence time with respect to both space dimension n and (logarithm of) initial distance to optimum. We show that the three main results still hold if we replace the normal mutation with uniform inside the sphere and also with the sum of two uniforms. That makes the case for an important conclusion: the linear convergence time of the algorithm is not a consequence of the normal mutation, but of the elitist selection and 1/5 success rule. A simplified version of the 1/5-rule allows also for an intuitive representation of the algorithm, as a sequence of constant-mutation, independent and identical (expected) length cycles.
Virtual IMU Data Augmentation by Spring-Joint Model for Motion Exercises Recognition without Using Real Data

A conventional motion exercises recognition system only tracks designated motion types, and it enables users cannot use a customized system according to personal needs. The virtual IMU data provides a new opportunity to reduce the cost of training datasets and flexibly design the activity recognition system using online resources. To better design a user-customized motion exercises recognition system using virtual IMU data, this paper proposes a virtual IMU sensor module with a spring-joint model to augment the virtual acceleration signal from the limited online 2D video. The original virtual acceleration signal is extended with data from different acceleration distributions generated by the spring-joint model and used to train a motion exercises recognition system. The proposed method can design a classifier for three motions with limited video resources, showing an average accuracy of 85.5 on the real motion data of seven individuals.
Multi-preference Book Recommendation Method Based on Graph Convolution Neural Network

In the book recommendation system, the relationship between users and books can be regarded as a bipartite graph. The user's interest preferences are mined from the graph through Collaborative Filtering recommendation method, and then use the graph convolution neural network to effectively aggregate the characteristics of users and books, so as to form the book recommendation content that user interest. However, the mining of user interest in the existing book recommendation system is always based on a single user preference, ignoring the diversity of user preferences. We propose a multi-preference book recommendation method based on graph convolution neural network to observe the potential reading interest of users when interacting with books. By capturing these reading interests, we can get more information about users' preferences, so as to recommend books more in line with their preferences. We extract the recommendation dataset from the real scenario of Bohai University Library between May 2014 and May 2021, and evaluate our method on it. The experimental results show that our method effectively improves the performance of book recommendation.
The Optimization and Application of 3-Bit Hermitian Gates and Multiple Control Toffoli Gates

The well-known 3-bit Hermitian gate (a Toffoli gate) has been implemented using Clifford + T circuits. Compared with the Peres gate, its implementation circuit requires more controlled-NOT (CNOT) gates. However, the Peres gate is not Hermitian. This paper reports four 3-bit Hermitian gates named LI gates. Whose realized circuits have the same T-count, T-depth, and CNOT-count as the Peres gate. Furthermore, two decomposition methods of a multiple control Toffoli (MCT) gate are proposed for different primary optimization goals. Then, we design the equality, less-than, and full comparators with the minimum circuit width using proposed Hermitian gates and optimized MCT gates. A fault-tolerant circuit is required for robust quantum computing. Clifford+T circuits are accepted solutions for fault-tolerant implementation. Considering T-count, T-depth, CNOT-count, and circuit width as the primary optimization goals, we design the optimized Clifford + T circuits of three comparators using LI gates and optimized MCT gates. Comparison and analysis show that the proposed comparators have better overall performances for T-count, T-depth, CNOT-count, and circuit width than the best-known comparators without quantum measurements.
IoT-Based Aggregate Smart Grid Energy Data Extraction using Image Recognition and Partial Homomorphic Encryption

The high expense of upgrading traditional analog and digital smart grid meters with network sensor devices is a major implementation cost for collecting energy consumption statistics. A cost-effective solution for the information extraction from the meter images is to use a systematic IoT-Based periodic digital imaging methodology instead of manual reading techniques. The computations on and secrecy of the extracted energy information are enabled by the additive homomorphic nature of encryption in Paillier's cryptosystem. The encrypted data is then, retrieved via the IoT device with the image sensor installed at the industrial, commercial and residential subscriber metres. This power consumption ciphertext is subjected to decentralized progressive aggregation as it is en route online from the end user grid meter at level 0 to the level <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> semi-trusted final collector for decryption through numerous minimum height spanning trees (MHST) constructed at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n-1$</tex> geographic levels. The proposed scheme's robustness to replay, false data injection (FDI) attacks and data integrity are ensured by pairing based Boneh-Lynn-Shacham (BLS) short signature, time stamping and SHA-256 function. This practical technique, therefore, facilitates secure and distributed data aggregation from various sorts of users in a hierarchical fashion in an efficient manner.
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning

The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expensive. Active learning is a research area that addresses the issues of expensive labeling by selecting the most important samples for labeling. Diversity-based sampling algorithms are known as integral components of representation-based approaches for active learning. In this paper, we introduce a new diversity-based initial dataset selection algorithm to select the most informative set of samples for initial labeling in the active learning setting. Self-supervised representation learning is used to consider the diversity of samples in the initial dataset selection algorithm. Also, we propose a novel active learning query strategy, which uses diversity-based sampling on consistency-based embeddings. By considering the consistency information with the diversity in the consistency-based embedding scheme, the proposed method could select more informative samples for labeling in the semi-supervised learning setting. Comparative experiments show that the proposed method achieves compelling results on CIFAR-10 and Caltech-101 datasets compared with previous active learning approaches by utilizing the diversity of unlabeled data.
Physically-guided Disentangled Implicit Rendering for 3D Face Modeling

This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for highfidelity 3D face modeling. The motivation comes from two observations: Widely-used graphics renderers yield excessive approximations against photo-realistic imaging, while neural rendering methods produce superior appearances but are highly entangled to perceive 3D-aware operations. Hence, we learn to disentangle the implicit rendering via explicit physical guidance, while guaranteeing the properties of: (1) 3D-aware comprehension and (2) high-reality image formation. For the former one, PhyDIR explicitly adopts 3D shading and rasterizing modules to control the renderer, which disentangles the light, facial shape, and viewpoint from neural reasoning. Specifically, PhyDIR proposes a novel multi-image shading strategy to compensate for the monocular limitation, so that the lighting variations are accessible to the neural renderer. For the latter, PhyDIR learns the face-collection implicit texture to avoid ill-posed intrinsic factorization, then leverages a series of consistency losses to constrain the rendering robustness. With the disentangled method, we make 3D face modeling benefit from both kinds of rendering strategies. Extensive experiments on benchmarks show that PhyDIR obtains superior performance than state-of-the-art explicit/implicit methods on geometry/texture modeling.
Boths: Super Lightweight Network-Enabled Underwater Image Enhancement

Since light is scattered and absorbed by water, underwater images have inherent degradation (e.g., hazing, color shift), consequently impeding the development of remotely operated vehicles (ROVs). Toward this end, we propose a novel method, referred to as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> est <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${o}\text{f}$ </tex-math></inline-formula> Bo th World <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> (Boths). With parameters of only 0.0064 M, Boths can be considered a super lightweight neural network for underwater image enhancement. On the whole, it has three levels: structure and detail features; pixel and channel dimensions; high- and low-frequency information. Each of these three levels represents “Best of Both Worlds.” Initially, by interacting with structure and detail features, Boths can focus on these two aspects at the same time. Further, our network can simultaneously consider channel and pixel dimensions through 3-D attention learning, which is more similar to human visual perception. Lastly, the proposed model can focus on high- and low-frequency information, through a novel loss function based on the wavelet transforms. Upon subsequent analysis and evaluation, Boths has shown superior performance compared with state-of-the-art (SOTA) methods. Our models and datasets are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/perseveranceLX/Boths</uri> .
Closing the Planning–Learning Loop With Application to Autonomous Driving

Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregulated urban traffic of cars, motorcycles, buses, etc. The robot vehicle has to plan in both short and long terms, in order to interact with many traffic participants of uncertain intentions and drive effectively. Planning explicitly over a long time horizon, however, incurs prohibitive computational cost and is impractical under real-time constraints. To achieve real-time performance for large-scale planning, this work introduces a new algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning from Tree Search for Driving</i> (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation. Specifically, LeTS-Drive learns a policy and its value function from data provided by an online planner, which searches a sparsely sampled belief tree; the online planner in turn uses the learned policy and value functions as heuristics to scale up its run-time performance for real-time robot control. These two steps are repeated to form a closed loop so that the planner and the learner inform each other and improve in synchrony. The algorithm learns on its own in a self-supervised manner, without human effort on explicit data labeling. Experimental results demonstrate that LeTS-Drive outperforms either planning or learning alone, as well as open-loop integration of planning and learning.
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding

Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data can be represented as two interchangeable representations: 3D point clouds and range images, most previous work focus on compressing the generic 3D point clouds. In this work, we show that directly compressing the range images can leverage the lidar scanning pattern, compared to compressing the unprojected point clouds. We propose a novel datadriven range image compression algorithm, named RIDDLE (Range Image Deep DeLta Encoding). At its core is a deep model that predicts the next pixel value in a raster scanning order, based on contextual laser shots from both the current and past scans (represented as a 4D point cloud of spherical coordinates and time). The deltas between predictions and original values can then be compressed by entropy encoding. Evaluated on the Waymo Open Dataset and KITTI, our method demonstrates significant improvement in the compression rate (under the same distortion) compared to widely used point cloud and range image compression algorithms as well as recent deep methods.
Improving Bitcoin’s Post-Quantum Transaction Efficiency With a Novel Lattice-Based Aggregate Signature Scheme Based on CRYSTALS-Dilithium and a STARK Protocol

This paper proposes a novel lattice-based aggregate signature (LAS) scheme that bring post-quantum security to the Bitcoin system without sacrificing its transaction efficiency. Bitcoin currently employs Elliptic Curve Digital Signature Algorithm (ECDSA), which is insecure against the emerging quantum technology, so post-quantum signature schemes like the proposed LAS will become necessary in the near future. However, most of the post-quantum signatures schemes have large signature sizes which decrease Bitcoin’s efficiency while our proposed scheme does not have this negative side effect. Our LAS scheme is based on CRYSTALS-Dilithium and a zero-knowledge Scalable Transparent Arguments of Knowledge (STARK) protocol. CRYSTALS-Dilithium is the most prominent algorithm chosen by the National Institute of Standards and Technology (NIST), yet it still has an adverse limitation: it would cause Bitcoin’s transaction efficiency to fall by 17 times due to its relatively large signature size. On the other hand, the proposed LAS scheme takes full advantage of signature aggregation using the STARK protocol and Dilithium’s easy and fast implementation, thus generating signatures with post-quantum security and small signature sizes which are critical to transaction efficiency. Our proofs convey the correctness, compactness, and post-quantum security of our construction in the quantum random oracle model, and our implementation conveyed that the proposed scheme would only decrease Bitcoin’s transaction efficiency by 3 times, a significant improvement from using Dilithium and other lattice-based aggregate signature schemes. Our proposed scheme has many advantages over the existing schemes and may become very valuable to Bitcoin.
StyleFace: Towards Identity-Disentangled Face Generation on Megapixels

Identity swapping and de-identification are two essential applications of identity-disentangled face image generation. Although sharing a similar problem definition, the two tasks have been long studied separately, and identity-disentangled face generation on megapixels is still under exploration. In this work, we propose StyleFace, a unified framework for $$1024^2$$ resolution high-fidelity identity swapping and de-identification. To encode real identity while supporting virtual identity generation, we represent identity as a latent variable and further utilize contrastive learning for latent space regularization. Besides, we utilize StyleGAN2 to improve the generation quality on megapixels and devise an Adaptive Attribute Extractor, which adaptively preserves the identity-irrelevant attributes in a simple yet effective way. Extensive experiments demonstrate the state-of-the-art performance of StyleFace in high-resolution identity swapping and de-identification.
Heterogeneous Training Intensity for Federated Learning: A Deep Reinforcement Learning Approach

Federated learning (FL) has recently received considerable attention in Internet of Things, due to its capability of letting multiple clients collaboratively train machine learning models, without sharing their private information. However, in synchronous FL, the client with weak computing or communication capability may significantly drag down the model training process, which leads to very high waiting latency for other clients. Intuitively, to alleviate this straggler problem, the clients with lower (higher) training capabilities should be assigned with less (more) training intensity. Inspired by this observation, this paper formulates a novel Heterogeneous Training Intensity assignment problem for FL, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HTI_FL</i> , aiming at reducing the largest training latency gap among clients. To address HTI_FL problem, we first propose an optimal deterministic algorithm, which however is only suitable for a static FL context with stable network conditions and clients' computing capabilities. To consider a practical dynamic context, we propose a Deep Reinforcement Learning Approach to learning the network conditions and clients' capabilities, and furthermore adaptively assign training intensities to clients. Finally, simulation results demonstrate the effectiveness of the proposed scheme in reducing the waiting time and accelerating the convergence of FL.
The Design and Implementation of AR Glass for Coal Mine Application

Augmented Reality (AR) has been applied in many applications. The key for the AR implementation is the devices or AR glasses. Currently, many companies have published their AR glasses, which could not be directly used in coal mine underground environments. Therefore, in this paper, we propose an improved design of AR glass especially for the coal mine situation. The novel device improves the light projection according to the low illumination in coal mine. Meanwhile, the overall electricity system is enhanced to prevent the fire or explosion. The devices has been applied in the coal mine and the effects are analyzed. Also, the feedback from the users to the new AR glass on the decision support in coal mine is collected for the future improvements.
To type or not to type?

JavaScript (JS) is one of the most popular programming languages, and widely used for web apps, mobile apps, desktop clients, and even backend development. Due to its dynamic and flexible nature, however, JS applications often have a reputation for poor software quality. While the type-safe superset TypeScript (TS) offers features to address these prejudices, there is currently insufficient empirical evidence to broadly support the claim that TS applications exhibit better software quality than JS applications.
Comparative Study of Twoing and Entropy Criterion for Decision Tree Classification of Dispersed Data

In decision tree building, the choice of the splitting criteria highly affects the quality of model that is developed. In this paper, decision tree models are developed on dispersed data using entropy measure and twoing criterion as the splitting criteria. Dispersed data in this sense has multiple independent local tables on which decision tree models are built. Prediction vectors are generated based on the local models and a final prediction is made from aggregation using majority voting. In effort to improve model quality ensemble method technique (bagging) is applied to build multiple models for each local table. The main purpose of this paper is to make a comparative study on the classification quality of decision tree models built on dispersed data using entropy and twoing splitting measure. The main observation is that when knowledge is highly dispersed in a lot of local tables, using twoing criterion in building decision tree models is better than using entropy measure.