Reviewerly/exHarmony huggingface dataset:
Motion Planning of an Inchworm Robot Based on Improved Adaptive PSO

Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering both kinematic and dynamic constraints. Firstly, we offer a mathematical description of the energy consumption and space curve similarity operator. Secondly, we describe the mathematical models of the robot trajectory and path that were established in terms of their dynamics and kinematics. Then, we propose a motion planning method based on improved adaptive particle swarm optimization (PSO) to accelerate the convergence speed of the algorithm and ensure the accuracy of the model calculation. Finally, we outline the simulation test carried out to measure the inchworm-like robot’s creeping gait. The results show that the motion path obtained by using the planning method proposed in this paper is the one with the least energy consumption by the robot among all the comparison paths. Moreover, compared with other algorithms, it was found that the result obtained by using the algorithm proposed in this paper is the one with the shortest solution time and the lowest energy consumption under the same iteration times. The calculation results verify the feasibility and effectiveness of the planning method.
Business Document Information Extraction: Towards Practical Benchmarks

Information extraction from semi-structured documents is crucial for frictionless business-to-business (B2B) communication. While machine learning problems related to Document Information Extraction (IE) have been studied for decades, many common problem definitions and benchmarks do not reflect domain-specific aspects and practical needs for automating B2B document communication. We review the landscape of Document IE problems, datasets and benchmarks. We highlight the practical aspects missing in the common definitions and define the Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) problems. There is a lack of relevant datasets and benchmarks for Document IE on semi-structured business documents as their content is typically legally protected or sensitive. We discuss potential sources of available documents including synthetic data.
Synthetic generation of plausible solar years for long-term forecasting of solar radiation

In this work, we test and improve an algorithm proposed in previous studies to generate synthetic series of plausible solar years (PSY). The method provides 100 synthetic years of coupled global horizontal irradiance (GHI) and direct normal solar irradiance (DNI) in 1-min resolution. The algorithm uses 10–20 years of hourly coupled GHI + DNI datasets that can be retrieved for most of the locations of the world from satellite estimates. The method consists of three steps. In the first step, we use the probability integral transform method to obtain 100 annual set series at monthly scale. The second step, we downscale the synthetic sets from monthly to daily time resolution using a first-order autoregressive model (AR 1). In the last step, we use ND model for generate the 1-min synthetic data sets from daily sets. The algorithm is evaluated at five locations with different type of climate according to the Koppen-Geiger classification and at different temporal scales: annual, monthly, daily, and 1-min resolution. In all cases, synthetic PSYs series cover a wider range of scenarios than the observed series but maintaining their distribution. Results suggest that the synthetically generated PSYs are capable to reproduce the natural variability of the solar resource at any location facilitating the stochastic simulation of solar harnessing systems.
Overview of the CLEF 2022 SimpleText Lab: Automatic Simplification of Scientific Texts

Although citizens agree on the importance of objective scientific information, yet they tend to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Instead, they rely on shallow information on the web or social media often published for commercial or political incentives rather than the correctness and informational value. This paper presents an overview of the CLEF 2022 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.
Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents

This paper presents an overview of the second edition of HIPE (Identifying Historical People, Places and other Entities), a shared task on named entity recognition and linking in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, HIPE-2022 confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. This shared task is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of HIPE-2022, run as an evaluation lab of the CLEF 2022 conference, is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets. Tasks, corpora, and results of participating teams are presented.
A dynamic feature selection and intelligent model serving for hybrid batch-stream processing

The increasing complexity of real-world applications, especially those related to the Internet of Things and cloud computing, highlights the importance of using hybrid batch-stream processing techniques to analyze big data. Hybrid processing combines the accuracy of batch processing and the speed of stream processing. Among the important challenges of this approach are selecting relevant and diverse features to build the base models and intelligently choosing which of those models to use in computing the final results. We present the H-DIFS architecture, a dynamic and intelligent feature selection approach to address these two challenges of hybrid batch-stream processing for anomaly detection. The proposed architecture employs a dynamic feature selection method based on the genetic algorithm that is fully compatible with the nature of hybrid processing and dynamically changes the models’ features over time based on the input data stream. Additionally, two components, the model evaluator and intelligent model recommender, allow for offering various batch and stream models and selecting from them based on the defined policies. The experimental results, on the datasets with various feature set sizes, indicate that the proposed architecture increases the speed of hybrid processing and anomaly detection by eliminating irrelevant and redundant features. Moreover, it increases detection accuracy by selecting appropriate models and aggregating their results. • Integrating advantages of batch and stream processing in one architecture. • Proposing dynamic feature selection compatible with hybrid processing. • Providing various batch and stream models, and merging their results. • An enhanced solution for accurate real-time anomaly detection.
Enhancing dissipative cat-state generation via nonequilibrium pump fields

Cat states, which were initially proposed to manifest macroscopic superpositions, play an outstanding role in fundamental aspects of quantum dynamics. In addition, they have potential applications in quantum computation and quantum sensing. However, cat states are vulnerable to dissipation, which puts the focus of cat-state generation on higher speed and increased robustness. Dissipative cat-state generation is a common approach based on the nonlinear coupling between a lossy pump field and a half-frequency signal field. In such an approach, the pump field is usually kept in equilibrium, which limits the cat-state generation. We show that the equilibrium requirement can be removed by leveraging a synchronous pump method. In this nonequilibrium regime, the speed of the cat-state generation can be increased by one order of magnitude, and the robustness to single-photon loss can be enhanced. The realization of synchronous pumps is discussed for both time-multiplexed systems and standing modes.
SphereMap: Dynamic Multi-Layer Graph Structure for Rapid Safety-Aware UAV Planning

A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this \paper{}. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we demonstrate how this graph representation can be converted into a lightweight shareable topological-volumetric map of the environment, which enables decentralized multi-robot cooperation. The proposed approach was successfully validated in several kilometers of real subterranean environments, such as caves, devastated industrial buildings, and in the harsh and complex setting of the final event of the DARPA SubT Challenge, which aims to mimic the conditions of real search and rescue missions as closely as possible, and where our approach achieved the \nth{2} place in the virtual track.
Attention-Based CNN Ensemble for Soil Organic Carbon Content Estimation With Spectral Data

At present, deep learning method relies on its strong feature extraction ability has been successfully applied to the estimation of soil organic carbon (SOC) content with hyperspectral data. However, due to the high dimensionality of hyperspectral data and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands. To address this issue, in this letter, attention mechanism is combined with convolutional neural network (CNN) to assign different weights to different bands of the hyperspectral data. This method constructs a three-layer CNN with a squeeze-and-excitation module at the front of it. Then, five attention-based CNNs are combined to establish an ensemble regression system with diversity. The inputs of each branch in this system are the original hyperspectral data and its transformed data. Moreover, an improved label distribution smoothing technique is proposed to address the problem of imbalanced samples. The experimental results on three soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) 2009, LUCAS2015 and AfSIS (Africa Soil Information Service), show that this method obtains good estimation performance compared with several state-of-art methods, especially in the areas with high SOC content which has small sample sizes.
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification

Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. In this paper, we provide a new perspective to explain the success of knowledge distillation based on the information theory, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for classification. To this end, we consider the signal processing in a DNN as a layer-wise process of discarding information. A knowledge point is referred to as an input unit, the information of which is discarded much less than that of other input units. Thus, we propose three hypotheses for knowledge distillation based on the quantification of knowledge points. 1. The DNN learning from knowledge distillation encodes more knowledge points than the DNN learning from scratch. 2. Knowledge distillation makes the DNN more likely to learn different knowledge points simultaneously. In comparison, the DNN learning from scratch tends to encode various knowledge points sequentially. 3. The DNN learning from knowledge distillation is often more stably optimized than the DNN learning from scratch. To verify the above hypotheses, we design three types of metrics with annotations of foreground objects to analyze feature representations of the DNN, i.e., the quantity and the quality of knowledge points, the learning speed of different knowledge points, and the stability of optimization directions. In experiments, we diagnosed various DNNs on different classification tasks, including image classification, 3D point cloud classification, binary sentiment classification, and question answering, which verified the above hypotheses.
Application Deployment Strategies for Reducing the Cold Start Delay of AWS Lambda

Serverless computing has emerged in recent years as the new computing paradigm adopted by key players in the industry for software development. This new paradigm has seen rapid growth in adoption due to its unique billing model and scaling characteristics. Public cloud providers such as Amazon Web Services (AWS) offer several configurations and language runtimes for their serverless functions. Although extensively explored by the research community, this field still lacks current studies that address the many challenges developers face when leveraging serverless functions for real-world applications. One of these challenges that are often overseen by many programmers is the cold start problem which is present in any serverless application. For this reason, we propose the first study to characterize the underlying cold start impacts caused by the choice of language runtime, application size, memory size and deployment type on AWS Lambda. In this paper, we analyze the performance of the container-based deployment and ZIP-based deployment of AWS Lambda using a variety of language runtimes and applications running with different function configurations; then we propose guidelines for developers and cloud managers to consider when deploying/managing the workloads on the cloud.
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization

We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the degree of heterogeneity of local data across parties, we provide convergence guarantees for Fed+ for convex and non-convex loss functions under different (robust) aggregation methods. The Fed+ theory is also equipped to handle heterogeneous computing environments including stragglers without additional assumptions; specifically, the convergence results cover the general setting where the number of local update steps across parties can vary. We demonstrate the benefits of Fed+ through extensive experiments across standard benchmark datasets.
Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum devices may be able to tackle complex problems even without error correction. However, coherent errors due to the increased complexity of these devices is an outstanding issue. They can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like quantum imaginary time evolution are susceptible to these errors. This article presents the combination of both noise tailoring using randomized compiling and error mitigation with purification. We also show that cycle benchmarking gives an estimate of the reliability of the purification. We apply this method to the quantum imaginary time evolution of a transverse field Ising model and report an energy estimation error and a ground-state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.
Safety-Critical Trajectory Generation and Tracking Control of Autonomous Underwater Vehicles

Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV's actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.
NFOMP: Neural Field for Optimal Motion Planner of Differential Drive Robots With Nonholonomic Constraints

Optimal motion planning is one of the most critical problems in mobile robotics. On the one hand, classical sampling-based methods propose asymptotically optimal solutions to this problem. However, these planners cannot achieve smooth and short trajectories in reasonable calculation time. On the other hand, optimization-based methods are able to generate smooth and plain trajectories in a variety of scenarios, including a dense human crowd. However, modern optimization-based methods use the precomputed signed distance function for collision loss estimation, and it limits the application of these methods for general configuration spaces, including a differential drive non-circular robot with non-holonomic constraints. Moreover, optimization-based methods lack the ability to handle U-shaped or thin obstacles accurately. We propose to improve the optimization methods in two aspects. Firstly, we developed an obstacle neural field model to estimate collision loss; training this model together with trajectory optimization allows improving collision loss continuously, while achieving more feasible and smoother trajectories. Secondly, we forced the trajectory to consider non-holonomic constraints by adding Lagrange multipliers to the trajectory loss function. We applied our method for solving the optimal motion planning problem for differential drive robots with non-holonomic constraints, benchmarked our solution, and proved that the novel planner generates smooth, short, and plain trajectories perfectly suitable for a robot to follow, and outperforms the state-of-the-art approaches by 25% on normalized curvature and by 75% on the number of cusps in the MovingAI environment.
Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems

As an important application of pattern recognition, emotion recognition can make Cyber-Physical-Social Systems (CPSS) provide more efficient services for humans. In order to improve the recognition accuracy, this paper proposes an electroencephalogram (EEG) emotion recognition method based on brain connectivity reservoir (BCR) and valence lateralization (VL) for CPSS. First, for the purpose of comprehensively considering the temporality, nonlinearity, and correlation of EEG signals, an emotion recognition model based on BCR is established. Specifically, according to the connectivity index, the correlation between EEG channels is calculated to determine the brain connectivity structure of BCR, and the features of EEG signals are represented through BCR, then the classification result is obtained by the fully connected neural network according to the feature representation. Second, for the purpose of enhancing the feature representation capability of BCR, a training algorithm of BCR based on VL is proposed. Specifically, BCR is divided into two parts, i.e., the left hemi-BCR and the right hemi-BCR. These two parts are trained separately, so that the lateralization characteristic of the brain is better reflected. Finally, the experimental results on DEAP demonstrate that the proposed method achieves a recognition accuracy of 85.55% which is higher than the state-of-the-art methods.
A dynamic global backbone updating for communication-efficient personalised federated learning

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