Reviewerly/exHarmony huggingface dataset:
Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation

Unwanted errors caused by hand tremors are a bottleneck for the application of teleoperation robots in space explorations, underwater explorations, and minimally invasive surgery. In order to eliminate hand tremor signals in teleoperation control systems, two tremor-filtering models based on artificial neural networks are defined. With the purpose of decreasing the errors of tremor filtering models, a novel Broad Learning Extreme Learning Machine with Improved Equilibrium Optimizer (IEO-BLELM) is proposed. Firstly, the structure of Extreme Learning Machine (ELM) is re-designed by coupling with broad learning. Time series and smoothing are introduced as feature extraction layer and enhancement layer, respectively. Secondly, different activation functions are selected to construct Broad Learning ELM (BLELM). An Improved Equilibrium Optimizer is introduced to optimize input weights, thresholds, and parameters of the BLELM model. To verify the performance of the IEO-BLELM model, the proposed model is applied to six examples and compared with other models. The results show that Mean Absolute Error (MAE) of the proposed model in six examples is at least lower than 0.253. As compared with the ELM, the MAE of the IEO-BLELM model can be decreased by 51.03% through reasonable improvement strategies. In particular, estimation errors are mainly contributed to peak and the proposed model significantly reduces the peak errors. The forecasting performance of the proposed model is better than that of previously existing models. In general, this study provides effective models to eliminate hand tremor signals in teleoperation control systems.
Oxygen fugacity during tin ore deposition from primary fluid inclusions in cassiterite

• Redox conditions during tin ore formation from fluid inclusions in cassiterite . • Studied cassiterite precipitated between QFM and above NNO up to MH. • Hydrothermal cassiterite forms at more oxidized conditions than previously thought. • In situ Raman spectroscopic experiment demonstrates Sn(IV) aq at RRO . • Cassiterite formation by redox reaction may be much less common than assumed so far. Reconstruction of the redox conditions during formation of rocks and ore deposits often remains a challenging task, particularly if no reactions buffering the oxygen fugacity ƒO 2 can be identified. This information is crucial for genetic models of numerous ore deposit types, and consequently there has been long-standing and in recent years increasing research activity addressing complexation and redox state of metals in fluids at high temperatures. In this study, we constrain ƒO 2 during tin ore formation from the composition of primary fluid inclusions in the principal ore mineral cassiterite, SnO 2 . We show that the analysed cassiterite samples from worldwide locations (from hydrothermal veins, greisens, skarn deposits, and pegmatites) precipitated at ƒO 2 between QFM (quartz-fayalite-magnetite) and above NNO (nickel-nickel oxide), with more oxidizing conditions up to MH (magnetite-hematite) for certain vein/greisen deposits. This, together with in situ Raman spectroscopic experiments, supports the hypothesis that Sn(IV) prevailed over Sn(II) in many if not most tin-ore forming hydrothermal fluids, which implies that redox reactions play a smaller role than assumed in previous formation models of hydrothermal tin deposits.
Sample and feature selecting based ensemble learning for imbalanced problems

Imbalanced problem is concerned with the performance of classifiers on the data set with severe class imbalance distribution. Traditional methods are misled by the majority samples to make the incorrect prediction and fail to make full use of minority samples. This paper is motivated to design a novel hybrid ensemble learning strategy named Sample and Feature Selection Hybrid Ensemble Learning (SFSHEL) and combine it with random forest to improve the classification performance of imbalanced data. Specifically, SFSHEL considers cluster-based stratification to undersample the majority samples and adopts sliding windows mechanism to generate a diversity of feature subsets, simultaneously. Then the weights trained through validation are assigned to different base learners and SFSHEL makes the prediction by weighted voting at last. In this manner, SFSHEL could not only guarantee the acceptable performance, but also save computational time. Furthermore, the weighting process makes SFSHEL interpret the importance of each selected feature set, which is important in the real-world scenarios. The contributions of the proposed strategy are: (1) reducing the impact of class imbalance distribution, (2) assigning based learner weights only once after the training process, and (3) generating weights of features to help interpret the importance of clinical features. In practice, the random forest is adopted as the base learner for SFSHEL, so as to build a classifier abbreviated as SFSHEL-RF. The experiments show the average performance of the proposed SFSHEL-RF on a part of KEEL dataset reaches 91.37%, which is comparable to our previous best ECUBoost-RF method and higher than the other eleven methods. On the clinical heart failure datasets, the performance of SFSHEL-RF can stably reach the level of the top three with three indicators. The experimental results on both the standard imbalanced and clinical heart failure datasets validate the effectiveness and stability of SFSHEL-RF.
Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

This paper studies the multitarget tracking problem based on an asynchronous network of sensors with different sampling rates, where each sensor runs a cardinalized probability hypothesis density (CPHD) filter. To fuse the filter estimates obtained at different sensors conditioned on asynchronous measurements, an arithmetic averaging approach is recursively carried out in a timely manner according to the network-wide sampling time sequence. The intersensor communication is conducted by a so-called partial flooding scheme, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. The fused results may not feedback to the filter, which will avoid communication delay to the local filters cased by intersensor fusion at the expense of reduced information gain. Furthermore, an extension of the proposed multi-sensor CPHD filter based on the bootstrap filtering algorithm is given to accommodate unknown clutter rate and detection profile. Numerical simulations are performed to test the proposed approaches.
Parallel Key-Insulated Multiuser Searchable Encryption for Industrial Internet of Things

With the rapid development of the industrial Internet of Things (IIoT) and cloud computing, an increasing number of companies outsource their data to cloud servers to save costs. To protect data privacy, sensitive industrial data must be encrypted before being outsourced to cloud servers. A multiuser searchable encryption (MUSE) scheme was introduced to ensure high efficiency of encrypted data retrieval. In an IIoT system with numerous users, the existing MUSE schemes suffer from certain key exposure problems owing to the limited key protection of smart devices and frequent queries by users. In this article, we propose a parallel key-insulated MUSE scheme for IIoT. This scheme utilizes broadcast encryption technology to implement MUSE. In addition, our scheme introduces a key-insulated primitive to improve the tolerance to key exposure. The security of our scheme is proved in the random oracle model. The experimental results show that our scheme achieves high computational efficiency.
Joint Scheduling and Resource Allocation for Efficiency-Oriented Distributed Learning Over Vehicle Platooning Networks

The limited communication and computing resources, as well as the rising concerns about the privacy protection, bring significant challenges to the massive data training and analysis in vehicular networks. To address these challenges, in this paper a platoon-based distributed learning framework design for data learning is carried out, where the vacant computation resources of vehicle platooning networks are leveraged. In the proposed framework, a 2-phase Markovian stochastic process is utilized to depict the learning service heterogeneity for each participating vehicle. Meanwhile, we propose a joint scheduling and resource allocation scheme for efficiency-oriented distributed learning to maximize the learning accuracy subject to a given learning time constraint. The optimization problem is solved as follows. First, given the scheduled vehicles, the communication resource allocation is modeled as a minimum-maximum problem to minimize the learning delay of each learning round. Subsequently, an efficiency-oriented unbiased global aggregation policy is proposed to explore the convergence difference between partial scheduling and total scheduling. Considering the learning convergence and remaining time, an on-demand scheduling scheme is introduced to determine the number of scheduled vehicles. Finally, combining the learning efficiency of each vehicle with the scheduled number of vehicles, the scheduled vehicle set is selected. Simulations results show that the proposed scheduling policy can schedule the number of participating vehicles on demand based on the trade-off between learning performance and learning latency.
A method for identifying the boundary of regions in welded coupon specimens using digital image correlation

The coupon specimen with a transverse butt weld in the middle could be used for determining local constitutive properties of the heat-affected zone (HAZ) and the weld metal (WM) based on the digital image correlation (DIC). However, limited research is reported to demonstrate how to identify the boundary of each region in DIC results. Accordingly, it is difficult to determine the adequate gauge length for measuring the strain of each region and establishing a stress–strain curve for Finite Element Analysis (FEA) of structural problems. In this paper, a method for identifying the region's boundary is proposed based on coupon tests using three steel grades, S355, S500, and S700, corresponding to three weld matching types, match, overmatch, and undermatch, respectively. First, the hardness and the microstructure investigation were conducted to determine the region's boundary. Then, the boundary was identified based on the DIC result using the proposed method. Finally, the identified HAZ regions were verified against hardness results. Using the proposed method, the gauge length for measuring the strain of a single region, such as HAZ and WM, are determined based on the DIC results. The primary purpose is to establish the stress–strain relationship for FEA of welded joints.
Facial Expression Recognition Using Dynamic Local Ternary Patterns With Kernel Extreme Learning Machine Classifier

Rapid growth in advanced human-computer interaction (HCI) based applications has led to the immense popularity of facial expression recognition (FER) research among computer vision and pattern recognition researchers.Lately, a robust texture descriptor named Dynamic Local Ternary Pattern (DLTP) developed for face liveness detection has proved to be very useful in preserving facial texture information.The findings motivated us to investigate DLTP in more detail and examine its usefulness in the FER task.To this end, a FER pipeline is developed, which uses a sequence of steps to detect possible facial expressions in a given input image.Given an input image, the pipeline first locates and registers faces in it.In the next step, using an image enhancement operator, the FER pipeline enhances the facial images.Afterward, from the enhanced images, facial features are extracted using the DLTP descriptor.Subsequently, the pipeline reduces dimensions of the high-dimensional DLTP features via Principal Component Analysis (PCA).Finally, using the multi-class Kernel Extreme Learning Machine (K-ELM) classifier, the proposed FER scheme classifies the features into facial expressions.Extensive experiments performed on four in-the-lab and one in-the-wild FER datasets confirmed the superiority of the method.Besides, the cross-dataset experiments performed on different combinations of the FER datasets revealed its robustness.Comparison results with several state-of-the-art FER methods demonstrate the usefulness of the proposed FER scheme.The pipeline with a recognition accuracy of 99.76%, 99.72%, 93.98%, 96.71%, and 78.75%, respectively, on the CK+, RaF, KDEF, JAFFE, and RAF-DB datasets, outperformed the previous state-of-the-art.
Projection Path Explorer: Exploring Visual Patterns in Projected Decision-making Paths

In problem-solving, a path towards a solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories—for different initial conditions, end states, and solution strategies—in the same embedding space. We argue that general statements about the problem-solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik’s cube), strategy games (chess), and optimization problems (neural network training). We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.
Towards information-rich, logical dialogue systems with knowledge-enhanced neural models

Dialogue systems have made massive promising progress contributed by deep learning techniques and have been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic responses because they cannot ground dialogue context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in dialogue systems, namely knowledge-enhanced dialogue systems. The challenges of knowledge-enhanced dialogue systems include how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into responses generation process. Combined with external knowledge, dialogue systems can deeply understand the dialogue context, and generate more informative and logical responses. This survey gives a comprehensive review of knowledge-enhanced dialogue systems, summarizes research progress to solve these challenges and proposes some open issues and research directions.
Importance of real-time PGV in terms of lead-time and shakemaps: Results using 2018 ML 6.2 &amp; 2019 ML 6.3 Hualien, Taiwan earthquakes

Two earthquakes having almost the same magnitude occurred in the Hualien area of Taiwan in 2018 and 2019. The 2018 earthquake had a magnitude ML 6.2 produced severe destruction; however, the 2019 earthquake (ML = 6.3) did not cause any severe damage. The P-Alert Strong Motion Network provides real-time shakemaps, in addition, to earthquake early warning (EEW) in terms of lead-time. Each instrument provides a different lead-time using peak ground acceleration (PGA) and peak ground velocity (PGV). During both the events, the instruments reported a lead-time of 1.5 to 8.0 s in the epicentral region. This network system also generated high-quality shakemaps during both earthquakes. The shakemaps showed that the higher PGAs are concentrated in the epicentral region for the 2018 and 2019 earthquakes. The lower PGA contour (≥25 Gal) extended to a broader area, including Taipei, during the 2019 earthquake compared to the 2018 earthquake. However, PGV shakemaps display a different pattern. The higher PGV values (more than 17 cm/s) are observed in the epicentral region during the 2018 earthquake (locations suffering building collapse) compared to the 2019 earthquake, suggesting that PGV correlates better with damage distribution as compared to the PGA. The PGV shakemap, currently only available for the P-Alert network, provides crucial information that complements the PGA issued by the official agency in Taiwan.
FOVEA: Foveated Image Magnification for Autonomous Navigation

Efficient processing of high-resolution video streams is safety-critical for many robotics applications such as autonomous driving. Image downsampling is a commonly adopted technique to ensure the latency constraint is met. However, this naive approach greatly restricts an object detector’s capability to identify small objects. In this paper, we propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. The magnified regions are those that are believed to have a high probability of containing an object, whose signal can come from a dataset-wide prior or frame-level prior computed from recent object predictions. The magnification is implemented by a KDE-based mapping to transform the bounding boxes into warping parameters, which are then fed into an image sampler with anti-cropping regularization. The detector is then fed with the warped image and we apply a differentiable backward mapping to get bounding box outputs in the original space. Our regional magnification allows algorithms to make better use of high-resolution input without incurring the cost of high-resolution processing. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. Additionally, building on top of the previous state-of-the-art in streaming detection, our method sets a new record for streaming AP on Argoverse-HD (from 17.8 to 23.0 on a GTX 1080 Ti GPU), suggesting that it has achieved a superior accuracy-latency tradeoff.
Path planning of mobile robot in unknown dynamic continuous environment using reward‐modified deep <scp>Q</scp>‐network

Abstract The path planning problem of mobile robot in unknown dynamic environment (UDE) is discussed in this article by building a continuous dynamic simulation environment. To achieve a collision‐free path in UDE, the reinforcement learning theory with deep Q‐network (DQN) is applied for the mobile robot to learn optimal decisions. A reward function is designed with weight to balance the obstacle avoidance and the approach to the goal. Moreover, it is found that the relative motion between moving obstacles and robots may cause abnormal rewards and further lead to a collision between robot and obstacle. To address this problem, two reward thresholds are set to modify the abnormal rewards, and the experiments shows that the robot can avoid all obstacles and reach the goal successfully. Finally, double DQN (DDQN) and dueling DQN are applied in this article. This article compares the results of reward‐modified DQN (RMDQN), reward‐modified DDQN (RMDDQN), dueling RMDQN, and dueling RMDDQN and concludes that the result of RMDDQN is the best.
Custom-tailored clone detection for IEC 61131-3 programming languages

Automated production systems (aPS) are highly customized systems that consist of hardware and software. Such aPS are controlled by a programmable logic controller (PLC), often in accordance with the IEC 61131-3 standard that divides system implementation into so-called program organization units (POUs) as the smallest software unit and is comprised of multiple textual (Structured Text (ST)) and graphical (Function Block Diagram (FBD), Ladder Diagram (LD), and Sequential Function Chart(SFC)) programming languages that can be arbitrarily nested. A common practice during the development of such systems is reusing implementation artifacts by copying, pasting, and then modifying code. This approach is referred to as code cloning. It is used on a fine-granular level where a POU is cloned within a system variant. It is also applied on the coarse-granular system level, where the entire system is cloned and adapted to create a system variant, for example for another customer. This ad hoc practice for the development of variants is commonly referred to as clone-and-own. It allows the fast development of variants to meet varying customer requirements or altered regulatory guidelines. However, clone-and-own is a non-sustainable approach and does not scale with an increasing number of variants. It has a detrimental effect on the overall quality of a software system, such as the propagation of bugs to other variants, which harms maintenance. In order to support the effective development and maintenance of such systems, a detailed code clone analysis is required. On the one hand, an analysis of code clones within a variant (i.e., clone detection in the classical sense) supports experts in refactoring respective code into library components. On the other hand, an analysis of commonalities and differences between cloned variants (i.e., variability analysis) supports the maintenance and further reuse and facilitates the migration of variants into a software productline (SPL). In this paper, we present an approach for the automated detection of code clones within variants (intra variant clone detection) and between variants (inter variant clone detection) of IEC61131-3 control software with arbitrary nesting of both textual and graphical languages. We provide an implementation of the approach in the variability analysis toolkit (VAT) as a freely available prototype for the analysis of IEC 61131-3 programs. For the evaluation, we developed a meta-model-based mutation framework to measure our approach's precision and recall. Besides, we evaluated our approach using the Pick and Place Unit (PPU) and Extended Pick and Place Unit (xPPU) scenarios. Results show the usefulness of intra and inter clone detection in the domain of automated production systems.
Cloud computing and human resource management: systematic literature review and future research agenda

Purpose This study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research. Design/methodology/approach The systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda. Findings The four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives. Research limitations/implications This study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact. Originality/value This paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.