Reviewerly/exHarmony huggingface dataset:
Filtered data augmentation approach based on model competence evaluation

The scale of parallel corpus plays an important role in training high-quality neural machine translation models. In order to expand the scale of parallel corpus in low-resource scenarios, researchers have proposed a series of data augmentation approaches, in which the most representative work is the back-translation. The back-translation approach uses a basic translation model to translate the target monolingual corpus into the source language, and then combines it into a pseudo parallel corpus to expand the training data. Due to the simple and efficient data expansion strategies, the back-translation approach becomes the mainstream data augmentation approach of neural machine translation. However, in low-resource scenarios, only low-precision basic translation models can be used. The pseudo-parallel corpus translated by this model contains low-quality sentence pairs, which inevitably introduces noises and leads to negative impacts to translation procedure. To improve the performance of low-resource neural machine translation, we propose a filtered data augmentation method based on the model competence evaluation, which can effectively improve the quality of training data by using both the filtered back-translation and dynamic evaluation of translation model capabilities. Comparative experiments show that the proposed approach can improve the quality of training data and the performance of low-resource neural machine translation.
Trustworthy adaptive adversarial perturbations in social networks

Deep neural networks have achieved excellent performance in various research and applications, but they have proven to be susceptible to adversarial examples. Generating adversarial examples can help identify the vulnerability of the deep neural networks and further enhance the robustness and reliability of these models. However, the existing adversarial attacks can hardly achieve the balance between robustness and imperceptibility, which is not trustworthy in social networks. To solve these problems, we propose adaptive adversarial perturbation (AAP) to improve the universal robustness of the adversarial examples while ensuring imperceptibility. To optimize the imperceptibility of the perturbation, we design a noise visibility function (NVF) to reflect the features of the original images based on the human visual system (HVS). By further calculating a coefficient matrix based on the NVF, the perturbation intensity of different pixels can be adjusted dynamically to improve the robustness. The experimental results prove that the proposed method alleviates the trade-off between robustness and imperceptibility, and outperforms existing attack methods in both one-step and iterative ways. Our method makes the adversarial attack more reliable and applicable in social networks.
An interaction model applied to optimize photovoltaic farm location: A case study of China

Given the escalating environmental challenges and the looming carbon peaking and carbon neutrality goals, developing renewable energies becomes a global imperative. The pivotal initial stage involves site selection; however, this process has been plagued by the ambiguity of subjective decision-making and the inconsistency of multi-factor decisions. In this regard, this study proposes a new evaluation system which combines Geodetector and Interaction Linear Model to minimize subjective interventions and reveal the mechanisms by which interaction terms influence siting. China is selected to unveil the model's capability through solar PV site selection. The results indicate that compared with the Basic Linear Model, the Interaction Linear Model increased the estimation accuracy by 8%, with some interactive terms showing higher determinant power than single factors. Based on this interaction model, there is a total of 13% of China's land is developable for solar PV plants, consisting of 46% highly suitable areas (581,576 km2), 39% moderately suitable areas (492,615 km2), and 15% marginally suitable areas (198,088 km2). Finally, Xinjiang was identified as the most suitable place in China. Above all, the new siting system proposed here provides a new perspective on renewables' site selection, promoting the high-quality energy transition globally.
Timeframe of eastern Paleo-Tethys closure: Constraint on the Songpan-Ganzi Complex by big data-based multiproxy provenance analysis

The Songpan-Ganzi Complex (SGC) is one of the largest turbidite complexes on Earth. The source-to-sink processes of the SGC are closely linked to the closure of the Paleo-Jinsha Ocean in the eastern Paleo-Tethys realm, so the spatiotemporal transitions in SGC provenance provide the potential to constrain the closure time. A comprehensive multiproxy provenance investigation, integrating detrital zircon UPb geochronology, whole-rock SmNd isotope data, and paleocurrent analysis, shed light on the origin of the Triassic SGC turbidites. In this study, it was determined that a significant proportion (exceeding 70%) of the detrital material comprising the SGC turbidites was sourced from the northwestern margin of the Yangtze Craton and the Qinling Orogen. A comprehensive and quantitative provenance study revealed that the Songpan-Ganzi turbidites primarily originated from collisional orogenic belts at convergent continental plate margins and secondarily from magmatic arcs and other tectonic units surrounding the basin. In this study, it was demonstrated that the SGC contains important information on the convergence of Eurasia and the tectonic evolution of surrounding terranes. (1) The South Qinling Orogen experienced significant rapid exhumation during the Ladinian to early Carnian. (2) The closure of the Paleo-Jinsha Ocean most likely occurred during the transition period between the upper and lower members of the Zhuwo Formation (ca. 231 Ma, middle Carnian). (3) During the Late Triassic, the crust of the Yidun Terrane underwent significant vertical accretion, resulting in a topography exceeding that of the Jomda-Weixi arc by the late Norian.
SCTracker: Multi-Object Tracking With Shape and Confidence Constraints

Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this article proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an intersection of union (IoU) distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman filter to improve the track performance with the inaccurate detection result. Experimental results on the MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking when compared with the state-of-the-art methods.
Development of an OTDR-Based Hand Glove Optical Sensor for Sign Language Prediction

This article demonstrates a wearable optical fiber-based sensing glove for predicting hand gestures for sign language. The sensors were created using macrobends on single mode fiber (SMF) for each finger to simultaneously monitor the change in bending radius due to the change in position of each finger by creating different hand gestures. The glove sensing capability is based on the change in macrobending loss due to a change in bending radius. The system includes an optical time-domain reflectometer (OTDR), optical fiber, and hand glove. The functionality and performance of the glove were thoroughly evaluated on different participants for repeatability. We retrieved hand gesture information using data from each finger macrobend loss through the sensor’s response on OTDR—the developed glove acquired real-time raw data on alphabet and numerical hand postures. A set of unique features were extracted from the acquired raw data. The selected features were used to classify hand gestures using the ensemble classifier. The performance of this classifier was optimized using the training dataset (70%), and the performance of this trained algorithm was evaluated to recognize hand gestures using a testing dataset (30%). Compared to some existing methods, the proposed method enhances sign language recognition accuracy and improves tracking of hand gesture movement. This article presents statistical results with 93.57% classification accuracy on test data representing good dynamic gesture recognition scenarios.
MAD-MEL: Combining Entity and Metric Learning for Anomaly Detection in Multivariate Time Series

Detection of anomalies in multivariate time series (MVTS) data has been crucial for various application areas. Though recent approaches have addressed such issues, still the problem is challenging not only due to identifying anomalous behavior but also to reducing false alarms. The problem becomes more exciting and challenging when the model takes care of errors due to measurement, environmental effects, and temporal dependency among the observations. Toward this, we propose a framework MAD-MEL, an MVTS Anomaly Detection Method using metric and entity learning (EL) approaches. The metric learning-based model identifies the behavior of individual series as well as the relationship between different attributes, whereas the entity-based model learns the behavior of MVTS as a single entity. An empirical mode decomposition-based feature learning (FL) component is incorporated to learn more generalized behavior of data and to handle the problem of minor disturbances. Also, the proposed method combines a linear component to control the spatial scaling effect in the train and test set. The idea is to learn the normal behavior of MVTS by combining all the components as a prediction-based model and to use prediction error for anomaly detection. We adopt transfer learning to reduce the time complexity while learning the individual series in case of a large number of attributes or multiple instances of MVTS. Our proposed multivariate time series anomaly detection using metric and entity learning (MAD-MEL) achieves the F1-score of 94% on public dataset, which is an improvement of 3% from SOTA.
Intra-Inter Region Adaptive Graph Convolutional Networks for skeleton-based action recognition

To effectively capture the spatio-temporal dependencies of the skeletal data, graph convolution has been widely applied. However, it tends to emphasize the dependence relationship between adjacent joints and does not consider long-distance dependence relationships among joints. Another problem is single-structure temporal convolution, which is difficult to extract global temporal features. To address the above issues, we propose Intra-Inter Region Adaptive Graph Convolutional Networks (IIR-AGCN), which models the long-distance relationships of skeleton sequences at temporal and spatial scales. Our contributions are three-fold: First, to enhance global topological learning capabilities of graph convolution, we propose a regional-coupled attention module, which divides joint features into multiple sub-regions, and then constructs coupled relationships between intra-inter regions by self-attention mechanism, which realizes global joint information interaction. Second, to address the issue of difficulty in extracting global temporal features, we propose a pyramid multi-scale temporal module that extracts deep multi-scale temporal features and implements adaptive cross-scale feature fusion. Third, IIR-AGCN adopts a two-stream architecture, evaluating performances on two large-scale human skeleton datasets, NTU-RGB+D 60 and NTU-RGB+D 120, respectively.
Stabilized activation scale estimation for precise Post-Training Quantization

In recent years, there have been more and more studies on Post-Training Quantization (PTQ). Many outstanding works have emerged, which has greatly promoted the availability of PTQ methods. However, in terms of low-bit quantization, there is still a wide gap between PTQ and the current state-of-the-art Quantization-Aware Training (QAT) methods. In this work, we find that the current way of obtaining the activation scale is not completely reasonable in PTQ, which leads to the fact that the weight cannot be well adapted to the biased quantized activation during inference. Based on experiments and analysis, we propose a method called StablePTQ. It obtains a stable activation scale and mixes rich input into the block reconstruction to achieve an improving quantization accuracy. StablePTQ achieves remarkable improvements in several different bit quantization especially W2A4, and can be applied to other quantization algorithms as a plug-and-play approach. The source code of StablePTQ is avaiable at .
Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

In location-based social networks, calibrating a point-of-interest (POI) recommendation system is as important as its accuracy for improving user satisfaction. POI recommendation calibration is primarily classified as categorical or geographical calibration. Categorical calibration ensures that the recommended items are distributed proportionally among the past interest categories of the target user. When a target user checks 80 Chinese, 10 Japanese, and 10 French restaurants, a recommendation list with a ratio of 8:1:1 for Chinese, Japanese, and French restaurants can be reasonably expected. In addition to categorical calibration, geographical calibration has been proposed to increase user interest in the recommended results. Users have a high probability of revisiting locations in their subareas. Therefore, the POIs recommended in multiple subareas of interest are more suitable than those from one small and frequently visited subarea. However, improving the calibration and accuracy are conflicting tasks. To achieve high calibration while maintaining accuracy, previous studies proposed reranking-based techniques to rerank the candidate list and return POIs with high calibration. However, optimizing the calibration by reranking is independent of the basic-candidate-item generation model, resulting in a suboptimal system. To tackle the problem, we propose a novel sampling-based differentiation technique to merge the task of improving calibration into the GCN model training process and directly generate the final recommendation list. The model is flexible and can be applied to different domains, where a domain can be a subarea or category. In a three-layer GCN, the layer one represents the historical check-ins of the user, whereas layer three includes the candidate POIs from which the target user aggregates information. We trained the model to make the distribution of the POI domains at layer three approximated the distribution at layer one. Experimental results on Philadelphia and Tucson datasets confirmed that the proposed method outperforms all state-of-the-art GCN+ geo-reranking and GCN+ MCF baselines, improving Recall@ 5 from 0.0394 to 0.0412 (4.57%) and Jensen–Shannon measure (JS)@ 5 from 0.5931 to 0.6734 (13.54%) on the Philadelphia dataset and improving Recall@ 5 from 0.0495 to 0.0517 (4.40%) and JS@ 5 from 0.5869 to 0.6598 (12.42%) on the Tucson dataset for categorical calibration. The model was also tested in the geographical domain and a similar trend was observed.
Deep style transfer to deal with the domain shift problem on spheroid segmentation

Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumor behaviour, and for testing different preclinical and clinical treatments. Image analysis of spheroids is crucial to understand the formation and behaviour of such tumors, but when performed manually is a time-consuming task. Deep convolutional models can help in the analysis of spheroid images since they provide accurate results for the segmentation of tumors, a task that is necessary to further study features of spheroids. However, the images from tumor spheroids greatly vary depending on the experimental conditions, and also on the equipment (microscopes) and conditions (focus and magnification) employed to capture the images. This issue, known as domain shift, hinders the applicability of deep learning models for spheroid segmentation. In this work, we address this problem by studying style transfer methods based on both convolutional neural networks and generative adversarial networks to transfer the style from the images used for training segmentation models to the images from a new domain. This approach successfully handled the domain shift problem in the context of spheroid segmentation for 6 different deep learning models. Finally, we developed a high-level API that facilitates the of our approach to other contexts where the domain-shift problem might occur.
Consistency–exclusivity guided unsupervised multi-view feature selection

Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for multi-view data. It aims to obtain the important feature subset from multi-view data, which can significantly minimize the impacts of noises, outliers and redundancy. Although previous UMFS methods achieve remarkable achievements, they fail to fully take into account the consistency or the exclusivity hidden in multi-view data. To address this, this article presents a consistency–exclusivity guided unsupervised multi-view feature selection (CE-UMFS) method. Specifically, we design a multi-view matrix factorization model to simultaneously explore the consistency and exclusivity in multi-view data. Meanwhile, we employ the Hilbert–Schmidt independence criterion (HSIC) to preserve the exclusivity of different views. Furthermore, we impose a nuclear norm on the consistent representation matrix to explore the consistency across views. At last, promising experimental results demonstrate the superiority of the proposed method compared with some state-of-the-art methods.
Theoretical analysis of co-existing periodic orbits in sparse binary neural networks

A sparse binary neural network is a discrete-time recurrent neural network characterized by local binary connections and signum-type neuron models. The dynamics is described by an autonomous difference equation of binary state variables. Depending on the parameters, the network generates various periodic orbits of binary vectors. Multiple periodic orbits can co-exist and the network exhibits one of them depending on initial condition. Real/potential applications of the periodic orbits include control signals of switching circuits and basic signals for time-series approximation. The network is well suited for theoretical analysis and simple hardware implementation. This paper considers shift-type periodic orbits in the networks. As the main theoretical results, we clarify the number, period, and stability of the periodic orbits for key parameters. As a first step to the applications in the future, we present an FPGA-based hardware prototype and confirm typical periodic orbits experimentally.
GlocalFuse-Depth: Fusing transformers and CNNs for all-day self-supervised monocular depth estimation

In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieves remarkable results on standard benchmarks. However, most of existing methods only focus on either daytime or nighttime images, their performance degrades on the other domain because of the large gap between daytime and nighttime images. To address this problem, we propose a two-branch network named GlocalFuse-Depth for self-supervised depth estimation of all-day images in this paper. The daytime and nighttime images in input image pair are fed into the two branches: CNN branch and Transformer branch, respectively, where both local details and global dependency can be effectively captured. Besides, a novel fusion module is proposed to fuse multi-dimensional features from the two branches. Extensive experiments demonstrate that GlocalFuse-Depth achieves state-of-the-art results for all-day images of the Oxford RobotCar dataset, which proves the superiority of our method.
An efficient multi-order cascade distillation model for the detection of small targets and occluded objects in transmission line inspection

The cascade distillation detection model is proposed to address the problems of low accuracy of multiple small target defect detection and lack of robustness under complex environments in transmission line UAV intelligent inspection. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. Secondly, a high-order aggregated feature extraction backbone network is proposed to enhance the feature extraction of small and medium-sized targets and obscured targets on transmission lines. Then, a spatially deformable attention mechanism is designed to optimize the selection of sampling points for transmission line targets and defects and the calculation of their attention weights by aggregating contextual information in the vicinity of the targets, enhancing the module's ability to refine and adjust the bounding boxes. Finally, a large dataset of transmission line defect targets is constructed and generated, and the validity of the model is verified by experiments.
An educational simulation of a country’s electric power system

We have created an educational simulation of the electric power system of a country. The simulation can be used by high school or university students to get a hands-on experience with some of the complexities of meeting the demand for electrical power, while optimizing for cost and CO2 emission. The simulation parameters are primarily based on real historical data. The simulation is bundled with an instruction manual including background information and the tasks to be completed. The program was first tried out by the contestants of the Leó Szilárd Physics competition in Hungary. The contestants’ result and our experience with the program are also discussed.