Reviewerly/exHarmony huggingface dataset:
Analysis of Hotspots and Trends in Digitalization Research of Chinese Archives Based on Bibliometrics

This paper aims to reveal the current status of research development, research hotspots and trends, and possible future research directions in the field of archival digitization research in China. This paper focuses on the field of archival digitization research in China, using 1267 relevant literature articles retrieved from the China National Knowledge Infrastructure (CNKI) database between 1995 and 2022 as the research object. The study employs a bibliometric analysis and knowledge graph analysis and visualizes and analyzes its research content, authors, and institutions using software such as CiteSpace and VOSviewer. The study summarizes the research hotspots and development trends in the field of archival digitization. The results indicate that interdisciplinary research is a clear trend in current archival digitization research. Institutional construction, management mode, management technology, and archival form are this field’s four main thematic directions. Finally, based on the current research hotspots in China’s archival digitization, the future research and development direction of the field is discussed.
Improved multiple sclerosis diagnosis with advanced deep learning techniques

Abstract White matter pathologies analysis is important for segmenting multiple sclerosis (MS) lesions in MRI slices. Myelin damage affects motor function, sensory conflicts, paralysis, and vision. Extracting an optimistic number of hidden characteristic features induces high feature maps and reduces redundancy and dimensionality. A framework based on Hybrid Deep Convolutional Neural Networks (HD‐CNN) is presented to classify MS based on the acquired optimistic feature characteristic. First, Chaotic Leader‐Selective Particle Swarm Optimization uses the pixel intensity ratio of the targeted region to extract the most hidden white matter features. Second, Refined Slime Mold categorizes hidden features and ensures that the necessary features are selected effectively for a better segmentation result. An effective inbuilt Maximum Bidirectional Gradient Classifier accurately classifies MS lesions by detecting white matter spots in the target region. Finally, we will evaluate the proposed HD‐CNN framework using the industry‐standard University Medical Centre Ljubljana (UMCL) and international symposium on biomedical imaging (ISBI) 2015 datasets as benchmarks. It reaches 98.56%, 93.15%, and 98.44% accuracy for FLAIR, T1W, and T2W, respectively. Our HD‐CNN improves MS classification over other state‐of‐the‐art techniques, compared with accuracy, F‐score, Recall, Precision, Dice, and Jaccard indexes values.
Fast vehicle detection based on colored point cloud with bird’s eye view representation

RGB cameras and LiDAR are crucial sensors for autonomous vehicles that provide complementary information for accurate detection. Recent early-level fusion-based approaches, flourishing LiDAR data with camera features, may not accomplish promising performance ascribable to the immense difference between two modalities. This paper presents a simple and effective vehicle detection method based on an early-fusion strategy, unified 2D BEV grids, and feature fusion. The proposed method first eliminates many null point clouds through cor-calibration. It augments point cloud data by color information to generate 7D colored point cloud, and unifies augmented data into 2D BEV grids. The colored BEV maps can then be fed to any 2D convolution network. A peculiar Feature Fusion (2F) detection module is utilized to extract multiple scale features from BEV images. Experiments on the KITTI public benchmark and Nuscenes dataset show that fusing RGB image with point cloud rather than raw point cloud can lead to better detection accuracy. Besides, the inference time of the proposed method reaches 0.05 s/frame thanks to its simple and compact architecture.
Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering

Spammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be deleted, which can hamper users’ productivity and security. In this paper, a hybrid deep neural network architecture is suggested to address this problem. It is based on the convolution neural network (CNN), which has been enhanced with the convolutional block attention module (CBAM). Initially, CNN enhanced with CBAM is used to extract the most crucial information from each image-based e-mail. Then, the generated feature vectors are fed to the support vector machine (SVM) model to classify them as either spam or ham. Four datasets—including Image Spam Hunter (ISH), Annadatha, Chavda Approach 1, and Chavda Approach 2—are used in the experiments. The obtained results demonstrated that in terms of accuracy, our model exceeds the existing state-of-the-art methods.
An Improved Method for Photovoltaic Forecasting Model Training Based on Similarity

Photovoltaic (PV) power generation is the most widely adopted renewable energy source. However, its inherent unpredictability poses considerable challenges to the management of power grids. To address the arduous and time-consuming training process of PV prediction models, which has been a major focus of prior research, an improved approach for PV prediction based on neighboring days is proposed in this study. This approach is specifically designed to handle the preprocessing of training datasets by leveraging the results of a similarity analysis of PV power generation. Experimental results demonstrate that this method can significantly reduce the training time of models without sacrificing prediction accuracy, and can be effectively applied in both ensemble and deep learning approaches.
Adapting Static and Contextual Representations for Policy Gradient-Based Summarization

Considering the ever-growing volume of electronic documents made available in our daily lives, the need for an efficient tool to capture their gist increases as well. Automatic text summarization, which is a process of shortening long text and extracting valuable information, has been of great interest for decades. Due to the difficulties of semantic understanding and the requirement of large training data, the development of this research field is still challenging and worth investigating. In this paper, we propose an automated text summarization approach with the adaptation of static and contextual representations based on an extractive approach to address the research gaps. To better obtain the semantic expression of the given text, we explore the combination of static embeddings from GloVe (Global Vectors) and the contextual embeddings from BERT (Bidirectional Encoder Representations from Transformer) and GPT (Generative Pre-trained Transformer) based models. In order to reduce human annotation costs, we employ policy gradient reinforcement learning to perform unsupervised training. We conduct empirical studies on the public dataset, Gigaword. The experimental results show that our approach achieves promising performance and is competitive with various state-of-the-art approaches.
Improving Compressed Video Using Single Lightweight Model with Temporal Fusion Module

Video compression algorithms are commonly used to reduce the number of bits required to represent a video with a high compression ratio. However, this can result in the loss of content details and visual artifacts that affect the overall quality of the video. We propose a learning-based restoration method to address this issue, which can handle varying degrees of compression artifacts with a single model by predicting the difference between the original and compressed video frames to restore video quality. To achieve this, we adopted a recursive neural network model with dilated convolution, which increases the receptive field of the model while keeping the number of parameters low, making it suitable for deployment on a variety of hardware devices. We also designed a temporal fusion module and integrated the color channels into the objective function. This enables the model to analyze temporal correlation and repair chromaticity artifacts. Despite handling color channels, and unlike other methods that have to train a different model for each quantization parameter (QP), the number of parameters in our lightweight model is kept to only about 269 k, requiring only about one-twelfth of the parameters used by other methods. Our model applied to the HEVC test model (HM) improves the compressed video quality by an average of 0.18 dB of BD-PSNR and -5.06% of BD-BR.
Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

The testing of the foundation pile is an important means to ensure the quality of the foundation pile in the construction process, and the low-strain pile test is one of the most commonly used testing technologies. However, in order to ensure that the testing signal is effective and reliable, it is necessary to provide the preliminary judgment results when acquiring the testing signal in the field. In this paper, we propose a data classification method for low-strain pile testing data using a recurrent neural network as the core. In this method, after identification, tailoring, and normalization, the input feature vector with a sequential structure is sent into this model. The model ensures the efficient use of data values while considering the sequential relationship among the data. At last, we designed and produced one complete model pile and six asymmetric model piles, which can form thirteen kinds of testing signals. The optimal application model was selected by the 10-fold cross verification method, and the influence of increasing the input feature dimension on the accuracy was discussed. Finally, compared with the other two methods, this model has the highest accuracy, at 98.46%, but it requires more training parameters and a longer training time.
A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Abstract In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.
Signal Communication for Collaborative Robot Behavior Control

Robots are increasingly required to work in close cooperation with humans and other robots, performing common tasks jointly, in collaboration. High-level decision making by an autonomous agent with such interactions require specific means of responding to the situation. This paper describes the implementation of the control system for a collaborative robot, based on the emotion-need architecture that provides reliable and interpretable interaction means of the robot with other agents. The robot is an autonomous device whose behavior is aimed at satisfying its current needs. The communication between the operator and the robot is based on signal communication: the operator’s signal ("command") activates an element of the emotion-need scheme — a gateway that implements the emotional feedback. Since the gateway connects the sensors and behavioral procedures, its activation starts the corresponding procedure, as if the sensory system had registered a real stimulus. The external signal of such indirect control can be represented in acoustic form or as the operator’s posture. The needs of the robot are aimed at both its physical survival (danger avoidance) and maintenance of the social connections (accompanying friendly agents). The robot recognizes external commands and activates the gateway associated with the highest priority action. The activity also gradually fades, which allows actions to be performed for some time even in the absence of an appropriate internal stimulus. The resulting robot is able to perform the simplest patrol tasks together with a human, acting in a predictable and understandable (interpretable) way for the human.
Detection of potential hydrocarbon accumulations at Qaret El-Soda area, Western Desert, Egypt, based on airborne geophysical survey data

Airborne spectral gamma-ray survey data were processed using Th-normalization technique for oil and gas exploration in the Qaret El-Soda area, Western Desert of Egypt. This technique was applied to suppress the effects of surface lithology, which are the main factors influencing the variation of radioelement content in rocks. Normalization of K and U by thorium yielded residual potassium and residual uranium estimates. Possible occurrences of new hydrocarbon microseepages were determined by mapping low values of residual potassium and high values of residual uranium relative to potassium, which are indicated as DRAD values, which were obtained by subtracting residual potassium from residual uranium values (eUresid – Kresid). Lower residual values of K, which were associated with higher DRAD anomaly values, highlight areas of prospective hydrocarbon accumulations. The obtained results from quantitative analysis and interpretation of aeromagnetic data show sufficiently thick sediments, probably suitable for the accumulation of hydrocarbons. This means that the study area may possess a potential for hydrocarbon exploration if supported by other detailed geophysical and geochemical exploration techniques.
Forward modeling and inverse estimation for nonlinear filtering

Abstract The highly accurate state estimation of nonlinear systems is needed in many different application fields. In many existing nonlinear filters, the uncompensated nonlinear approximation error and widely used numerical sampling methods both seriously affect the estimation accuracy and numerical stability. In this paper, based on the variational Bayesian framework, a novel iterative nonlinear filter consisting of forward modeling and inverse estimation is proposed. In the forward modeling stage, a linear Gaussian regression process with distributed variational parameters (VPs) is designed to fit the whole nonlinear measurement likelihood. The distributed VPs consider both first and second moments information, so as to compensate for approximation error from the whole likelihood perspective, instead of only the nonlinear function approximation, which can further improve the accuracy of Bayesian posterior update. In the linear Gaussian regression process, the original nonlinear measurement function (NMF) is just viewed as a prior and no longer directly participates in the modeling of the measurement likelihood. Hence, in the inverse estimation stage, when maximizing the evidence lower bound to update the posteriors of the state and VPs, we can obtain closed‐form solutions without numerical sampling methods to approximate the integral of NMF required in many existing methods, which can guarantee numerical stability. Moreover, to avoid the random setting of hyperparameters and to block the propagation of the accumulated error, we propose two rules, estimation and lower bound consistencies, to conduct the hyperparameters' initialization at the beginning of iteration at each sampling time and also derive an optimization method to update hyperparameters during iteration. Finally, the performance of our proposed method is demonstrated in the nonlinear orbital estimation problems.
Research data management services in academic libraries: a comparative study of South Asia and Southeast Asia

Purpose This study aims to understand the research data management (RDM) services offered by academic libraries in South Asian and Southeast Asian countries. This study aims to evaluate the library and information science professionals’ required RDM skills and the challenges faced with providing RDM services. Design/methodology/approach The research methodology for this study used a survey method with purposive sampling. Data were collected through online structured questionnaires, which were used to examine the current state of RDM services offered in academic libraries in South Asia and Southeast Asia. Findings South Asian and Southeast Asian region major types of RDM services provided were data repository, data management training, maintaining Web resources, data study and analysis, and promoting awareness of reusable data sources. Little attention was given to advisory services on data analysis/mining/visualization and supporting reproducibility and workflow transparency. The results indicated that most respondents agreed that metadata standards and data management planning skills were required for RDM services in South Asia and Southeast Asia. Originality/value This study is significant because it offers a comprehensive assessment of ongoing RDM services in academic libraries of South Asia and Southeast Asia. Most current literature focuses on best practices in developed nations. This study highlights the need for more competent and dedicated academic staff for effective RDM services. Library professionals can use this study to identify the gaps in RDM services and suggest formative measures to overcome such challenges.
MER: Modular Element Randomization for robust generalizable policy in deep reinforcement learning

Improving the generalization ability of reinforcement learning (RL) agents is an open and challenging problem and has gradually received attention in recent years. Previous work attempts to partially solve this problem by recombining several learned policies to achieve compositional generalization. However, these methods are usually limited to tasks that are composed of fixed subtasks with no task-agnostic environment change. In this paper, we propose a more flexible method termed MER to learn a compositionally generalizable policy that depends on task-dependent invariant elements among tasks, instead of depending on fixed subtasks. As a result, our learned policy can overcome the task-agnostic change in the environment and generalize to more different compositional tasks. Theoretical analysis and experimental results show that our method outperforms traditional methods and exhibits superior generalization ability in unseen new tasks.
Object Detection and Tracking: Exploring a Deep Learning Approach and Other Techniques

Object detection and tracking have a wide range of uses, for example, in security and surveillance systems to deter and investigate crimes, for traffic monitoring, and for communication through video sharing. In a smart city, data is continuously being obtained through various means. In terms of video data, the data collected through cameras and other digital devices need to be analyzed to derive useful information from it. Hence, the concept of object detection and tracking comes into play. This chapter looks into developing various frameworks for object detection and tracking in the context of video data. We will be working with a database of 1,939 pencil images. These images will be used to train a neural network that performs image classification tasks. Various object detection methods are implemented such as Convolution Neural Network (CNN) image classification, Canny edge detection, object detection using the Haar Cascade classifier, and background subtraction. The experiments are carried out with Python programming language, TensorFlow, and OpenCV library.&lt;br&gt;
Compressed Video-Based Classification for Efficient Video Analytics

Videos have become a crucial part of human life nowadays and share a large proportion of internet traffic. Various video-based platforms govern the mass consumption of videos through analytics-based filtering and recommendations. Various video-based platforms govern their mass consumption by analytics-based filtering and recommendations. Video analytics is used to provide the most relevant responses to our searches, block inappropriate content, and disseminate videos to the relevant community. Traditionally, for video content-based analytics, a video is first decoded to a large raw format on the server and then fed to an analytics engine for metadata generation. These metadata are then stored and used for analytic purposes. This requires the analytics server to perform both decoding and analytics computation. Hence, analytics will be fast and efficient, if performed over the compressed format of the videos as it reduces the decoding stress over the analytics server. This field of object and action detection from compressed formats is still emerging and needs further exploration for its applications in various practical domains. Deep learning has already emerged as a de facto for compression, classification, detection, and analytics. The proposed model comprises a lightweight deep learning-based video compression-cum classification architecture, which classifies the objects from the compressed videos into 39 classes with an average accuracy of 0.67. The compression architecture comprises three sub-networks i.e. frame and flows autoencoders with motion extension network to reproduce the compressed frames. These compressed frames are then fed to the classification network. As the whole network is designed incrementally, the separate results of the compression network are also presented to illustrate the visual performance of the network as the classification results are directly dependent on the quality of frames reconstructed by the compression network. This model presents a potential network and results can be improved by the addition of various optimization networks.&lt;br&gt;