Reviewerly/exHarmony huggingface dataset:
A Joint Spatiotemporal Video Compression Based on Stochastic Adaptive Fourier Decomposition

This paper proposes a novel video compression method &#x2013; stochastic adaptive Fourier decomposition (SAFD) based joint spatiotemporal model (JSTM). SAFD is a recently developed sparse representation theory that combines the traditional signal decomposition method with machine learning to adaptively decompose multi-signals into common atoms of predefined interpretable Szeg<inline-formula><tex-math notation="LaTeX">$\ddot{\text{o}}$</tex-math></inline-formula> kernel dictionary. Based on SAFD, this paper firstly proposes a learning-based 3D video to 2D data embedding method. The method learns common atoms based on the frames themselves that require to be compressed, without the need for pre-training on large-scale data like deep learning. Then the embedding-based JSTM and a feasible video compression architecture are developed. The contribution is twofold: introducing SAFD into video compression first time in the literature and developing a new video compression model JSTM. The experimental results are promising.
An Efficient Identity Authentication Scheme With Provable Security and Anonymity for Mobile Edge Computing

As a novel communication architecture, mobile edge computing (MEC) not only satisfies customers’ needs in real-time data processing and analysis, but also alleviates the inherent limitations in cloud computing. However, the MEC inherits some security and privacy challenges from cloud computing, most of the existing security solutions in the literature cannot meet the requirements of increasing user experience. To further address the aforementioned issues, in this article, an efficient identity authenticated protocol with provable security and anonymity is created for the MEC. The proposed protocol ensures that users remain anonymous during communication, and its security can be rigorously proved based on the classic complexity assumptions in the random oracle model. The experiment results demonstrate that our scheme enjoys relatively fewer overheads in computation costs. Therefore, our protocol is more suitable for efficient application requirement of identity authentication in the MEC.
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning

Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks.However, most existing methods can only learn from aligned image-caption data and rely heavily on expensive regional features, which greatly limits their scalability and performance.In this paper, we propose an end-to-end unifiedmodal pre-training framework, namely UNIMO-2, for joint learning on both aligned image-caption data and unaligned image-only and text-only corpus.We build a unified Transformer model to jointly learn visual representations, textual representations and semantic alignment between images and texts.In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.The experiments show that our grounded learning method can improve textual and visual semantic alignment for improving performance on various cross-modal tasks.Moreover, benefiting from effective joint modeling of different types of corpora, our model also achieves impressive performance on single-modal visual and textual tasks.
Market-Based Resource Allocation of Distributed Cloud Computing Services: Virtual Energy Storage Systems

The cloud-based application is a major developing feature of smart grids. Apart from centralized Internet data centers (IDCs), distributed cloud resources (CRs) can also provide cloud computing services with low latency and high reliability. For the distributed cloud computing, CRs aggregators (CRAs) will integrate the distributed idle computing resources, which are dispersed energy consumers in the system, to form virtual IDCs. This article presents a market-based computing resource allocation method for distributed cloud computing services. The computing resource allocation refers to the approach to allocating the computing workloads to different CRs. First, the batch workload scheduling (BWS)-based virtual energy storage system (VESS) model and thermal inertia (TI)-based VESS model are proposed to help CRAs better aggregate the distributed CRs and characterize the energy consumption flexibility of the virtual IDCs. Then, the energy trading behavior of the CRAs in the transactive energy market is modeled in the resource allocation process. Case studies are conducted on a 55-bus electricity system. It can be found that energy consumption costs can be reduced by applying the proposed methodology, and revenues from providing cloud computing services can be increased.
Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Vehicle counting is important for smart city applications such as logistics management, traffic estimation, and financial analysis. To perform vehicle counting using aerial images, researchers have proposed many algorithms, including detection-based, regression-based and density-based methods. However, most of these algorithms are only applicable to high-resolution images, which require clear vehicle outlines. For the reasons of acquisition difficulty, frequency and cost, it is necessary to explore methods for vehicle counting using low-resolution or even very low-resolution images. We build a cross-resolution vehicle counting (CRVC) dataset, including 192 very low-resolution images and 8 high-resolution images of a port from 2016 to 2019. For this task, we propose a novel vehicle counting via cross-resolution spatial consistency and intra-resolution time continuity constraints. The segmentation map is first obtained by semantic segmentation with the prior information above. The vehicle coverage rate relative to the located parking lot is calculated and then converted to vehicle area. Finally, the relationship between the area and the number of vehicles is established by regression. Experiments show that the vehicle counting results obtained by our method are highly consistent with the annotations and outperform other state-of-the-art methods. Our method is also applicable for images with a lower resolution of 10m and other locations. Code, data and pre-trained models are available online at https://github.com/hbsszq/Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images.
Type-Driven Multi-Turn Corrections for Grammatical Error Correction

Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors.In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference.Previous studies mainly focus on the data augmentation approach to combat the exposure bias, which suffers from two drawbacks.First, they simply mix additionallyconstructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections.Second, they ignore the interdependence between different types of corrections.In this paper, we propose a Type-Driven Multi-Turn Corrections approach for GEC.Using this approach, from each training instance, we additionally construct multiple training instances, each of which involves the correction of a specific type of errors.Then, we use these additionally-constructed training instances and the original one to train the model in turn.By doing so, our model is trained to not only correct errors progressively, but also exploit the interdependence between different types of errors for better performance.Experimental results and in-depth analysis show that our approach significantly benefits the model training.Particularly, our enhanced model achieves state-of-the-art single-model performance on English GEC benchmarks.
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization

Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models.However, a standing limitation of these models is that they are trained against limited references and with plain maximum-likelihood objectives.As for many other generative tasks, reinforcement learning (RL) offers the potential to improve the training of MDS models; yet, it requires a carefully-designed reward that can ensure appropriate leverage of both the reference summaries and the input documents.For this reason, in this paper we propose fine-tuning an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents.To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and unbiased, and we fine-tune the baseline in a few-shot style for both stability and computational efficiency.Experimental results over the Multi-News and WCEP MDS datasets show significant improvements of up to +0.95 pp average ROUGE score and +3.17 pp METEOR score over the baseline, and competitive results with the literature.In addition, they show that the coverage of the input documents is increased, and evenly across all documents.
DuReadervis: A : A Chinese Dataset for Open-domain Document Visual Question Answering

Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e.g., web pages, PDFs, or Word documents) as the information source.However, designing different text extraction approaches is time-consuming and not scalable.In order to reduce human cost and improve the scalability of QA systems, we propose and study an Open-domain Document Visual Question Answering (Open-domain DocVQA) task, which requires answering questions based on a collection of document images directly instead of only document texts, utilizing layouts and visual features additionally.To advance this task, we introduce the first Chinese Open-domain DocVQA dataset called DuReader vis , containing about 15K question-answering pairs and 158K document images from the Baidu search engine.There are three main challenges in DuReader vis :(1) long document understanding, (2) noisy texts, and (3) multi-span answer extraction.The extensive experiments demonstrate that the dataset is challenging.Additionally, we propose a simple approach that incorporates the layout and visual features, and the experimental results show the effectiveness of the proposed approach.The dataset and code will be publicly available at
A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm

Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.
Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure

Temporal knowledge graph (TKG) reasoning that infers missing facts in the future is an essential and challenging task. When predicting a future event, there must be a narrative evolutionary process composed of closely related historical facts to support the event's occurrence, namely fact precursors. However, most existing models employ a sequential reasoning process in an auto-regressive manner, which cannot capture precursor information. This paper proposes a novel auto-encoder architecture that introduces a relation-aware graph attention layer into transformer (rGalT) to accommodate inference over the TKG. Specifically, we first calculate the correlation between historical and predicted facts through multiple attention mechanisms along intra-graph and inter-graph dimensions, then constitute these mutually related facts into diverse fact segments. Next, we borrow the translation generation idea to decode in parallel the precursor information associated with the given query, which enables our model to infer future unknown facts by progressively generating graph structures. Experimental results on four benchmark datasets demonstrate that our model outperforms other state-of-the-art methods, and precursor identiﬁcation provides supporting evidence for prediction.
An EMD-Based Adaptive Client Selection Algorithm for Federated Learning in Heterogeneous Data Scenarios

Federated learning is a distributed machine learning framework that enables distributed nodes with computation and storage capabilities to train a global model while keeping distributed-stored data locally. This process can promote the efficiency of modeling while preserving data privacy. Therefore, federated learning can be widely applied in distributed conjoint analysis scenarios, such as smart plant protection systems, in which widely networked IoT devices are used to monitor the critical data of plant production to improve crop production. However, the data collected by different IoT devices can be dependent and identically distributed (non-IID), causing the challenge of statistical heterogeneity. Studies have also shown that statistical heterogeneity can lead to a marked decline in the efficiency of federated learning, making it challenging to apply in practice. To promote the efficiency of federated learning in statistical heterogeneity scenarios, an adaptive client selection algorithm for federated learning in statistical heterogeneous scenarios called ACSFed is proposed in this paper. ACSFed can dynamically calculate the possibility of clients being selected to train the model for each communication round based on their local statistical heterogeneity and previous training performance instead of randomly selected clients, and clients with heavier statistical heterogeneity or bad training performance would be more likely selected to participate in the later training. This client selection strategy can enable the federated model to learn the global statistical knowledge faster and thereby promote the convergence of the federated model. Multiple experiments on public benchmark datasets demonstrate these improvements in the efficiency of the models in heterogeneous settings.
Technical University Teacher Competences in Russia and Abroad

“Education 4.0” should be consistent with the Fourth Industrial Revolution. The reforms of engineering education as its component require new competences of technical university teachers who are responsible for these reforms and as a result, trigger the improvement of further teachers’ professional development programs to train them for that. Such a program is also one of the aims of the project “Effective training model of technical discipline lecturers for the purpose of obtaining “International Educator of Engineering University” certificate – “ING-PAED IGIP”. It is realized by Moscow Automobile and Road Construction State Technical University (MADI) that was recognized as a Federal Innovative Platform in 2020. The project is aimed at designing an additional program intended to develop technical university teachers’ competences that are, on the one hand, relevant to the needs of Russian higher school, and, on the other hand, correspond to the requirements for applying for the title «ING-PAED IGIP» of the International Society for Engineering Pedagogy (IGIP). In order to achieve that it is necessary to identify if technical university teachers’ competences that are relevant in Russia correspond the competences determined in IGIP. In order to solve this problem, the authors undertook comparative analysis of the competencies registered in two documents: RF Professional Standard “Pedagogue of professional teaching, professional education and additional professional education” and the Fourth IGIP Prototype Curriculum. The paper specifies the notion of technical university teacher competence for the research, validates the possibility of the two documents comparison, describes the procedure of comparative analysis on the basis of compiling summary tables. The paper also gives the main conclusions of the comparative analysis. It proves that RF Professional Standard covers all the competences required by Fourth IGIP Prototype Curriculum. RF Professional Standard registers teachers’ actions more distinctly, consistently, and with more details. The research revealed the competences relevant for Russian higher education, but not necessary for IGIP. The research also allowed to formulate preliminary recommendations for designing additional teachers’ professional development program in the MADI project.
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification

In recent years, NLP has advanced greatly along with the proliferation of pre-trained language models.The pre-trained language models are also properly adapted to downstream tasks when there is sufficient labeled data.However, in real-world applications, we often encounter the deficiency of labeled data.When only given a few instances for a new task, extracting task-aware features from a pre-trained language model regardless of the adaptation is a promising alternative.In the study, we propose a novel embedding transfer method, called LEA, for leveraging pre-trained language models with even only few-shot instances.LEA derives meta-level attention aspects using our new meta-learning framework.We evaluate our method on five text classification benchmark datasets.The results show that the novel method robustly provides the competitive performance compared to recent few-shot learning methods.
Comparison of Principal-Component-Analysis-Based Extreme Learning Machine Models for Boiler Output Forecasting

In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA–ELM are compared. In both the ELM and PCA–ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA–ELM proved as a promising technique in forecasting with reduced errors and reduced time.
Improved circuit implementation of the HHL algorithm and its simulations on QISKIT

In 2019, Yonghae Lee et al. combined the circuit implementation of the Harrow-Hassidim-Lloyd (HHL) algorithm with a classical computer, and designed a hybrid HHL algorithm to reduce experimental errors caused by decoherence and so on. However, the improvement is achieved only in the auxiliary quantum coding phase, and no quantum resource reduction is done on the quantum phase estimation and inverse quantum phase estimation stages. At the same time, the circuit improvement illustration on a [Formula: see text] linear system just has the result and no specific process. In this paper, based on the idea of the hybrid HHL algorithm and a generic circuit of HHL algorithm, an improved circuit implementation of the HHL algorithm is proposed. The feasibility of the improved circuit implementation of the HHL algorithm is verified by IBM's qiskit. The improved circuit illustrations on a [Formula: see text] linear system show that the improved circuit implementation of the HHL algorithm can effectively reduce quantum resources without losing the fidelity of the results. Thus the improved circuit implementation of the HHL algorithm can further avoid some result errors than the existing implementation methods.
Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

The changing landscape of power grids with distributed energy sources and power electronic devices has led to increasing relay maloperations.Wide area backup protection is necessary for the resolution of faults and for a reliable power grid.This paper presents detecting and classifying faults in transmission lines for wide-area backup protection using phasor measurement units (PMU) data.The faults are detected and classified using a Weighted Extreme Learning Machine (WELM) algorithm, which considers the variable distribution of data among the different classes using a weighted approach.The PMU signal data used were generated by the simulation of an IEEE 39 bus test system in the PowerWorld/OpenPDC/MATLAB environment.For classification, the input features data were derived using a wavelet transform-based ensemble feature extraction technique, and the WELM classifier was optimized using Particle Swarm Optimization (PSO).The PSO optimized WELM (PSO-WELM) model trained on PMU data detected faults with 100% accuracy and classified them into different types with an accuracy of 99.85%.It is validated that the PSO-WELM outperforms other known classifiers on performance comparison.The model also classified noisy data with a signal-to-noise ratio (SNR) as low as 10 dB and with an accuracy of 97%.