Reviewerly/exHarmony huggingface dataset:
Particle Swarm Optimization Algorithm of Pellet Burden Cost Optimization Based on Penalty Function

Taking the lowest cost as the objective, considering the influence of the weight gain of iron oxide oxidation process, the burning loss of raw materials and the amount of desulfurization on the chemical composition and cost of pellet products, this paper constructs an optimization model of pellet proportioning, and gives the particle swarm optimization algorithm based on penalty function. This model can not only meet the chemical composition requirements of finished ore, but also reduce the cost effectively, and greatly reduce the time cost of calculation. Compared with the existing research results, it has higher calculation efficiency on the basis of ensuring the accuracy of calculation results.
Derivational and Inflectional Morphemes on the Thesis Abstracts

An analysis of derivational and inflectional morpheme has great values because sometimes, learners face difficulties in understanding those morphemes. Dealing with this problem, this study was aimed to describe the types of derivational and inflectional morphemes. As this study analyzed the written documents, this study was identified as library research. Subjects of this study were five alumni of the master of English education study program of STKIP PGRI Sidoarjo. Documentation and the researcher himself were recognized as the instruments of this study. The data of this study were all words attached by derivational or inflectional morpheme, and all those data were taken from students’ research abstracts. In analyzing the data, the researcher used content analysis. The results showed that the researcher found and discussed two types of derivational morpheme namely derivational prefix (“-un” and “-in”) and derivational suffix (“-ion”, “-al”, “-ance”, “-er”, “-ive”, “-ity”, “-able”, “-ary”, and “-ly”). However, the researcher found and discussed 5 types of inflectional morpheme covering: plural (-s), possessive (-s’ or ‘s), present (-s or -es), past (-ed), and present participle (-ing).
Edge-based Protection Against Malicious Poisoning for Distributed Federated Learning

Federated learning is proposed to solve data islands and protect privacy. Especially in the big data environment, participating users can build a model together without sharing private sensitive data. However, as the number of end devices becomes larger, and the model becomes more complex, high concurrent access to the cloud server often brings communication delay, and it is also a great challenge to the computing power of end devices. To address this problem, we introduce Unmanned Aerial Vehicle (UAV) swarms as mobile edge nodes for end devices. UAV swarms can provide caching and computing resources for end devices. Therefore, we can implement edge aggregation of parameters on UAV swarms to reduce direct access to the cloud server. Meanwhile, the distributed end-edge-cloud federated learning architecture based on UAV swarms is an open environment, which may have potential malicious end devices or external channel eavesdropping. Malicious end devices or external eavesdroppers may maliciously poison training data sets or model parameters to reduce the classification accuracy of the model. In order to resist malicious poisoning, on UAV swarms we can calculate the cosine similarities between local parameters and their edge aggregation parameters to exclude malicious parameters, which do not conform to the trend of collaborative convergence. Then, the reliable parameters can be aggregated again, and uploaded to the cloud server with Schnorr signature to ensure the authenticity of the data. We analyze the security of the proposed scheme, and verify through experiments that it can resist malicious poisoning effectively and improve the accuracy of the model.
A Novel Screening Framework for Lymph Node Metastasis in Colorectal Cancer Based on Deep Learning Approaches

As a diagnostic criterion for cancer, histopathology image analysis is quite critical for the subsequent therapeutic treatment of patients. Nowadays, the diagnosis is mainly depended on manually which is less precise and low-accuracy. To address the problem, we propose a novel screening framework combined image preprocess and AI approaches for the automatic detection of lymph node metastasis of colorectal cancer. First calculates the Histogram of Oriented Gradient (HOG) and Gray Level Cooccurrence Matrix (GLCM) of high-resolution digital images transformed from pathological sections. Statistical analysis show that Support Vector Machine (SVM) can be used to automatically identify cancerous areas. We further introduce deep learning models Convolutional Neural Network (CNN) into our framework, taking preprocessed images as inputs. The screening results demonstrate that the highest overlapping ratio can be achieved compared with manually annotation areas is 93.09% got by CNN, while another approaches SVM get an accuracy of 83.75%. The combination of image preprocess and deep learning can effectively improve the efficiency of lymph node metastasis screening in colorectal cancer and has great significance for the further development of Computer Aided Diagnosis (CAD) systems.
A Globe Calibration Method for Optical Multisensor in 3D Complex Surface Measurement System

There are few existing omnipotent sensors that handle a complex surface inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantage of their strengths. One key task is the extrinsic parameter calibration (global calibration) of the multiple sensors before measurement. This paper proposes a method of optimal extrinsic calibration for a structured light sensor (SLS) and conoscopic holography sensor (CHS). In adopting this method, a common planar calibration board is placed with different poses in front of the multisensory system, and the extrinsic calibration problem is solved through a three-dimensional reconstruction of the calibration board and using geometric constraints of the views from the SLS and CHS. This calibration method, which uses only the plane calibration board, is simple. Physical experiments demonstrate that the proposed method is robust and accurate in the calibration of multiple inhomogeneous optical sensors for the measurement of a complex surface.
Image Feature Matching Based on Improved AKAZE and I-SURF Algorithm

Aiming at the defect of AKAZE algorithm with low feature matching accuracy, this paper proposes an improved AKAZE and I-SURF algorithm to optimize the AKAZE feature matching. First, the composite conduction function according to the scale factors is used to nonlinear diffusion filtering. Then the improved SURF descriptors are calculated by second order derivative multi-scale template to obtain more high-quality matching point pairs, and the Best Bin First (BBF) method with high threshold is adopted to improve the number of matching point pairs during the matching. Finally, the MSAC algorithm is used to further remove redundant matching points. The experimental results show the superiority of proposed scheme compared with other classical algorithms in terms of matching accuracy, and computation efficiency, which can well possess the real-time performance and accuracy.
SITD: Insider Threat Detection Using Siamese Architecture on Imbalanced Data

In the insider threat detection domain, data imbalance is a well-known problem. Most existing solutions, including rebalancing datasets and anomaly detection, have problems such as model overfitting, high cost, and high False Positive Rate (FPR). Therefore, how to effectively detect insider threats on an imbalanced dataset is a challenge. This paper proposes a new Siamese-architecture Insider Threat Detection (SITD) method, which detects insider threat by judging whether the input sample pairs belong to the same category instead of directly classifying a sample while avoiding the abovementioned problems. In addition, we improve the contrastive loss function to make the model pay more attention to the samples pairs of different categories, which significantly enhances the detection performance. Experimental results show that SITD outperforms other insider detection methods on the imbalanced CERT dataset. Moreover, SITD can achieve a good result no matter how imbalanced the dataset is.
A PCA-aided EV-EGI Method for Registering Volumetric Datasets

A method for volumetric dataset registration that utilizes principal component analysis (PCA) and volumetric extended Gaussian image (EGI)-based processing is presented. The method uses PCA to determine an initial coarse estimate of orientation difference between two volumetric datasets. The PCA is based on certain automatically selected (i.e., significant) voxels. The coarse estimate then is refined by a three-stage process that utilizes enhanced volumetric extended Gaussian images (EV-EGIs). These final EV-EGI stages also provide the translational component. The method's combination of steps allows for faster processing at roughly similar accuracy versus prior work based solely on EV-EGIs. Experimental comparisons with Globally optimal Iterative Closest Pointset (Go-ICP) registration are also reported and analyzed.
Object Detection based on Light Field Imaging

Improving the object confidence score of an image using the machine intelligence method is one of the critical objectives for object detection. In this paper, we propose an end-to-end framework to object detection based on light field (LF) imaging. First, we apply refocusing technology to enhance the visual feature expression between different objects in LF images. then, we combine the LF refocusing technology with the efficient darknet53 feature extraction network and multi-feature fusion method, which ensures effectiveness in improving the confidence score of the object in the image. To evaluate the framework, we use a light field camera (LFC) to construct a new real LF image detection dataset, which consists of as many as 20 kinds of common objects (person, car, etc.). Compared with the popular methods, the confidence score achieved by our method shows an improvement for the detection of any single object. Specifically, for the detection of cars and people, the confidence score is increased by 0.03 and 0.02, respectively. For the detection of bicycles, the confidence score significantly improves by 0.27. To some extent our method can also solve the problem of object missed and false detection.
Emerging trends: General fine-tuning (<i>gft</i>)

Abstract This paper describes gft (general fine-tuning), a little language for deep nets, introduced at an ACL-2022 tutorial. gft makes deep nets accessible to a broad audience including non-programmers. It is standard practice in many fields to use statistics packages such as R. One should not need to know how to program in order to fit a regression or classification model and to use the model to make predictions for novel inputs. With gft , fine-tuning and inference are similar to fit and predict in regression and classification. gft demystifies deep nets; no one would suggest that regression-like methods are “intelligent.”
Automated Quantum Volume Test

Abstract As a benchmark for the overall performance of quantum computers, quantum volume has the advantage of being able to reflect the depth of running quantum circuits. But, the quantum volume test code provided by IBM needs to be executed manually, and the simulation result of the quantum simulator is used as the result of the volume test, so that users cannot quickly and accurately test the quantum volume of the actual quantum computer required. In response to this problem, this paper designs an automated quantum volume test program. The program automatically generates quantum volume sequences, selects the number of executions of quantum circuits, and defines real quantum computers to facilitate users to perform quantum volume tests on quantum computers provided by the IBM Quantum Cloud Platform. Simultaneously, according to the automated test program, the quantum volume of IBM’s four small superconducting quantum computers was tested. The test results show that (1) the quantum computer is different, and the qubit layout and execution times ntrials are the same, will cause the quantum volume is uncertain; (2) the same quantum computer, whether ntrials is the same, the robustness of qubit coupling will be affected to a certain extent.
FIK: Find Important Knobs for Permessioned Blockchain Hyperledger Fabric

Permissioned blockchain technology has a significant impact on many industries due to its security and scalability, which has many configurable knobs to control all aspects of it. Too many knobs lead to an exponential increase in the configurable knob combination, so it is vital to find a subset of knobs that affect the performance. This article proposes FIK (Find Important Knobs), which uses (1) Latin hypercube sampling technology to create a data set of knobs and performance. The data set includes more than 10000 samples of three different network architectures of the permissioned blockchain network and three different chaincode. (2) The relationship between knobs and performance is modeled based on the Xgboost method. The performance of this model is better than other selected machine learning models. (3) The SHAP-based method helps decision-makers explain the contribution of the knobs of the permissioned blockchain to performance.
Impact of fuzzy logic: a bibliometric view

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Transformer evaluation strategy based on improved machine learning

Abstract A transformer state assessment method based on an improved extreme learning machine is proposed in this paper, which introduces an adaptive evolution algorithm into the extreme learning machine. This method is used in the evaluation model to evaluate the state of the transformer. The algorithm is trained and tested through sample sets. Furthermore, the test results are analyzed to prove the feasibility of the proposed control strategy.
Design of Personalization Warehouse Management Platform Based on SaaS Model

SaaS is widely used in the fields of operation management, business process outsourcing, data analysis, and information security. However, with the improvement of the degree of information, the generalized SaaS platform is unable to satisfy the requirements of enterprise personalization. SaaS products face great challenges in personalized customization technology due to the application of multi-tenant architecture. The challenges include multi-tenant customization in SaaS model, data isolation during customization, and mapping of multi-tenant virtual warehouse locations to actual locations. Therefore, we decompose personalization technology into metadata driver, cloud data placement and mapping mechanism for research. We decompose the personalized technologies into metadata drive, cloud data placement, and mapping mechanism. In order to solve the problem that traditional personalization customization is unable to be applied in the SaaS field, we propose a multi-tenant personalized warehousing mode architecture, designed a personalization warehouse management platform based on SaaS model, and realized SaaS-based data isolation, interface customization, rapid positioning, and on-demand customization services. Experimental results show that the proposed multi-tenant personalized warehouse model can achieve data isolation and customization, and reflect the advantages of highly automated warehousing, shared storage resources, and on-demand customization in terms of warehouse management, data security, and user experience.
Платформа за взаимопомощ на учители и училища

This article presents the created Platform for mutual assistance of teachers and schools, which is accessible and free for all schools and pedagogical specialists in the country after registration. Opportunities, through the mutual aid platform, allow teachers to network and collaborate with other teachers from anywhere, anytime. The Platform for teachers and schools is updated every three months and includes an organizational response to the diversity and complexity of educational needs in the following areas: Innovative methods in the classroom, STEM education, Coaching, Leadership, Media, communications, PR and Skills of the 21st century. The mutual aid platform has been active since the beginning of 2021 and already has 2200 registered members - principals and teachers, leaders of the education system in Bulgaria; more than 160 lessons have been shared.
Intraoperative compensation of magnetic field distortions for fluoroscopic and electromagnetic hybrid navigation

Abstract Purpose Hybrid navigation is a promising technique which combines the benefits of optical or electromagnetic tracking (EMT) and fluoroscopy imaging. Unfortunately, the fluoroscopy system is a source of metallic distortion for the EMT system. In this work, we present a new method for intraoperative calibration and real-time compensation of dynamic field distortions. The method was tested in the presence of a fluoroscopy C-arm, and sub-millimetre errors were obtained after distortion correction. Methods A hybrid navigation scenario was created by combining the open-source electromagnetic tracking system Anser EMT and a commercial fluoroscopy C-arm. The electromagnetic field generator was placed directly on top of the X-ray collimator, which introduced significant field distortion. Magnetic sensors were placed at known positions to capture the magnetic distortion, and virtual magnetic dipole sources were used to model the distortion magnetic field. The accuracy of the compensated EMT model was tested on a grid of test points. Results Error reduction was demonstrated from 12.01 to 0.35 mm and from 25.03 to 0.49 mm, for horizontal and vertical sensor orientations, respectively, over a volume of 16 × 16 × 6 cm. It is proposed that such sub-millimetre tracking errors meet the needs of most endoscopic navigation tasks. Conclusions We describe a method to model a magnetic field in real time, based on redundant electromagnetic field measurements, and we apply it to compensate for the distortion introduced by a fluoroscopy C-arm. The main limitation of the approach is the requirement for a high number of sensors, with possible occlusion of the operative space. Solutions might come from miniaturisation and wireless sensing.
Demosaicking Algorithm Based on Accurate Edge-directed Interpolation

In this paper, we proposed a new demosaicking algorithm for imaging system based on accurate edge detection. Different from the conventional demosaicking method, the proposed method has two steps: first, using thresholding method, the geometric distribution of the local region is investigated to detect the interpolation direction; second, different interpolation methods are adopted to recover the missing color components according to the local region statistics; As compared with the latest demosaicking methods, experiments showed that the proposed algorithm provided outstanding performance in terms of both objective and subjective image qualities.
A Study on Pedagogical Support in Teacher

The present investigation was developed with the purpose of determining the pact that generates the virtual recycling classroom in the knowledge of the environment, in this regard we worked with a sample of 32 directors of educational institutions of special basic education in the region of Libertad Likewise, reliable and duly validated interview guide, questionnaire and observation guides were used for the collection of information processed thanks to the statistical software SPSS version 25, it was concluded virtual recycling classroom does generate a significant impact on knowledge of the environment in The special basic education modality, due to the Pearson and Durbin Watson correlation coefficient, showed positive values ​​of 0.826 and 1.039 respectively and a significance of less than 5% (p &lt;0.05).
EfficientFCOS: An Efficient One-stage Object Detection Model based on FCOS

Object detection is playing an important role in computer vision. With the rapid development of deep learning, object detection algorithms based on convolutional neural networks have been successful. However, deep learning requires a large amount of data to train, it is crucial to improve model efficiency with the same required hardware resources. In this paper, we propose an efficient one-stage object detection model, EfficientFCOS based on pixel-level prediction, which can improve the accuracy and efficiency of existing one-stage object detection network model. In particular, EfficientFCOS first uses EfficientNet to extract the input image features, which has fewer parameters and better performance than ResNet. Next, it employs the scaling approaches to uniformly scale number of channels of the model’s backbone network, feature fusion network and shared head network according to the resolution of the image. Furthermore, it adopts the weighted bidirectional feature pyramid network to achieve fast and multi-scale feature fusion. Meanwhile, it integrates the geometric factors (center point distance, overlap rate and scale) to the regression loss of target prediction, such that the regression of the target prediction box becomes more stable. Our experimental results indicate that EfficientFCOS is more efficient than the existing FCOS. On the Pascal Voc data set, EfficientFCOS achieves 82.1 for the mAP value, which increases 2.8%. It has 15M network parameters, which is 4.3x smaller than FCOS. Meanwhile, it improves the GPU LAT and CPU LAT by 12.2% and 20.0% respectively.
A TDF-WNSP-WLFM algorithm for product recommendation based on multiple types of implicit user behavior

E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.
Participation Experience of Ural Federal University Department of Human Resource Management and Psychology’s Students in Career Guidance Work Using Social Networks in the Conditions of the COVID-19 Pandemic

The article analyzes the practice of the Ural Federal University’s Department of Human resource management and Psychology’s students on career guidance in 2020 and 2021 in the conditions of the COVID-19 pandemic. The relevance of this study is determined by generational peculiarities, the need for career guidance, distance learning and new formats of educational work with students. In the process of writing the following sources were analyzed: reports by undergraduate students, majoring in human resource management, on the implementation of 15 online career guidance projects in social networks. Based on the data obtained, some opportunities for career guidance work, which take into account the peculiarities of the epidemiological situation, were revealed. The authors' proposals are to create additional functions and tools for vocational guidance of schoolchildren and retention of applicants, as well as to offer effective ways of informing potential students and their parents about the human resource management training in new conditions.