Reviewerly/exHarmony huggingface dataset:
Secure Ride-Sharing Services Based on a Consortium Blockchain

Due to poor traffic conditions and the high costs of traveling by private cars, ride sharing has become a popular means to trip. In view of the security threats and centralization existing in the current ride-sharing service, we propose a secure ride-sharing scheme based on a consortium blockchain, which can guarantee the security, confidentiality, and privacy of data interaction via attribute-based proxy re-encryption algorithm. First, the passenger presets the access structure and encrypts the data using attribute-based encryption. The ciphertext is then sent to the roadside unit (RSU), which broadcasts the carpooling request to the driver. After receiving the request, the driver sends the itinerary attribute to RSU, which performs carpool matching according to received ciphertext and itinerary attributes, then the ciphertext is re-encrypted and sent to the matched driver. Second, the master node uses an improved Delegated Proof-of-Stake (DPoS) consensus to verify the carpool record, which is stored on the blockchain after the verification is successful. In case of disputes, block data can be utilized for traceability. Third, drivers and passengers use the credibility mechanism to score each other after ride sharing. In addition, trusted authority can reveal the real identity of malicious users. Finally, we conduct a security analysis and performance evaluation for our proposed scheme. The results manifest that our scheme not only meets the security and privacy requirements of ride-sharing services but also effectively resists potential security risks. Therefore, our scheme is feasible, efficient, and suitable for ride-sharing services.
MTTLADE: A multi-task transfer learning-based method for adverse drug events extraction

Extracting mentions of Adverse Drug Events (ADEs) and the potential relationships among them from clinical textual data remains challenging tasks due to the following issues: (1) many ADEs mentions have multiple relations, also known as the multi-head issue, and (2) many ADEs relations contain discontinuous mentions. To deal with these problems, in this paper, we propose a Multi-Task Transfer Learning-based method for ADEs extraction, called MTTLADE. Firstly, the MTTLADE system converts the ADEs extraction task to a dual-task sequence labelling which includes ADEs source mention extraction (ADE-SE) and ADEs attribute-relation extraction (ADE-Att-RE) tasks. The ADE-SE task aims at extracting the source mentions that are likely related to at least one relation, while the ADE-Att-RE task consists in linking the previously identified source mentions to their target attributes and relation types by adopting a unified sequence labelling. Then, it uses the multi-task transfer learning (MTTL) based approach to process the two proposed tasks simultaneously. The MTTL adopts a shared representation obtained from the pre-trained language model learned through transformer architecture and ends up with task-specific fine-tuning. This allows the MTTLADE system to yield more generalized representation across the tasks. Finally, MTTLADE produces sequences for each task from the generated model so as to extract ADEs mentions and relations. Experimental evaluations conducted on two datasets provided by the TAC 2017 and n2c2 2018 shared tasks show the effectiveness and generalizability of MTTLADE. The proposed MTTLADE system significantly outperforms the state-of-the-art ones on both datasets. The results also show that combining transfer and multi-task learning makes MTTLADE more effective for solving the multi-head issue and extracting intricate ADEs.
Surrogate-Assisted Particle Swarm Optimization for Evolving Variable-Length Transferable Blocks for Image Classification

Deep convolutional neural networks (CNNs) have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fast depth growth and the increasingly complex topologies of CNNs. As a result, neural architecture search (NAS) has emerged to automatically design CNNs that outperform handcrafted counterparts. However, the computational cost is immense, e.g., 22400 GPU-days and 2000 GPU-days for two outstanding NAS works named NAS and NASNet, respectively, which motivates this work. A new effective and efficient surrogate-assisted particle swarm optimization (PSO) algorithm is proposed to automatically evolve CNNs. This is achieved by proposing a novel surrogate model, a new method of creating a surrogate data set, and a new encoding strategy to encode variable-length blocks of CNNs, all of which are integrated into a PSO algorithm to form the proposed method. The proposed method shows its effectiveness by achieving the competitive error rates of 3.49% on the CIFAR-10 data set, 18.49% on the CIFAR-100 data set, and 1.82% on the SVHN data set. The CNN blocks are efficiently learned by the proposed method from CIFAR-10 within 3 GPU-days due to the acceleration achieved by the surrogate model and the surrogate data set to avoid the training of 80.1% of CNN blocks represented by the particles. Without any further search, the evolved blocks from CIFAR-10 can be successfully transferred to CIFAR-100, SVHN, and ImageNet, which exhibits the transferability of the block learned by the proposed method.
Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction

Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. However, the use of DNNs in the context of 3D reconstruction from large and high-resolution image datasets is still an open challenge, due to memory and computational constraints. We propose a pipeline which takes advantage of DNNs to improve the quality of 3D reconstructions while being able to handle large and high-resolution datasets. In particular, we propose a confidence prediction network explicitly tailored for Multi-View Stereo (MVS) and we use it for both depth map outlier filtering and depth map refinement within our pipeline, in order to improve the quality of the final 3D reconstructions. We train our confidence prediction network on (semi-)dense ground truth depth maps from publicly available real world MVS datasets. With extensive experiments on popular benchmarks, we show that our overall pipeline can produce state-of-the-art 3D reconstructions, both qualitatively and quantitatively.
Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology

The field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of artificial intelligence (AI) to important questions in immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use.
Path Planning for UGVs Based on Traversability Hybrid A*

In this letter, a new method of path planning for unmanned ground vehicles (UGVs) on terrain is developed. For UGVs moving on terrain, path traversability and collision avoidance are important factors. If traversability is not considered, the planned path may lead a UGV into areas that will cause rough vehicle motion or lead to the UGV getting stuck if the traversability is low. The proposed path planning method is based on the Hybrid A* algorithm and uses estimated terrain traversability to find the path that optimizes both traversability and distance for the UGV. The path planning method is demonstrated using simulated traversability maps and is compared to the original Hybrid A* algorithm. The method is also verified through real-time experiments in real terrain, further demonstrating the benefits of terrain traversability optimization using the proposed path planning method. In the experiments, the proposed method was successfully applied for autonomous driving over distances of up to 270 m in rough terrain. Compared with the existing Hybrid A* method, the proposed method produces more traversable paths.
Deep-LIFT: Deep Label-Specific Feature Learning for Image Annotation

Image annotation aims to jointly predict multiple tags for an image. Although significant progress has been achieved, existing approaches usually overlook aligning specific labels and their corresponding regions due to the weak supervised information (i.e., “bag of labels” for regions), thus failing to explicitly exploit the discrimination from different classes. In this article, we propose the deep label-specific feature (Deep-LIFT) learning model to build the explicit and exact correspondence between the label and the local visual region, which improves the effectiveness of feature learning and enhances the interpretability of the model itself. Deep-LIFT extracts features for each label by aligning each label and its region. Specifically, Deep-LIFTs are achieved through learning multiple correlation maps between image convolutional features and label embeddings. Moreover, we construct two variant graph convolutional networks (GCNs) to further capture the interdependency among labels. Empirical studies on benchmark datasets validate that the proposed model achieves superior performance on multilabel classification over other existing state-of-the-art methods.
Class-Imbalanced Domain Adaptation: An Empirical Odyssey

Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However the current literature assumes that the label distribution is domain-invariant and only aligns the feature distributions or vice versa. In this work, we explore the more realistic task of Class-imbalanced Domain Adaptation: How to align feature distributions across domains while the label distributions of the two domains are also different? Taking a practical step towards this problem, we constructed its first benchmark with 22 cross-domain tasks from 6 real-image datasets. We conducted comprehensive experiments on 10 recent domain adaptation methods and find most of them are very fragile in the face of coexisting feature and label distribution shift. Towards a better solution, we further proposed a feature and label distribution CO-ALignment (COAL) model with a novel combination of existing ideas. COAL is empirically shown to outperform most recent domain adaptation methods on our benchmarks. We believe the provided benchmarks, empirical analysis results, and the COAL baseline could stimulate and facilitate future research towards this important problem.
The instrument transfer function for optical measurements of surface topography

Abstract For optical measurements of areal surface topography, the instrument transfer function (ITF) quantifies height response as a function of the lateral spatial frequency content of the surface. The ITF is used widely for optical full-field instruments such as Fizeau interferometers, confocal microscopes, interference microscopes, and fringe projection systems as a more complete way to characterize lateral resolving power than a single number such as the Abbe limit. This paper is a comprehensive review of the ITF, including standardized definitions, ITF prediction using theoretical simulations, common uses, limitations, and evaluation techniques using material measures.
Explainable AI in Deep Reinforcement Learning Models: A SHAP Method Applied in Power System Emergency Control

The application of artificial intelligence (AI) system is more and more extensive, using the explainable AI (XAI) technology to explain why machine learning (ML) models make certain predictions as important as the accuracy of the predictions, because it ensures the trust and transparency in the model decision-making process. For deep reinforcement learning (DRL) model, although some outstanding progress based on DRL has been made in many fields, it is difficult to explain and cannot be used in safety related occasions. Especially in power system, for the power system emergency control based on DRL, how to provide an intuitive and reliable XAI technology is urgent and necessary. The Shapley additive explanations (SHAP) method has been adopted to provide a reasonable interpretable model for an open-source platform named Reinforcement Learning for Grid Control (RLGC). Through a series of summary plots, force plots and probability of SHAP value, the under-voltage load shedding of power system based on DRL can be interpreted much easier and clearer. More importantly, this work is unique in the power system field, presenting the first use of the SHAP method and the probability of SHAP value to give explanations for emergency control based on DRL in power system.
NoPeek: Information leakage reduction to share activations in distributed deep learning

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient to such reconstruction attacks and is based on reduction of distance correlation between raw data and learned representations during training and inference with image datasets. We prevent such reconstruction of raw data while maintaining information required to sustain good classification accuracies.
A Scalable Approach to Predict Multi-Agent Motion for Human-Robot Collaboration

Human motion prediction is considered a key component for enabling fluent human-robot collaboration. The ability to anticipate the motion and subsequent intent of the partner(s) remains a challenging task due to the complex and interpersonal nature of human behavior. In this work, we propose a novel sequence learning approach that learns a robust representation over the observed human motion and can condition future predictions over a subset of past sequences. Our approach works for both single and multi-agent settings and relies on an interpretable latent space that has the implicit benefit of improving human motion understanding. We evaluated the proposed approach by comparing its performance against state-of-the-art motion prediction methods on single, multi-agent, and human-robot collaboration datasets. The results suggest that our approach outperforms other methods over all the evaluated temporal horizons, for single-agent and multi-agent motion prediction. The improved performance of our approach for both single and multi-agent settings, coupled with an interpretable latent space, can enable close-proximity human-robot collaboration.
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles

Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime—trading accuracy and latency—which minimises the inference’s energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots

Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client's activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client's response.
Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion

Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowledge graph completion infers and predicts missing edges in a KG automatically. Knowledge graph embeddings have shed light on addressing this task. Recent research embeds KGs in hyperbolic (negatively curved) space instead of conventional Euclidean (zero curved) space and is effective in capturing hierarchical structures. However, as multi-relational graphs, KGs are not structured uniformly and display intrinsic heterogeneous structures. They usually contain rich types of structures, such as hierarchical and cyclic typed structures. Embedding KGs in single-curvature space, such as Euclidean or hyperbolic space, overlooks the intrinsic heterogeneous structures of KGs, and therefore cannot accurately capture their structures. To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Specifically, we define and construct a mixed-curvature space through a product manifold combining multiple single-curvature spaces (e.g., spherical, hyperbolic, or Euclidean) with the purpose of modeling a variety of structures. However, constructing a mixed-curvature space typically requires manually defining the fixed curvatures, which needs domain knowledge and additional data analysis. Improperly defined curvature space also cannot capture the structures of KGs accurately. To address this problem, we set mixed-curvatures as trainable parameters to better capture the underlying structures of the KGs. Furthermore, we propose a Graph Neural Updater by leveraging the heterogeneous relational context in mixed-curvature space to improve the quality of the embedding. Experiments on three KG datasets demonstrate that the proposed M2GNN can outperform its single geometry counterpart as well as state-of-the-art embedding methods on the KG completion task.
Determinants of digital technology adoption in supply chain. An exploratory analysis

This paper is aimed at identifying the main patterns related to the application of new Digital Supply Chain Technologies that through the Industry 4.0 paradigm, are redefining supply chain organisations. To this end, a set of DSC initiatives has been analysed via an Analysis of Variance (ANOVA) to understand the influence of several social and economical factors on the implementation of DSC solutions. Results show that the time factor, the Gross Domestic Product per capita, the amount of foreign investment, and the expenditure in Research and Development are significant drivers of DSC technologies. In particular, Big Data are associated with a higher economic effort than Blockchain, although these two technologies, together with Augmented Reality and Artificial Intelligence, usually characterise most recent implementations. The present work might foster research about the contextual factors affecting the DSC diffusion it could assist both practitioners and policymakers in defining appropriate DSC strategies.