Reviewerly/exHarmony huggingface dataset:
Anomaly Detection and Segmentation Based on Defect Repaired Image Resynthesis

Anomaly detection is a challenging task in data analysis, especially when it comes to unsupervised pixel-level segmentation of anomalies in images. In this paper, we present a novel multi-stage defect repaired image resynthesis framework for the detection and segmentation of anomalies in images. In contrast to the existing reconstruction-based approaches, our reconstruction is free from artifacts caused by defective regions so that the defects can be identified from the residual map between input samples and their resynthesized defect-eliminated outputs. Our method outperforms the state-of-art benchmarks in most categories using the publicly available MVTec dataset. Besides, the method also demonstrates an excellent capability of repairing defects in abnormal samples.
Foreign language as a means of educating a professional

Rethinking the goals of vocational education, integrating into the international professional community, required rethinking of the academic disciplines role, which primarily affected the foreign language. Researchers note its importance for successful professional activity, for the development of special qualities. At the same time, the problem of educating professional responsibility, conditioned by the outlook of the individual, the conviction of the need to make professional decisions with an orientation towards their consequences for society and the country as a whole, has ceased to be a priority. The article focuses on the means of implementing the educational function of a foreign language in the unity of the content (context of the language) and methods of comparison with regional natural history, which activates creative thinking and affects the social position in the perception of the modern worldview, on the formation of a person's motivational-value attitude to his native culture. Comparative analysis of the native and foreign language culture allows a deeper understanding of the native culture and the formation of patriotic beliefs. The conclusions obtained in the course of understanding the educational function of a foreign language are based on the results of a comprehensive study conducted by graduate students of Samara University on the theory and methodology of vocational education, the purpose of which was to determine the effective personal characteristics of students in different areas of vocational training.
Breast cancer diagnosis using SE-DNI model

Breast cancer has long been a life-threatening disease, and the application of digital pathological detection systems on breast cancer detection, which employs convolutional neural networks (CNN), is a milestone in the medical field. For medical testing, higher accuracy means a stronger possibility of saving ones’ life. However, the accuracy of the current study on mammography image classification is not satisfying enough. In this paper, we simplified and improved the current network structure's performance on classifying histopathological images by proposing a new Squeeze and Excitation network DenseNet based improved model (SE-DNI), which is a hierarchical multi-stage process that consists of six layers. We tested three different DenseNet models for feature extraction, and our improved model of DenseNet201 with attention layer (SENet) has the highest test score among all three models we tested, which achieved an accuracy of 99.82% compared with 96.15%, the accuracy of the network that uses a normal DenseNet201 model.
There is No Need for Trusted Third Party Data Aggregation Schemes for Privacy Protection in the Grid

The location privacy of users is very important in V2G network, but users are easy to expose their location when sharing data. In this paper, data privacy and location privacy of users in smart grid communication and V2G network communication are studied. Based on traditional data aggregation and data sharing schemes, a series of solutions are proposed in terms of functionality, security and performance cost
How Learners Sketch Data Stories

Learning data storytelling involves a complex web of skills. Professional and academic educational offerings typically focus on the computational literacies required, but professionals in the field employ many non-technical methods; sketching by hand on paper is a common practice. This paper introduces and classifies a corpus of 101 data sketches produced by participants as part of a guided learning activity in informal and formal settings. We manually code each sketch against 12 metrics related to visual encodings, representations, and story structure. We find evidence for preferential use of positional and shape-based encodings, frequent use of symbolic and textual representations, and a high prevalence of stories comparing subsets of data. These findings contribute to our understanding of how learners sketch with data. This case study can inform tool design for learners, and help create educational programs that introduce novices to sketching practices used by experts.
Local Path Planning Algorithm for UGV Based on Improved Covariance Matrix Adaptive Evolution Strategy

Local path planning algorithm is one of the key technologies for unmanned ground vehicle(UGV). In order to reduce the computational complexity of the local path planning algorithm and ensure the real-time performance of the algorithm, a non-uniform column grid lines modeling method is introduced, and on this basis, a modeling method for planning paths is proposed. Aiming at the path planning problem in a multi -obstacle environment, the evaluation function of the path is constructed from four aspects, and the covariance matrix adaptive evolution strategy(CMA-ES) is used to solve the nonlinear optimization problem. In order to further reduce the amount of calculation, the CMA-ES algorithm is improved by the dynamic adjustment strategy of population size. Experiment results show that the path planning algorithm can effectively realize the local path planning in complex environment.
MINING OF DRUGS REACTIONS IN PATIENTS WITH MALARIA USING APRIORI ALGORITHM

Data mining refers to the entire process of extracting useful and novel patterns or models form large data sets. With the widespread use of medical information systems that include databases, which have recently featured explosive growth in their sizes, physicians and medical researchers are faced with a problem of making use of the stored data.
Numerical Simulation Analysis of Highway Tunnel Excavation Based on Artificial Intelligence Algorithm

In the numerical simulation of highway tunnel excavation, it is difficult to calculate the yield criterion of rock and soil in traditional calculation, which leads to the lack of individual parameters in the final numerical simulation results, resulting in the low overall stability of tunnel deep foundation section, rebound rate of deep foundation pit bottom and limit equilibrium rate of supporting pile. Therefore, a numerical simulation analysis method of highway tunnel excavation based on artificial intelligence algorithm is proposed. Firstly, the damage stage method is used to confirm the surrounding rock pressure parameters. After confirming the stability of the surrounding rock, the seismic factors of the highway tunnel are considered to determine the buried depth of the tunnel. The yield function is used to calculate the yield criterion of geotechnical materials. Finally, the artificial intelligence algorithm is used for numerical simulation. The simulation results show that the overall stability of the tunnel deep foundation section, the rebound rate of the deep foundation pit bottom and the limit equilibrium rate of the supporting pile are high.
“I’ll be back”: Examining Restored Accounts On Twitter

Online social networks like Twitter actively monitor their platform to identify accounts that go against their rules. Twitter enforces account level moderation, i.e. suspension of a Twitter account in severe cases of platform abuse. A point of note is that these suspensions are sometimes temporary and even incorrect. Twitter provides a redressal mechanism to ‘restore’ suspended accounts. We refer to all suspended accounts who later have their suspension reversed as ‘restored accounts’. In this paper, we release the first-ever dataset and methodology 1 to identify restored accounts. We inspect account properties and tweets of these restored accounts to get key insights into the effects of suspension. We build a prediction model to classify an account into normal, suspended or restored. We use SHAP values to interpret this model and identify important features. SHAP (SHapley Additive exPlanations) is a method to explain individual predictions. We show that profile features like date of account creation and the ratio of retweets to total tweets are more important than content-based features like sentiment scores and Ekman emotion scores when it comes to classification of an account as normal, suspended or restored. We investigate restored accounts further in the pre-suspension and post-restoration phases. We see that the number of tweets per account drop by 53.95% in the post-restoration phase, signifying less ‘spammy’ behaviour after reversal of suspension. However, there was no substantial difference in the content of the tweets posted in the pre-suspension and post-restoration phases.
Performance Comparison of Turkish Web Pages Classification

Nowadays., web page classification is essential for efficient and fast search engines. There is an ever-increasing need for automatic classification techniques with higher classification accuracy. In this article., a performance comparison of existing Turkish language CNN models for web pages classification systems is performed. In more detail., the content of web pages is extracted first., then preprocessing steps that aim to detect the important parts and eliminate useless contents are used. Next., Bert word embedding is integrated to represent the texts by efficient numerical vectors. Finally., three state-of-the-art CNN models that fully support the Turkish language are investigated to find the best classifier. Overall., the three studied models obtained an acceptable performance while classifying the Turkish webpages., however., the third model was able to achieve slightly better than the other two models.
Depot medených predmetov z Mníchovej Lehoty (okr. Trenčín). Príspevok k poznaniu antimónovej medi typu Nógrádmarcal v období staršieho eneolitu / Depotfund von Kupfergegenständen aus Mníchova Lehota (Bez. Trenčín). Beitrag zur Erkennung des Antimonkupfers vom Typ Nógrádmarcal in dem älteren Aeneolithikum

Hoard of Copper Artefacts from Mníchova Lehota (Distr. Trenčín). Contribution to the Issue and Distribution of the Antimony Copper of Nógrádmarcal Type in the Early Copper Age. Submitted paper deals with the archaeological evaluation and elemental analysis of a collection of copper industry from the cadastre of municipality Mníchova Lehota (distr. Trenčín) in the north-west Slovakia. The hoard consists of two flat copper axes and a copper chisel. The basic typological-chronological analysis of the flat copper axes showed their connection with the Southeast European types and varieties of the series Pločnik, Coteana, Gumelniţa, as well as Central European types Stollhof, Strážnice, Kalinowice Dolne and axes of the group III of J. Říhovský. Statistical evaluation of basic morphometric parameters of the artefacts by means of principal component analysis (PCA) suggests that the objects from the hoard from Mníchova Lehota belong to the Central European shapes. Separate issue is the elemental analysis of the copper artefacts by means of inductively coupled plasma – mass spectrometry (ICP-MS) and X-ray fluorescence spectroscopy (XRF). On the basis of the statistical evaluation of the elemental analyses the authors set the origin of the copper material mainly in the antimonic copper. They pay attention to its distribution in the area of Central Europe and possible location of the copper material deposit.
A Bionic Simultaneous Location and Mapping with Closed-Loop Correction Based on Dynamic Recognition Threshold

A bionic simultaneous location and mapping with closed-loop correction based on the dynamic recognition threshold is proposed to solve the problem that there exist missing matching and false matching in the model of the rat simultaneous location and mapping for the continuous experienced scenes in the bio-inspired navigation. An enhanced threshold factor and an attenuated threshold factor are introduced in the stage of forming local view cells. The recognition results for the first few frames are used as the excitation to dynamically adjust the template recognition threshold of the current frame image in real time, and to judge whether new local view cells need to generate according to the threshold. Then the experience map is corrected through closed loop detection by integrating the recognition results for the continuous experienced scenes. Experimental results and a comparison with the method under the fixed threshold show that the proposed method not only avoids false matching, but also increases the correct recognition rate, which urges the agent to modify the experience map in a timely manner and to improve the accuracy of the experience map.
e-Detect: Non-User Mask Detection Based on Image Processing Using Convolutional Neural Network Method

The COVID-19 pandemic is a situation that spreads the disease caused by the corona virus. One of the efforts to prevent the spread of the virus from one person to another is by requiring everyone to wear a face mask, especially for those in public areas. There have been several similar previous studies, however, none of them accompanied by follow-up, if someone is found without a face mask. The purpose of this research is to make a detector called e-Detect to detect visitors in public areas such as supermarkets, hospitals, schools and other similar places that without wearing a face mask (non-user face mask) uses the Convolutional Neural Network (CNN) method. If e-Detect detects non-user face mask who will enter through the gate of a public area without wearing face mask, the gate will not open, the buzzer will sound, and the visitor&#x0027;s photo will be sent to the security guard via telegram as a notification. The gate is only open when visitors wear face masks. Experiments have been carried out using 17 types of masks with percentages of: accuracy 94&#x0025;, precision 100&#x0025;, sensitivity 94.11&#x0025;, specificity 100&#x0025;, and error rate is 5.56&#x0025;. A trial on e-Detect ability, show in the experiment range detection distance using any type of mask, which is 175 centimeters. All visitors who come to public areas through the gate that has been installed e-Detect can be ensured that the visitor&#x0027;s face will not be more than 175 cm apart, thus all visitors can be ensured to be well supervised. Based on this data, it can be said that e-Detect is feasible to be produced and used as an effort to prevent the spread of COVID-19.
An Incast-Coflow-Aware Minimum-Rate-Guaranteed Congestion Control Protocol for Datacenter Applications

Today s datacenters need to meet service level objectives (SLOs) for applications, which can be translated into deadlines for (co)flows running between job execution stages. As a result, meeting (co)flow deadlines with high probabilities is essential to attract and retain customers and hence, generate high revenue. To fill the lack of a transport protocol that can facilitate low (co)flow deadline miss rate, especially in the face of incast congestion, in this paper, we propose DCMRG, an incast-coflow-aware, ECN-based soft minimum-rate-guaranteed congestion control protocol for datacenter applications. DCMRG is composed of two major components, i.e., a congestion controller running on the send host and an incast congestion controller running on the receive host. DCMRG possesses three salient features. First, it is the first congestion control protocol that integrates congestion control with coflow-aware incast control while providing soft minimum flow rate guarantee. Second, DCMRG is readily deployable in datacenter networks. It only requires software upgrade in the hosts and minimum assistance (i.e., ECN) from in-network nodes. Third, DCMRG is backward compatible with and, by design, friendly to the widely deployed, standard-based transport protocols, such as DCTCP. The results from large-scale datacenter network simulation demonstrate that in the absence of incast congestion, DCMRG can reduce flow deadline miss rates by 3x and 1.6x compared to D<sup>2</sup>TCP and MRG, respectively. Moreover, DCMRG further reduces the coflow deadline miss rate by more than 40% and 60% and lowers the packet drop probability by 60% and 80%, in the face of incast congestion, compared to D<sup>2</sup>TCP with ICTCP and MRG with ICTCP, respectively.
Research and Practice on the Construction of Psychology Courses in Normal Universities of Engineering and Technology on the Educational Cloud Platform

Improving the teaching quality of psychology courses in normal universities of engineering and technology is the main goal of their course teaching reform. In the course of construction of psychology courses in normal universities of engineering and technology, the use of “educational cloud platform” is of great significance. At present, there are some common problems in the construction of psychology courses in normal universities of engineering and technology, such as backward educational concepts, single teaching mode, limited teacher information level, unable to apply modern information technology proficiently, and little interaction between professors and students andlack of communication. In order to deal with these problems, normal universities of engineering and technology shall depend on the educational cloud platform. Firstly, “cloud courses” of psychology shallbe set in normal universities of engineering and technology; secondly, scientific teaching design and implementation of psychology courses shall be conducted. Besides, professors’ informatization level shall be improve, the use of teacher educational cloud platform technology shall be promoted, and online and offline blended teaching model shall be used to improve the implementation effect of psychology courses.
THE EFFECT OF ONLINE LEARNING STRATEGIES ON THE LEARNING OUTCOMES OF STUDENTS CLASS III SD NEGERI 122358 PEMATANGSIANTAR

This study aims to describe online learning during the Covid19 pandemic as a learning strategy and learning outcomes for third grade students of SD Negeri 122358 Pematangsiantar. The problem to be answered from this research is: How can online learning strategies during the Covid19 pandemic affect the learning achievement of third grade students at SD Negeri 122358 Pematangsiantar? This study uses a qualitative method, the informants of this research are the principal, third grade teacher, and 2 third grade students of SD Negeri 122358 Pematangsiantar. The data collection of this research was done through interviews, observation, and documentation. The data analysis technique used is data reduction, data compilation and categorization. The results of this study indicate that online learning is carried out online by planning and implementing, the planning is to make materials and evaluation questions, while the implementation is to distribute materials and assignments to WhatsApp groups and students who do not have cellphones take questions to school or the teacher comes to students' homes. Student learning outcomes show that not all students can undergo online learning and not all student learning outcomes are achieved due to various factors such as students not having cellphones or WhatsApp applications, internet signals that are difficult to reach because students' homes are in the village, students' awareness of their responsibilities in doing work. assignments are still lacking, and parental awareness and environmental conditions are less supportive so that student learning outcomes are less than optimal.
Pretraining Strategies and Transfer Learning

Obtaining a good deep neural network for most modern datasets is a difficult task. Optimizers need to traverse extraordinarily high-dimensional, jagged loss landscapes and differentiate between good and mediocre solutions using a limited set of tools. Hence, you will seldom see modern deep learning designs solving relatively well-studied problem types directly training the neural network on the data right after initialization – it’s simply too difficult to get good results feasibly by training directly from scratch.
Security of Medical Data Against Adversaries in Internet of Medical Things

The emerging technology of smart medical devices has made the interconnection of those devices and their further monitoring and diagnosing without any human intervention possible by using the Internet of Medical Things(IoMT). While these increase numerous advantages, they also escalate consequential security and privacy concerns as medical management systems accumulate and process susceptible facts. Hence many schemes are used to prevent such casualties. The context-oriented programming protects medical devices from cybercriminal attacks. The Elliptical Curve Digital Signature Algorithm(ECDSA) provides a new solution for enhanced security and privacy between IoMT devices and the cloud via network constituents. On the other hand, Identity-based encryption(IBE), Attribute-based encryption(ABE), Role-Based Access Control Systems(RBAC), and Searchable Encryption(SE) ensures strong encryption of the electronic medical records(EMRs). A review of the conceptual and experimental investigations shows the effectiveness of the schemes as mentioned above concerning enumeration and communication.
Intelligent Recognition of Sports Teaching Video Based on Computer Vision Technology

In recent years, action recognition has gradually developed into a popular research problem in modern computers and other visual science and technology, and has great social science research value. Domestic and foreign experts, scholars and their research units have done a lot of research on this issue, and they have achieved good results. However, the number of research results obtained from the actual application of the human movement recognition technology in the exercise teaching video is relatively small. One of the main reasons is that the motion recognition technology in the human movement teaching video is relatively complex in the application of human movement teaching. At the same time, there is also its own occlusion problem in sports, which leads to a relatively low accuracy of action recognition. Therefore, the development of motion detection and recognition in sports teaching videos is relatively slow. This article aims to study the intelligent recognition of sports teaching videos based on computer vision technology. On the basis of computer vision technology, recognition algorithms and video image preprocessing analysis, support vector machines are used to train sports movements. After the training is completed, the movement can be realized. The experimental results show that the two simulation experiments have achieved good results, verifying the feasibility of the method in the intelligent recognition of sports teaching videos.