Reviewerly/exHarmony huggingface dataset:
Efficient and Accurate Cloud-Assisted Medical Pre-Diagnosis With Privacy Preservation

The emergence of cloud computing enables various healthcare institutions to outsource pre-diagnostic models and provide timely and convenient services for patients. However, healthcare institutions and patients have serious concerns about potential privacy leakage as cloud servers cannot be fully trusted. In this paper, a privacy-preserving cloud-assisted medical pre-diagnosis scheme, named NAIAD, is proposed, where patients can securely query the outsourced model and obtain their pre-diagnostic results. Specifically, the pre-diagnostic model is constructed on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN), and Mahalanobis Distance (MD) is chosen as the similarity metric to achieve high accuracy. Accordingly, a secure MD-based comparison method (SMDC) is designed based on a matrix encryption technique. The method is a basic module of NAIAD that enables cloud servers to compare encrypted medical records and achieve privacy-preserving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN-based pre-diagnosis with linear complexity. To further improve the computational efficiency, medical records are first clustered and encrypted to construct a hierarchical index tree, then patients can query the tree to speed up the query process. Detailed security analysis indicates NAIAD can resist closeness-same-pattern chosen-plaintext attack, and extensive experiments on real-world and synthetic databases demonstrate NAIAD has high query efficiency and pre-diagnosis accuracy.
Concept Drift Detection and Update Algorithm Based on Online Restricted Boltzmann Machine

In this paper, we address a concept drift detection and update algorithm based on the online restricted Boltzmann machine (O-RBM). We introduce an attention mechanism into RBM, and the parameters of each classifier in the concept drift detection model are updated according to the important information mined by the attention mechanism. The updated model complies with the various data better and judges the types and states of new data online. In the experiments, we compare the performance of the proposed algorithm with the algorithm proposed in our previous work, and the results show that O-RBM plays even better in concept drift detection.
Construction and Analysis of Scientific Research Knowledge Graph in the Field of Hydrogen Energy Technology

In order to effectively reveal the various scientific research entities and semantic relationships among them in the field of hydrogen energy technology, the top-down construction method was used to explore the construction process of the scientific research knowledge graph in the field of hydrogen energy technology. Ontology was used to construct the schema layer of the knowledge graph, after knowledge extraction and knowledge fusion in the data layer, the knowledge was stored in the Neo4j graph database. The knowledge graph constructed included 345300 entities of 8 types and 2167484 entity relationships of 12 types. Through the construction of the knowledge graph, the complex knowledge system visual analysis of various scientific research entities and their relationships can be effectively realized, it can provide support for researchers to grasp the whole research situation in the field of hydrogen energy technology.
Facial recognition for partially occluded faces

Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.
Boosting and bagging classification for computer science journal

In recent years, data processing has become an issue across all disciplines. Good data processing can provide decision-making recommendations. Data processing is covered in academic data processing publications, including those in computer science. This topic has grown over the past three years, demonstrating that data processing is expanding and diversifying, and there is a great deal of interest in this area of study. Within the journal, groupings (quartiles) indicate the journal's influence on other similar studies. SCImago provides this category. There are four quartiles, with the highest quartile being 1 and the lowest being 4. There are, however, numerous differences in class quartiles, with different quartile values for the same journal in different disciplines. Therefore, a method of categorization is provided to solve this issue. Classification is a machine-learning technique that groups data based on the supplied label class. Ensemble Boosting and Bagging with Decision Tree (DT) and Gaussian Nave Bayes (GNB) were utilized in this study. Several modifications were made to the ensemble algorithm's depth and estimator settings to examine the influence of adding values on the resultant precision. In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. Based on the average value of the accuracy results, it is known that the best algorithm for computer science datasets is GNB Bagging, with values of 68.96%, 70.99%, and 69.05%. Second-place XGBDT has 67.75% accuracy, 67.69% precision, and 67.83 recall. The DT Bagging method placed third with 67.31 percent recall, 68.13 percent precision, and 67.30 percent accuracy. The fourth sequence is the XGBoost GNB approach, which has an accuracy of 67.07%, a precision of 68.85%, and a recall of 67.18%. The Adaboost DT technique ranks in the fifth position with an accuracy of 63.65%, a precision of 64.21 %, and a recall of 63.63 %. Adaboost GNB is the least efficient algorithm for this dataset since it only achieves 43.19 % accuracy, 48.14 % precision, and 43.2% recall. The results are still quite far from the ideal. Hence the proposed method for journal quartile inequality issues is not advised.
Interactive Robot Learning: An Overview

How do people teach robots tasks? Here, we focus on main methods and models enabling humans to teach embodied social agents such as social robots, using natural interaction. Humans guide the learning process of such agents by providing various teaching signals, which could take the form of feedback, demonstrations and instructions. This overview describes how human teaching strategies are incorporated within machine learning models. We detail the approaches by providing definitions, technical descriptions, examples and discussions on limitations. We also address natural human biases during teaching. We then present applications such as interactive task learning, robot behavior learning and socially assistive robotics. Finally, we discuss research opportunities and challenges of interactive robot learning.
COMPARATIVE STUDY OF VARIOUS OBJECT DETECTION MODELS

Today object detection is a rapidly evolving technology in the field machine learning.One of the main goals in the deep learning discipline is computer vision.A method for determining whether an object is present in an image or video is called object detection.To increase task efficiency, object detection can be applied in a variety of contexts."Object detection has numerous uses, including in home automation, self-driving automobiles, population counting, military defense systems, sports, industrial activities, robots, and the aviation industry."Various methods, including R-CNN, MDSSD, RefineNet, YOLO, SPP-Net, and ResNet, can be used for object detection.These algorithms are compared, and the outcomes and performances of each are examined.The conclusion reached in this paper is to use Detectron2 (RCNN) model for our further implementation.
PAPNet: Convolutional network for pancreatic cyst segmentation

BACKGROUND: Automatic segmentation of the pancreas and its tumor region is a prerequisite for computer-aided diagnosis. OBJECTIVE: In this study, we focus on the segmentation of pancreatic cysts in abdominal computed tomography (CT) scan, which is challenging and has the clinical auxiliary diagnostic significance due to the variability of location and shape of pancreatic cysts. METHODS: We propose a convolutional neural network architecture for segmentation of pancreatic cysts, which is called pyramid attention and pooling on convolutional neural network (PAPNet). In PAPNet, we propose a new atrous pyramid attention module to extract high-level features at different scales, and a spatial pyramid pooling module to fuse contextual spatial information, which effectively improves the segmentation performance. RESULTS: The model was trained and tested using 1,346 CT slice images obtained from 107 patients with the pathologically confirmed pancreatic cancer. The mean dice similarity coefficient (DSC) and mean Jaccard index (JI) achieved using the 5-fold cross-validation method are 84.53% and 75.81%, respectively. CONCLUSIONS: The experimental results demonstrate that the proposed new method in this study enables to achieve effective results of pancreatic cyst segmentation.
Discovering AI-enabled convergences based on BERT and topic network

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times.To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas.In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database.The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI.The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications.Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences.Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing.These findings are expected to have policy implications for facilitating the convergences in diverse industries.Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.
Information Retrieval Method of Natural Resources Data based on Hash Algorithm

In order to improve the ability of searching and identifying information in natural resources data, this paper puts forward a method of searching information in natural resources data based on hash algorithm.Through the data center technology, the problems of information source positioning, data directory organization, data semantic definition and expression, and data entity relationship construction in natural resource data center are solved.Combined with the distribution of resource data stream, the information structure reorganization and data encryption in natural resource data center are realized by using hash algorithm, and the parameters of information quality control model in natural resource data center are established.Through natural resource data governance and semantic reconstruction, the characteristics detection and redundancy arrangement of information data in natural resource data center are realized by standardizing data collection rules, and they are stored in the intermediate database.Through data governance rules, the information in the natural resources data is structured and managed, and stored in the publishing library.Through all kinds of data processing tools, all kinds of data are processed, cleaned and reconstructed, and through Hash algorithm and data aggregation processing, information detection in natural resources data is realized.The simulation results show that the precision rate of natural resource data retrieval by this method is high.
Efficient FPGA-Based Implementation of Image Segmentation Algorithms for IoT Applications

The technique of segmenting a picture involves breaking it up into its component parts, such as boundaries and regions. Image edges are found via Edge detection algorithms. Edges of the picture are detected when the pixels’ intensities abruptly shift. Sobel, Prewitt, and Robert are three well-known gradient-based edge detection algorithms that operate on the 3-by-3 and 2-by-2 kernels in the X and Y axis. Python and Verilog HDL for synthesis on the Artix 7-based Basys 3 dev kit are used to perform in two separate phases. The image data is first converted to grayscale for the Python implementation before being passed through the gradient-based edge detection (Sobel, Prewitt, and Robert) operator. The edges of the entire image are then determined using an edge detection technique. The resultant picture is then examined using various input parameters including PSNR, MSE. The input grayscale image is passed via line buffers, which store the pixels for subsequent processing by the operators, in the second stage, which is Verilog implementation. Output buffer is then filled based on the output from the convolution operator. The kernels of 3 × 3 or 2 × 2 for the convolution are pre-defined based on the operator.
Escope: An Energy Efficiency Simulator for Internet Data Centers

Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers.
Optimization of parameter measurement precision with precoupling-assisted weak-value amplification

The weak-value amplification is extensively considered in precision metrology in order to achieve a higher sensitivity. Despite the practical benefits in amplifying small physical quantities, its metrological advantage still arouses a broad debate due to the low postselection probability of success. In this paper, by employing the quantum Fisher information metric, we show that the precision of estimating an unknown parameter can be improved by introducing a precoupling process with properly chosen interaction operators. We point out this result is credible for both real and imaginary weak values. By tracking the meter wave functions, we find this enhancement of estimation precision comes from a precoupling induced modulation of the meter wave function, thus the most sensitive regime with respect to the parameter is reached. In addition, the estimation error is investigated by considering the difference between the theoretical and estimated value. The analysis suggests that this kind of error can be effectively suppressed by averaging the estimations resulted from different initial meter states. These results are finally illustrated by an exact numerical simulation where the advantages of our proposal are displayed by the comparison to the standard weak-value amplification scheme.
Relating EPR steering with the fidelity of quantum teleportation for two- and three-qubit states

Abstract Einstein–Podolsky–Rosen (EPR) steering is an important quantum resource in quantum information and computation. The relationships between EPR steering and the fidelity of quantum teleportation for two- and three-qubit states are investigated in this paper. For two-qubit states, the upper bound of the average teleportation fidelity based on the EPR steering is derived. When the receiver can only perform the identity or the Pauli rotation operations, the two-qubit X-type states which violate the three-setting measurements steering inequality could be used for teleportation. In the end, the steering observables and the average teleportation fidelities of two-qubit reduced states for three-qubit pure states maintain the same ordering. The complementary relations between the steering observables and the average teleportation fidelities for three-qubit pure states are also established.
A study of attention information from transformer layers in hybrid medical image segmentation networks

Transformer models have recently started gaining popularity in Computer Vision related tasks. Within Medical Image Segmentation, segmentation models such as TransUNet have incorporated transformer blocks alongside convolutional blocks while remaining faithful to the popular U-Net architecture. The present work utilizes attention maps to examines information flow within transformer blocks of three such segmentation models: (i) TransUNet, (ii) 2D CATS, and (iii) 2D UNETR. Based on the attention maps, compressed versions of these models are proposed which retain only as many transformer layers as are necessary for the model to achieve a global receptive field. The parameter saving is more than 60% whereas the dice metric does not drop by more than 5% compared to the original (uncompressed) model.
Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods

The inherent intermittency and uncertainty of photovoltaic (PV) power generation impede the development of grid-connected PV systems. Accurately forecasting PV output power is an effective way to address this problem. A hybrid forecasting model that combines the clustering of a trained self-organizing map (SOM) network and an optimized kernel extreme learning machine (KELM) method to improve the accuracy of short-term PV power generation forecasting are proposed. First, pure SOM is employed to complete the initial partitions of the training dataset; then the fuzzy c-means (FCM) algorithm is used to cluster the trained SOM network and the Davies-Bouldin index (DBI) is utilized to determine the optimal size of clusters, simultaneously. Finally, in each data partition, the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression (MR) using least squares to complete coefficient evaluation to establish a regional MR model. The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014. Compared with other single global models, the root mean square errors (RMSEs) of the proposed regional KELM model are reduced by 52.06% in plant 1, 54.56% in plant 2, and 51.43% in plant 3 on average. Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models. In addition, the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods. The forecasts of different methods in different seasons revealed the strong robustness of the proposed method. In four seasons, the MAEs and RMSEs of the proposed SF-KELM are generally the smallest. Moreover, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> value exceeds 0.9, which is the closest to 1.
Research on Methods of Expressway Vehicle Detection under Abnormal Weather Conditions Based on Deep Learning

Aiming at the issue that vehicle detection accuracy is easily influenced by abnormal weather conditions such as rain, snow, and frog, et al. This paper studies the methods of expressway vehicle detection based on deep learning. First, methods for detecting expressway vehicles based on Faster-RCNN, YOLOV3, and SSD are compared and analyzed. Then, based on SEU expressway vehicle detection dataset under abnormal weather conditions, the training of the vehicle detection model and the research of compare experiments on Faster-RCNN, YOLOv3, and SSD are carried out by manually labeling and collecting specific regions of vehicles. Theoretical analysis and experimental results show that the YOLOv3-based detection model of expressway vehicle detection under abnormal weather conditions outperforms the other two methods, with an average accuracy of 99.2%.