Reviewerly/exHarmony huggingface dataset:
Meta-Residual Policy Learning: Zero-Trial Robot Skill Adaptation via Knowledge Fusion

Adapting the mastered manipulation skill to novel objects is still challenging for robots. Recent works have attempted to endow the robot with the ability to adapt to unseen tasks by leveraging meta-learning. However, these methods are data-hungry in the training phase, which limits their application in the real world. In this paper, we propose Meta-Residual Policy Learning (MRPL) to reduce the cost of policy learning and adaptation. During meta-training, MRPL accelerates the learning process by focusing on the residual task-shared knowledge that is hard to be embedded in the hand-engineered controller. During testing, MRPL achieves fast adaptation on similar unseen tasks through fusing task-specific knowledge in the demonstration with task-shared knowledge in the learned policy. We conduct a series of simulated and real-world peg-in-hole tasks to evaluate the proposed method. The experimental results demonstrate that MRPL outperforms prior methods in robot skill adaptation. Code for this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Bartopt/code4MRPL</uri> .
Knowledge embedding via hyperbolic skipped graph convolutional networks

With the aim of constructing a low-dimensional representation space, Hyperbolic Knowledge Embeddings have gradually become a hot spot in various information retrieval and machine learning tasks. However, most of the existing Hyperbolic knowledge embedding methods focus on the shallow embedding, and often ignore the network structure characteristics (e.g., hierarchy) of Knowledge Graphs. Therefore, this paper designs a novel Hyperbolic Skipped Knowledge Graph Convolutional Network, HSKGCN, to improve link prediction accuracy with low embedding dimension requirements. Firstly, the model is designed based on the hyperbolic geometric operations on the Poincaré ball, which can effectively utilize the characteristics of the hyperbolic geometry (e.g., Poincaré ball) to capture the hierarchy of Knowledge Graphs; Secondly, each single-layer convolutional layer introduces the feature aggregation weight, which ensures the reasonable distribution of node features during the aggregation process; In addition, the skip-connection mechanism is applied to HSKGCN to weaken the information loss caused by the stacked of the graph convolutional layers; Finally, we evaluate HSKGCN on benchmark datasets, WN18RR and FB15k-237. Experiments show that HSKGCN achieves substantial improvements against state-of-the-art models on the 32-dimensional embedding task, and the results of different relations on WN18RR show graphs similar with tree topology can performer better.
Multi-Participant Federated Feature Selection Algorithm with Particle Swarm Optimizaiton for Imbalanced Data under Privacy Protection

In real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants show imbalanced or even miss some classes, further increasing the difficulty of solving this kind of problem. In view of this, by introducing a trusted third party to process and integrate optimal information from participants, this paper proposes a multi-participant federated evolutionary feature selection algorithm for imbalanced data under privacy protection. Firstly, a multi-level joint sample filling strategy with multiple participants (called sampling-rough selection-fine tuning strategy) is proposed to fill imbalanced or empty classes on each participant while ensuring data privacy. Then, a federated evolutionary feature selection method based on particle swarm optimization (PSO) is proposed by periodically sharing the optimal feature subsets obtained by PSOs on participants. Finally, the proposed algorithm is applied to 15 test datasets, and compared with several typical imbalanced feature selection algorithms and two kinds of ensemble feature selection algorithms. The experimental results show that the proposed algorithm can significantly promote the ability of each participant on processing imbalanced datasets, and improve the classification accuracy of obtained feature subsets while protecting the participants' data privacy.
iLQR for Piecewise-Smooth Hybrid Dynamical Systems

Trajectory optimization is a popular strategy for planning trajectories for robotic systems. However, many robotic tasks require changing contact conditions, which is difficult due to the hybrid nature of the dynamics. The optimal sequence and timing of these modes are typically not known ahead of time. In this work, we extend the Iterative Linear Quadratic Regulator (iLQR) method to a class of piecewise-smooth hybrid dynamical systems with state jumps by allowing for changing hybrid modes in the forward pass, using the saltation matrix to update the gradient information in the backwards pass, and using a reference extension to account for mode mismatch. We demonstrate these changes on a variety of hybrid systems and compare the different strategies for computing the gradients.
Semantically Enhanced IoT-Oriented Seismic Event Detection: An Application to Colima and Vesuvius Volcanoes

Collecting massive seismic signals is a high-priority task in seismic risk evaluation, especially in densely populated areas, with cases of strong magnitude earthquake occurrence. At the same time, with the advent of the Internet of Things (IoT) paradigm, distributed and real-time environmental monitoring, supported by device interoperability, enhances the ability to collect data and make decisions especially in critical domains such as the seismic one. A crucial role is played by Semantic Web technologies that, in IoT ecosystems, promote syntactic and semantic interoperability, by enhancing the data quality that becomes ontology-annotated. This article introduces an IoT-oriented framework to collect seismic data, process and store them into a knowledge base. An ontology called Volcano Event Ontology (VEO) modeled for the seismic domain aims at gathering seismic signals collected by sensors for seismic event detection. The ontology is built on the well-known SSN/SOSA ontology, modeled to describe the systems of sensors, actuators, and observations. Seismic data have been collected by monitoring networks at Mt. Vesuvius (Naples, Italy) and Colima volcano (Mexico) and consolidated in the ontology. Moreover, the seismic data are also processed by a classification module to detect different seismic events (Volcano-Tectonic and long-period earthquakes, underwater explosions, and quarry blasts) and then stored in the knowledge base. Prompt detection and classification are, indeed, relevant to track any variation in the volcano dynamics, becoming crucial in cases of explosive crises. Finally, the VEO-driven knowledge base can be queried to get time-based seismic data and detected events, by queries.
Financial fraud risk analysis based on audit information knowledge graph

The frequent occurrence of financial fraud caused by listed companies has seriously hindered the healthy development of the capital market. Existing studies usually analyze financial fraud through the effectiveness of audit opinion or the correlation between auditor change rate and financial fraud, ignoring the relationship between different participants. By using the audit relationships among corporations, audit firms and auditors, this paper constructs an audit information knowledge graph and proposes a knowledge graph reasoning framework based on the Sub Feature Extraction method to detect potential fraud corporations. In the process of analyzing the audit data of 376 companies in the China Growth Enterprises Market from 2013 to 2019, it is found that potential fraud corporations can be well identified by searching from the known fraud corporations using searched paths. In addition, we find two new audit features of financial fraud corporations which are respectively related to abnormal audit opinions issued by auditors and abnormal associations of audit firms. They can help regulators more effectively find the potential financial fraud corporations that need to be supervised.
Scalable, password-based and threshold authentication for smart homes

Abstract Smart homes are a special use-case of the IoT paradigm, which is becoming more and more important in our lives. Although sensors, devices and applications make our daily lives easier, they often collect our sensitive data, which may lead to security problems (e.g., hacked devices, botnets, etc.). In several cases, the appropriate security mechanisms are missing within the devices. Therefore, security measures have become a central topic in the field of IoT. The most essential requirements are secure user–device authentication and confidentiality of transferred sensitive data. Passwords are the most widely used factors in various areas, such as user authentication, key establishment, and also secret sharing. Password-based protocols that are resistant to typical threats, such as offline dictionary, man-in-the-middle and phishing attacks, generate new session keys. The major aim of these solutions is to guarantee high-level security, even if a user applies a single low-entropy human memorable password for all their accounts. We introduce a threshold and password-based, distributed, mutual authenticated key agreement with key confirmation protocol for a smart home environment. The proposed protocol is a scalable and robust scheme, which forces the adversary to corrupt $$l-1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> smart home devices, where l is the threshold, in order to perform an offline dictionary attack. The protocol is designed to achieve password-only setting, and end-to-end security if the chosen IoT devices are also authenticated besides the user. We also provide a security analysis of the protocol in AVISPA. We apply the on-the-fly model checker and the constraint-logic-based attack searcher to perform protocol verification for bounded numbers of sessions. We show that the proposed protocol provides session key secrecy and mutual authentication of the user and the device manager. Since efficiency is a crucial aspect, we implemented our protocol to measure the computation and communication costs and demonstrate that our solution is appropriate and eligible for smart homes.
A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network

This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis.
Can Uncertainty Be Quantified?

Abstract In order to explore the quantifiability and formalizability of uncertainty a wide range of uncertainties are investigated. They are summarized under eight main categories: factual, possibilistic, metadoxastic, agential, interactive, value, structural, and linguistic uncertainty. This includes both classical uncertainty and the uncertainties commonly called great, deep, or radical. For five of the eight types of uncertainty, both quantitative and non-quantitative formalizations are meaningful and available. For one of them (interactive uncertainty), only non-quantitative formalizations seem to be meaningful, and for two (agential and structural uncertainty) neither quantitative nor non-quantitative formalization seems to be a useful approach.
Communication-Efficient Federated Learning

Owing to the universal availability of 5G networking networks, both business and academia have started to explore 6G interchanges. 6G is widely intended to be based on artificial intelligence’s pervasiveness in order to accomplish machine learning dependent on evidence (ML) arrangements in a diverse environment and large-scale organizations (AI). Traditional machine learning procedures, on the other side, include centralized data storage and processing by a single employee. Due to escalating security issues, this increasingly has become restriction on a wide range of applications in daily life. Federated learning, as a growing distributed AI method with a security-conscious design, is particularly appealing for a variety of remote applications and is being viewed as one of the critical solutions for achieving universal AI in 6G. We’ll go into the partnership between 6G and federated learning in this chapter, as well as some future 6G federated learning implementations. Then, in relation to 6G interchanges, we go through main technical problems, compare federated learning approaches, and answer unanswered questions for potential federated learning analysis.
Monitoring the blades of a wind turbine by using videogrammetry

Wind power is a reliable source of clean energy that is extensively used around the world. The blades of wind turbines are subject to a variety of mechanical stresses while they rotate and it is important to monitor their condition and diagnose problems to ensure their safe operation. Prevalent methods to this end are designed to be applied when the turbines are not operating. Moreover, current techniques to examine turbines are limited in that they can be used only to inspect defects on the surfaces of their blades. However, many faults in the blades of wind turbines are apparent only when they are operating, which makes it important to closely monitor the state of the blades as the turbine bears loads of varying magnitudes. Based on advanced videogrammetry, this study proposes a series of techniques to detect structural defects in the blades of turbines during their normal operation. Measurements of the global movement and local deformation in their operation, combined with inspections of the surface of the blades through an unmanned aerial vehicle, enable a complete inspection of the blades of the turbine as well as its systems of transmission, gear box, and wind power tower. The proposed method represents an important technical advancement that can help repair defects in wind turbines in a timely manner to reduce the cost of maintenance of wind power systems.
An eye tracker based on webcam and its preliminary application evaluation in Chinese reading tests

There is increasing evidence that eye trackers can be used in neuroscience research to assess cognitive function although expensive prices or low availability still prevent its ubiquitous application. Meanwhile, in tasks such as reaction speed testing in cognitive function assessment, the requirements for spatial performance are less critical than temporal resolution. Therefore, in this report we propose an eye-tracking system based on a common web-camera, to evaluate if it's possible to achieve a decent temporal resolution for eye-tracking tasks in cognitive experiments and then further evaluate it in Chinese reading tests. The performance of the proposed setup are evaluated in comparison experiments including 62 healthy subjects, with a commercialized eye-tracker (SMI RED250) simultaneously deployed. It’s found in the results that the webcam-based system showed no significant difference in the recognition accuracy of occurrence and switching of the eye movement events compared with the SMI system though the spatial eye-tracking performance is significantly worse. Then in an application experiment, cognitive-related Chinese reading tests are adapted and EMs of 27 young (18–30 years) and 28 elderly (over 60 years) readers are recorded to study their difference in Chinese reading. In the results there are significant differences in reading-time-per-sentence between young and elderly subjects. Meanwhile, a study on the reading-time-per-sentence difference using reading materials of different difficulties shows that elderly subjects are more sensitive to difficulty changes. These results preliminary demonstrated the feasibility of a Chinese reading test with a web-camera setup as an alternative option when the expensive eye-tracker is not available.
Consistency and Diversity Induced Human Motion Segmentation

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches.
GAN for vision, KG for relation: A two-stage network for zero-shot action recognition

Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most methods neglected the connotative relation and extensional relation between the action classes, which leads to the poor generalization ability of the zero-shot learning. Furthermore, the learned classifier inclines to predict the samples of seen class, which leads to poor classification performance. To solve the above problems, we propose a two-stage deep neural network for zero-shot action recognition, which consists of a feature generation sub-network serving as the sampling stage and a graph attention sub-network serving as the classification stage. In the sampling stage, we utilize generative adversarial networks (GAN) trained by action features and word vectors of seen classes to synthesize the action features of unseen classes, which can balance the training sample data of seen classes and unseen classes. In the classification stage, we construct a knowledge graph (KG) based on the relationship between word vectors of action classes and related objects, and propose a graph convolution network (GCN) based on attention mechanism, which dynamically updates the relationship between action classes and objects, and enhances the generalization ability of zero-shot learning. In both stages, we all use word vectors as bridges for feature generation and classifier generalization from seen classes to unseen classes. We compare our method with state-of-the-art methods on UCF101 and HMDB51 datasets. Experimental results show that our proposed method improves the classification performance of the trained classifier and achieves higher accuracy.
Support Vector Machine-Based Rapid Magnitude Estimation Using Transfer Learning for the Sichuan–Yunnan Region, China

ABSTRACT The Sichuan–Yunnan region is a seismically active area. To explore the feasibility of using the support vector machine (SVM) method for magnitude estimation in the area and to improve the rapid magnitude estimation accuracy, we construct an SVM magnitude estimation model using transfer learning (TLSVM-M model) based on a single-station record in this study. We find that the magnitude estimation of a single station shows that for the test dataset, within the 3 s time window after the P-wave arrival, the average absolute error (which reflects the size of the estimated magnitude error as a whole) and standard deviation (which reflects the scatter of magnitude estimation error) of the magnitudes estimated by the TLSVM-M model are 0.31 and 0.41, respectively, which are less than those of the SVM magnitude estimation model without transfer learning (0.44 and 0.55, respectively), the τc method (1.35 and 1.74, respectively) and the Pd method (0.44 and 0.56, respectively). In addition, in test involving five earthquake events via the TLSVM-M model, at 1 s after the first station is triggered, the magnitudes of three events (Ms 4.2, 5.2, and 6.3) are estimated within an error range of ±0.3 magnitude units. For the other two earthquakes (Ms 6.6 and 7.0), there is an obvious magnitude underestimation problem at 1 s after the first station is triggered, with less underestimation by increasing time after the first station is triggered. Meanwhile, for these two events (Ms 6.6 and 7.0), within 13 s after the first station was triggered, the magnitude estimation errors are both within ±0.3 magnitude units. The TLSVM-M model has the capability of rapid magnitude estimation for small-to-moderate events in the Sichuan–Yunnan region. Meanwhile, we infer that the proposed model may have potential in earthquake early warning.