Reviewerly/exHarmony huggingface dataset:
A Fast and Accurate Method of Power Line Intelligent Inspection Based on Edge Computing

The combination of power intelligent inspection and edge computing plays a crucial role in the construction of power Internet of Things and transparent power grid. However, as a consequence of the low computing power of edge devices, the detection speed of the model running on edge devices tends to be slow. Accordingly, a fast and accurate method of power line edge intelligent inspection is proposed in this article. First, for key component detection of power lines, RepVGG, diverse branch block (DBB), efficient channel attention (ECA), and improved spatial pyramid pooling (SPP) are utilized to improve YOLOv5, giving rise to RepYOLO. In addition, to solve the problem of low accuracy in pin defect detection, a two-stage cascaded method is proposed. The first stage localizes various connection fittings in the input image, and the second stage recognizes normal pins and missing pins in these connection fittings. Finally, <inline-formula> <tex-math notation="LaTeX">$\text{C}++$ </tex-math></inline-formula> language combined with TensorRT is employed to optimize and accelerate the model on the NVIDIA Jetson Xavier NX embedded platform, fulfilling efficient power line edge intelligent inspection. Experimental results show that the TensorRT optimized RepYOLO algorithm is four times the inference speed of YOLOv5 with a 1.2&#x0025; increase in accuracy. Moreover, TensorRT optimized two-stage cascaded method can achieve accurate and real-time pin defect detection on edge devices.
Knowledge graph-based multi-context-aware recommendation algorithm

Because a knowledge graph’s huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user’s preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN’s superiority over eight state-of-the-art baselines.
Characteristics of solar-irradiance spectra from measurements, modeling, and theoretical approach

An accurate solar-irradiance spectrum is needed as an input to any planetary atmosphere or climate model. Depending on the spectral characteristics of the chosen model, uncertainties in the irradiance may introduce significant differences in atmospheric and climate predictions. This is why several solar spectral-irradiance data sets have been published during the last decade. They have been obtained by different methods: either measurements from a single instrument or a composite of different spectra, or they are theoretical or semi-empirical solar models. In this paper, these spectral datasets will be compared in terms of irradiance, power per spectral interval, their derived solar-atmosphere brightness temperature, and time series. Whatever the different sources of these spectra are, they generally agree to within their quoted accuracy. The solar-rotation effect simultaneously observed by SORCE and PREMOS-PICARD is accurately measured. The 11-year long-term variability remains a difficult task, given the weak activity of solar cycle 24 and long-term instrument aging.
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding

Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings. However, most existing approaches utilize the position embeddings to incorporate the sequence information, neglecting the noisy improper reading order obtained by OCR tools. In this paper, we propose a robust layout-aware multimodal network named XYLayoutLM to capture and leverage rich layout information from proper reading orders produced by our Augmented XY Cut. Moreover, a Dilated Conditional Position Encoding module is proposed to deal with the input sequence of variable lengths, and it additionally extracts local layout information from both textual and vi-sual modalities while generating position embeddings. Experiment results show that our XYLayoutLM achieves competitive results on document understanding tasks.
Fast and simple constant-time hashing to the BLS12-381 elliptic curve

Pairing-friendly elliptic curves in the Barreto-Lynn-Scott family are seeing a resurgence in popularity because of the recent result of Kim and Barbulescu that improves attacks against other pairing-friendly curve families. One particular Barreto-Lynn-Scott curve, called BLS12-381, is the locus of significant development and deployment effort, especially in blockchain applications. This effort has sparked interest in using the BLS12-381 curve for BLS signatures, which requires hashing to one of the groups of the bilinear pairing defined by BLS12-381.While there is a substantial body of literature on the problem of hashing to elliptic curves, much of this work does not apply to Barreto-Lynn-Scott curves. Moreover, the work that does apply has the unfortunate property that fast implementations are complex, while simple implementations are slow.In this work, we address these issues. First, we show a straightforward way of adapting the “simplified SWU” map of Brier et al. to BLS12-381. Second, we describe optimizations to this map that both simplify its implementation and improve its performance; these optimizations may be of interest in other contexts. Third, we implement and evaluate. We find that our work yields constant-time hash functions that are simple to implement, yet perform within 9% of the fastest, non–constant-time alternatives, which require much more complex implementations.
Geological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network

Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine-learning (ML)-based methods are understandably worse than manual visual interpretation. Additionally, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep-learning (DL)-feature-based adaptive multi-source data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, wherein the atrous spatial pyramid pooling operation and an attention block are applied to adaptively extract and fuse multi-scale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by more than 2% in the experiment section. As far as we know, this research work is the first time DL features and multi-source remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pretraining can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision with modern versions of residual networks and the largest-ever dataset of images and corresponding hashtags. We study the performance of the resulting models in various transfer-learning settings including zero-shot transfer. We also compare our models with those obtained via large-scale self-supervised learning. We find our weakly-supervised models to be very competitive across all settings, and find they substantially outperform their self-supervised counterparts. We also include an investigation into whether our models learned potentially troubling associations or stereotypes. Overall, our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems. Our models, Supervised Weakly through hashtAGs (SWAG), are available publicly.
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion

A well-known challenge in applying deep-learning methods to omnidirectional images is spherical distortion. In dense regression tasks such as depth estimation, where structural details are required, using a vanilla CNN layer on the distorted 360 image results in undesired information loss. In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue. Our pipeline transforms a 360 image into less-distorted perspective patches (i.e. tangent images) to obtain patch-wise predictions via CNN, and then merge the patch-wise results for final output. To handle the discrepancy between patch-wise predictions which is a major issue affecting the merging quality, we propose a new framework with the following key components. First, we propose a geometry-aware feature fusion mechanism that combines 3D geometric features with 2D image features to compensate for the patch-wise discrepancy. Second, we employ the self-attention-based transformer architecture to conduct a global aggregation of patch-wise information, which further improves the consistency. Last, we introduce an iterative depth refinement mechanism, to further refine the estimated depth based on the more accurate geometric features. Experiments show that our method greatly mitigates the distortion issue, and achieves state-of-the-art performances on several 360 monocular depth estimation benchmark datasets. Our code is available at https://github.com/yuyanli0831/OmniFusion.
Anomaly detection based on weighted fuzzy-rough density

The density-based method is a more widely used anomaly detection. However, most of the existing density-based methods mainly focus on dealing with certainty data and do not consider the problem of uncertainty and fuzziness of the data. Fuzzy rough set theory, as an important mathematical model of granular computing, provides an effective method for information processing of uncertain data. For this reason, this paper proposes an anomaly detection based on fuzzy-rough density. First, the fuzzy-rough density is defined to describe the degree of aggregation of objects. Then, fuzzy entropy is introduced to compute the weights of each attribute. Further, an anomaly score is constructed to characterize the anomaly degree of the samples, which takes into account both the density and fuzziness of the samples. Finally, extensive experiments are conducted on publicly available data with nine popular detection methods. The experimental results show that the proposed method achieves better performance on three types of datasets.
A Fast Hyperchaotic Image Encryption Scheme

In this paper, we propose a fast hyperchaotic image encryption scheme based on RSVM and step-by-step scrambling-diffusion. In this scheme, we firstly propose a new algorithm named ring shrinkage with variable modulo (RSVM), which can randomly scramble the elements in a one-dimensional array, which are composed of the row numbers or column numbers of the pixel matrix to be encrypted. Before encryption, we use RSVM algorithm to generate two random one-dimensional arrays of pixel matrix (i.e. row array [Formula: see text] and column array [Formula: see text]), and each element in the [Formula: see text] represents the row/column number in the pixel matrix. Then the rows/columns of the pixel matrix are scrambled-diffused step-by-step according to the row/column numbers in the [Formula: see text]. The initial control parameters of RSVM algorithm are controlled by SHA-256 of plaintext pixels, and RSVM algorithm controls the step-by-step scrambling-diffusion process of pixel matrix, rows and columns, so the small changes of plaintext pixels will lead to great differences in ciphertext images. In addition, the overall time complexity of the image encryption scheme is only [Formula: see text], which can greatly reduce the time cost. Finally, the experimental results and extensive security analysis prove the efficiency and feasibility of this image encryption method.
Twitter Geolocation and Regional Classification via Sparse Coding

We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.
Effect of Polarization Dependent Loss on the Quality of Transmitted Polarization Entanglement

Quantum networking brings together several diverse research areas, such as fiber-optic communication, quantum optics, and quantum information, to achieve capabilities in security, secret sharing, and authentication which are unavailable classically.The development of practical fiber-based quantum networks requires an understanding of the reach, rates, and quality of the entanglement of distributed quantum states.Here, we present a theoretical model describing how the magnitude and orientation of polarization dependent loss (PDL), a common impairment in fiber-optic networks, affects the entanglement quality of distributed quantum states.Furthermore, we theoretically characterize how PDL in one fiber channel can be optimally applied in order to nonlocally compensate for the PDL present in another channel.We present experimental results that verify our theoretical model.
Stochastic Prediction of Multi-Agent Interactions from Partial Observations

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.
Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.
The Role of Flagship Universities in a Region: Transformation Models

Efforts in providing expert and methodological support for the implementation of flagship university development programs in 2016–2017 yielded a specific-purpose flagship university model and four generic flagship university transformation models: regional technology leader (RTL), regional comprehensive university (RCU), industry sector leader (industrial university) (IL), and trans-border region university (TBRU). The article provides distinctive features of the four models, analysis of the regions where specific types of models pre­vail, and the results of model testing.&#x0D; As it has been found, flagship universities basically develop along two gene­ric models, RCU (classical universities, nearly half of the project participants) and RTL (engineering universities, one third of the flagship universities). For most universities, the type of transformation model pursued is strongly related to their current status and external environment characteristics. However, a number of universities fall in between and cannot be classified neatly under any particular model due to some specific external and internal factors. In this case, universities may use elements of more than one transformation model at once, yet the choice of model should first of all be based on the regional factors that determine the position and role of the flagship university in terms of the priority areas of regional development.
Crypto Assets Require Better Regulation: Statement of the Financial Economists Roundtable on Crypto Assets

The exponential rise and volatility in the price of Bitcoin has heightened investor interest in cryptocurrencies and crypto assets. These assets have attracted a growing number of investors but also have been used to facilitate a wide range of illicit activities. In some cases, legitimate participants in crypto asset markets have incurred substantial losses. In response, the Financial Economists Roundtable discussed the potential benefits and risks associated with crypto asset markets and agreed that financial regulators need to be more proactive in addressing abusive activities.
Privacy-enhancing ETL-processes for biomedical data

Modern data-driven approaches to medical research require patient-level information at comprehensive depth and breadth. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform. Privacy-protection needs careful consideration when data is pooled or re-used for secondary purposes, and data anonymization is an important protection mechanism. However, common ETL environments do not support anonymization, and common anonymization tools cannot easily be integrated into ETL workflows. The objective of the work described in this article was to bridge this gap. Our main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and its schematic properties (e.g. data types), (3) to implement a method which is easy to understand and intuitive to configure, and (4) to provide high scalability. We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. By combining different instances into a single ETL process, data can be protected from multiple threats. The plugin supports very large datasets by leveraging the streaming-based processing model of the underlying platform. We present results of an extensive experimental evaluation and discuss successful applications. Our work shows that expert-level anonymization methodologies can be integrated into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools.
Toward Subjective Violence Detection in Videos

Violence detection in videos aims to identify whether a violent action occurred within a video stream. Effective tools for intelligent video analysis are highly demanded, specially to determine violence in video streams. Such solution could have applications in detecting inappropriate behaviors in video feeds, aiding law-enforcement in forensic cases, protecting children from accessing inappropriate online content and helping parents making informed decisions about what their kids should watch. Prior art on violence detection, particularly recently proposed deep learning based ones, seeks to identify violence in videos as a whole, without considering breaking down the subject into some of its underlying concepts. In this paper, we explore a different methodology of violence detection, which relies upon two deep neural network (DNNs) frameworks to learn spatial-temporal information on video clips under different scenarios - subjective- and conceptual-based. We leverage deep feature representations for each specific concept, and aggregate them by training a shallow neural network as a binary-classification problem to describe violence as a whole. Finally, we show that using more specific concepts is an intuitive and effective solution, besides being complementary to form a more robust definition of violence.