Reviewerly/exHarmony huggingface dataset:
Granular-Based Multilayer Spatiotemporal Network With Control Gates for Energy Prediction of Steel Industry

Spatiotemporal analysis has drawn a lot of attentions recently on industrial time series prediction. Most of the existing methods cannot consider the production semantics or recognize the time-order characteristic of different processes in spatial dimension. In this study, a novel multilayer spatiotemporal network based on granular modeling and long short-term memory (LSTM) network is proposed. Regarding the energy data that contain semantic characteristics, a number of information granules are first partitioned based on the process or phase semantics. For representing the spatiotemporal dependencies among the granular sequences, a spatiotemporal topology network is established, where the units are defined specifically by their spatial layers located. To reduce the network complexity, a new gating mechanism is further put forward within the units, with which the network can understand the time-order characteristics of spatial granules at each time step and adjust the impact of input data on the updating procedure accordingly. The performance of the proposed method is validated on the benchmark dataset and the practical energy data with different fluctuation characteristics, using the state-of-the-art comparative algorithms. The results indicate that the proposed method can recognize the spatiotemporal relationship effectively and outperform the comparative ones on prediction accuracy.
Semantics-aware Multi-modal Domain Translation: From LiDAR Point Clouds to Panoramic Color Images

In this work, we present a simple yet effective framework to address the domain translation problem between different sensor modalities with unique data formats. By relying only on the semantics of the scene, our modular generative framework can, for the first time, synthesize a panoramic color image from a given full 3D LiDAR point cloud.The framework starts with semantic segmentation of the point cloud, which is initially projected onto a spherical surface. The same semantic segmentation is applied to the corresponding camera image. Next, our new conditional generative model adversarially learns to translate the predicted LiDAR segment maps to the camera image counter-parts. Finally, generated image segments are processed to render the panoramic scene images. We provide a thorough quantitative evaluation on the Semantic-KITTI dataset [4] and show that our proposed framework outperforms other strong baseline models. Our source code is available.
Cross-Model Transformer Method for Medical Image Synthesis

Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (GAN) models are applied to many medical image synthesis tasks and show prior performance, since they enable to capture structural details clearly. However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. Therefore, the long-range dependencies have been destroyed in this processing. To address this issue, we introduce a double-scale deep learning method for cross-modal medical image synthesis. More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long-range dependencies to learning global feature through global discriminator based on transformer architecture. To evaluate the effectiveness of double-scale GAN, we conduct folds of experiments on the standard benchmark IXI dataset and experimental results demonstrate the effectiveness of our method.
PB-3PAKA: Password-based three-party authenticated key agreement protocol for mobile devices in post-quantum environments

Different mobile-based web applications have become popular among mobile users due to the enormous evolution of mobile communication technologies and hardware technologies. Mobile users can access various web services using mobile devices over the Internet at all times and places. A three-party authenticated key agreement (3PAKA) protocol based on public-key cryptography (PKC) is commonly used for the last three decades to yield a secure channel between two mobile users over any wireless network. A 3PAKA protocol allows the server to provide sufficient assistance so that two mobile users can establish a high-entropy common secret session key among them. However, the password-based 3PAKA (PB-3PAKA) protocol is a common approach due to its low computation cost and easy password memorization. Some recent studies showed that all the traditional PKC-based PB-3PAKA protocols could not resist quantum attacks. To provide the quantum attack resilience, we first put forward a new PB-3PAKA protocol for mobile users using ideal lattices. Our analysis showed that the PB-3PAKA protocol is safe in the post-quantum world based on the hardness assumption of the ring-learning-with-errors problem. Using the LatticeCrypto Library, we evaluated the performance of our PB-3PAKA protocol and compared it with competing protocols. The comparative study assures that our PB-3PAKA protocol is robust and efficient than the state-of-the-art protocols.
Research on Design and Application of Online English Education Platform Based on Web

Aiming at the problems of low user satisfaction and long response time in traditional online English education platforms, this paper designs a web-based online English education platform and carries out relevant application tests. With the support of the web, the hardware and software of the platform are designed. The hardware of the platform is composed of student module, teacher module, administrator module, and database module. On this basis, the K-means clustering method is used to cluster the learner data in the web, to determine their English level, and the collaborative filtering algorithm is combined to recommend relevant course materials for learners so as to complete the platform software design. The application test results show that the platform has the advantages of superior function and security, high user satisfaction, and short response time and has certain practical application value.
Learning to Learn the Future

Time series prediction has great practical value in a wide range of real-world scenarios such as stock market and retail. Existing methods typically face model aging issue caused by the concept drift: the model performance degrades along time. Undoubtedly, the model aging issue can cause serious damage in practical usage, e.g. wrong predictions in stock price may cause catastrophic losses in the financial domain. Therefore, it is essential to address the model aging issue so as to promise the predictor's performance in the future. In this paper, we propose a novel solution to address the issue. First, we uncover the theoretical connection between the complex concept drift in time series data and the gradients of deep neural networks. Based on this, we propose a novel framework called learning to learn the future. Specifically, we develop a learning method to model the concept drift during the inference stage, which can help the model generalize well in the future. Furthermore, to mitigate the impact of noises and randomness of time series data, we propose to enhance the framework by leveraging similar series in concept drift modeling. To the best of our knowledge, our approach is the first general solution to model aging issue in time series prediction. We conduct extensive experiments on three real-world datasets, which validate the effectiveness of our framework. For instance, it achieves a relative improvement of 33% in stock price prediction over the state-of-the-art methods.
Observation and Maneuver Detection for Cislunar Vehicles

Abstract International interest in the sustained development of cislunar space will generate traffic and debris in the region which requires monitoring; similar to how current space situation awareness is necessary for the traffic and debris near Earth. There are many challenges associated with developing a cislunar situation awareness program, but 2 primary issues addressed by this paper are observational strategies and maneuver detection methods. This work proposes an observational strategy that utilizes the ballistic Optimal Control Based Estimator (OCBE) to filter measurements from cislunar optical observers. To reduce numerical issues associated with filtering, new modifications to the ballistic Optimal Control Based Estimator (OCBE) are introduced that preserve the OCBE update equations in Square Root Information (SRI) space. This new derivation produces a more stable version of the ballistic OCBE which is beneficial for filtering larger data sets with non-linear motion and measurements. Applying the SRI OCBE to the estimation problem it was found that only a single L2 observer with angle and angle-rate measurements provided sufficient information for consistent estimation. Then a newly developed maneuver detection method is presented to statistically identify maneuvers. The method applies a binary hypothesis test to the optimal control policy of the ballistic OCBE to quantify mismodeling. This method was tested given a impulsive maneuver policy with a mean of 50 mm/s and standard deviation of 15 mm/s, and 194 out of 200 tests correctly identified if a maneuver occurred. The OCBE control policy also provided appropriate impulse estimates of mismodeling, which may be used to reconstruct maneuvers in future work. Together, the proposed observation and maneuver detection methodology yields reliable tracking and provides a statistical framework to detect maneuvers.
Multi-weather city: Adverse weather stacking for autonomous driving

Autonomous vehicles make use of sensors to perceive the world around them, with heavy reliance on vision-based sensors such as RGB cameras. Unfortunately, since these sensors are affected by adverse weather, perception pipelines require extensive training on visual data under harsh conditions in order to improve the robustness of downstream tasks - data that is difficult and expensive to acquire. Based on GAN and CycleGAN architectures, we propose an overall (modular) architecture for constructing datasets, which allows one to add, swap out and combine components in order to generate images with diverse weather conditions. Starting from a single dataset with ground-truth, we generate 7 versions of the same data in diverse weather, and propose an extension to augment the generated conditions, thus resulting in a total of 14 adverse weather conditions, requiring a single ground truth. We test the quality of the generated conditions both in terms of perceptual quality and suitability for training downstream tasks, using real world, out-of-distribution adverse weather extracted from various datasets. We show improvements in both object detection and instance segmentation across all conditions, in many cases exceeding 10 percentage points increase in AP, and provide the materials and instructions needed to re-construct the multi-weather dataset, based upon the original Cityscapes dataset.
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely used for vehicle perception. These techniques offer significant improvement on average perception accuracy over traditional methods, however have been shown to be susceptible to adversarial attacks, where small perturbations in the input may cause significant errors in the perception results and lead to system failure. Most prior works addressing such adversarial attacks focus only on the sensing and perception modules. In this work, we propose an end-to-end approach that addresses the impact of adversarial attacks throughout perception, planning, and control modules. In particular, we choose a target ADAS application, the automated lane centering system in OpenPilot, quantify the perception uncertainty under adversarial attacks, and design a robust planning and control module accordingly based on the uncertainty analysis. We evaluate our proposed approach using both public dataset and production-grade autonomous driving simulator. The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attack and can achieve 55% ~ 90% improvement over the original OpenPilot.
Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization

Efficient trajectory optimization is essential for avoiding collisions in unstructured environments, but it remains challenging to have both speed and quality in the solutions. One reason is that second-order optimality requires calculating Hessian matrices that can grow with O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) with the number of waypoints. Decreasing the waypoints can quadratically decrease computation time. Unfortunately, fewer waypoints result in lower quality trajectories that may not avoid the collision. To have both, dense waypoints and reduced computation time, we took inspiration from recent studies on consensus optimization and propose a distributed formulation of collocated trajectory optimization. It breaks a long trajectory into several segments, where each segment becomes a subproblem of a few waypoints. These subproblems are solved classically, but in parallel, and the solutions are fused into a single trajectory with a consensus constraint that enforces continuity of the segments through a consensus update. With this scheme, the quadratic complexity is distributed to each segment and enables solving for higher-quality trajectories with denser waypoints. Furthermore, the proposed formulation is amenable to using any existing trajectory optimizer for solving the subproblems. We compare the performance of our implementation of trajectory splitting against leading motion planning algorithms and demonstrate the improved computational efficiency of our method.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling

Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformation Ensembling (TTE) can work as a reliable defense against such attacks. While transforming the input data, both at train and test times, is known to enhance model performance, its effects on adversarial robustness have not been studied. Here, we present a comprehensive empirical study of the impact of TTE, in the form of widely-used image transforms, on adversarial robustness. We show that TTE consistently improves model robustness against a variety of powerful attacks without any need for re-training, and that this improvement comes at virtually no trade-off with accuracy on clean samples. Finally, we show that the benefits of TTE transfer even to the certified robustness domain, in which TTE provides sizable and consistent improvements.
Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings

As solar photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. By evaluating the energy balances for different annual consumptions, it is found that the canopy and façade installations offer better self-consumption of the PV produced energy, reflected in a 9% higher self-consumption degree using modules on façades and a 5% higher self-consumption degree using canopies. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing the time of return on investment by more than 2 years. The analysis of different alternatives for an industrial PV allowed us to identify several benefits for these orientations which are confirmed after a full year of operation, such as an increase of up to 59% in annual energy production over the optimal-producing orientation.
PathReasoner: Explainable reasoning paths for commonsense question answering

Commonsense question answering has attracted increasing attention as a challenging task requiring the human reasoning process of answering questions with the help of abundant commonsense knowledge. Existing methods mostly resort to large pre-trained language models and face many difficulties when dealing with the out-of-scope reasoning target, and are unaware of explainable structured information. In this paper, we explore explicitly incorporate external reasoning paths with structured information to explain and facilitate commonsense QA. For this purpose, we propose a PathReasoner to both extract and learn from such structured information. The proposed PathReasoner consists of two main components, a path finder and a hierarchical path learner. To answer a commonsense question, the path finder first retrieves explainable reasoning paths from a large-scale knowledge graph, then the path learner encodes the paths with hierarchical encoders and uses the path features to predict the answers. The experiments on two typical commonsense QA datasets demonstrate the effectiveness of the PathReasoner. The case study gives insightful findings that the reasoning paths provide explainable information for the question answering through the PathReasoner.
FPPW: A Fair and Privacy Preserving Watchtower for Bitcoin

In this paper, we introduce FPPW, a new payment channel with watchtower scheme for Bitcoin. This new scheme provides fairness w.r.t. all channel participants including both channel parties and the watchtower. It means that the funds of any honest channel participant are safe even assuming that other two channel participants are corrupted and/or collude with each other. Furthermore, the watchtower in FPPW learns no information about the off-chain transactions and hence FPPW provides privacy against the watchtower. As a byproduct, we also define the coverage of a watchtower scheme, that is the total capacity of channels that a watchtower can cover on a scale of 0 to 1, and show that FPPW's coverage is higher than those of PISA and Cerberus. The scheme can be implemented without any update in Bitcoin script.
Formal Verification of the Ethereum 2.0 Beacon Chain

Abstract We report our experience in the formal verification of the reference implementation of the Beacon Chain. The Beacon Chain is the backbone component of the new Proof-of-Stake Ethereum 2.0 network: it is in charge of tracking information about the validators , their stakes , their attestations (votes) and if some validators are found to be dishonest, to slash them (they lose some of their stakes). The Beacon Chain is mission-critical and any bug in it could compromise the whole network. The Beacon Chain reference implementation developed by the Ethereum Foundation is written in Python, and provides a detailed operational description of the state machine each Beacon Chain’s network participant (node) must implement. We have formally specified and verified the absence of runtime errors in (a large and critical part of) the Beacon Chain reference implementation using the verification-friendly language Dafny. During the course of this work, we have uncovered several issues, proposed verified fixes. We have also synthesised functional correctness specifications that enable us to provide guarantees beyond runtime errors. Our software artefact with the code and proofs in Dafny is available at https://github.com/ConsenSys/eth2.0-dafny .
Merged-Sampling Mask R-CNN With Random Proposal Expansion for Particle Measurement of SEM Images of Molecular Sieve Catalysts

Scanning electron microscope (SEM) images of molecular sieve catalysts contain information about the shapes and sizes of these particles. SEM image measurement is a crucial step in the evaluation of the catalytic performances. Instance segmentation methods, such as Mask R-CNN are effective in automatically analyzing SEM images. However, their performance is limited in small datasets. Although overfitting caused by small data sets can be addressed through data augmentation at the image level, the application of Mask R-CNN still needs further improvement for generalization enhancement. In this paper, two techniques for Mask R-CNN are proposed to alleviate overfitting during the training on small datasets. First, merged sampling on the region proposal network simultaneously samples proposals with high and low scores in order that the head networks can be exposed to more diverse proposal samples. Second, random proposal expansion enhances the diversity of samples provided to the mask-branch of Mask R-CNN. These two techniques can be viewed as data augmentation at the instance level. Experiments on a small SEM image dataset showed that merged-sampling Mask R-CNN with random proposal expansion improved about the average precision (AP) by 5%, compared with the original Mask R-CNN. Overfitting on the small dataset is effectively controlled using the proposed methods. Hence, the results of the particle measurements of the industrial SEM images improved.
Efficient Explicit Constructions of Multipartite Secret Sharing Schemes

Multipartite secret sharing schemes are those having a multipartite access structure, in which the set of participants is divided into several parts and all participants in the same part play an equivalent role. Secret sharing schemes for multipartite access structures have received considerable attention due to the fact that multipartite secret sharing can be seen as a natural and useful generalization of threshold secret sharing. This work deals with efficient and explicit constructions of ideal multipartite secret sharing schemes. Most ideal multipartite secret sharing schemes in the literature can be classified as either hierarchical or compartmented. The main results are the constructions for ideal hierarchical access structures, a family that contains every ideal hierarchical access structure as a particular case such as the disjunctive hierarchical threshold access structure and the conjunctive hierarchical threshold access structure, the constructions for three families of compartmented access structures, and the constructions for two families compartmented access structures with compartments. We present an efficient method to construct ideal linear schemes realizing these access structures by linear algebraic techniques on the basis of the relationship between multipartite secret sharing schemes, polymatroids and matroids.