Reviewerly/exHarmony huggingface dataset:
Machine Learning Method for the Breast Cancer Detection

Bioinformatics can be defined as a science of storing, fetching, arranging, interpreting and using information obtained from biological series and molecules. Prediction can be defined as a statement about future event on the basis of present situation. This work focuses on diabetic prediction with machine learning algorithms. The breast cancer prediction has various steps. A voting-based classifier is devised in this research to predict cancer. The performance for the breast cancer prediction is optimized up to 2 percent using proposed algorithm.
Transition Probabilities for Markov Process on a Line Segment Located within a Quarter-Plane

The paper considers a quadratic birth-death Markov process. The points on a line segment located within a quarter-plane represent the states of the random process. We designate the set of vectors that have integer non-negative coordinates as our quarter plane. The process is defined by infinitesimal characteristics, or transition probability densities. These characteristics are determined by a quadratic function of the coordinates at the segment points with integer coordinates. The boundary points of the segment are absorbing; at these points, the random process stops. We investigated a critical case when process jumps are equally probable at the moment of exiting a point. We derived expressions describing transition probabilities of the Markov process as a spectral series. We used a two-dimensional exponential generating function of transition probabilities and a two-dimensional generating function of transition probabilities. The first and second systems of ordinary differential Kolmogorov equations for Markov process transition probabilities are reduced to second-order mixed type partial differential equations for a double generating function. We solve the resulting system of linear equations using separation of variables. The spectrum obtained is discrete. The eigen-functions are expressed in terms of hypergeometric functions. The particular solution constructed is a Fourier series, whose coefficients are derived by means of expo-nential expansion. We employed sums of functional series known in the theory of special functions to construct the exponential expansion required
A study on Chinese teacher education professional accreditation based on the standards comparison

By comparing with the American CAEP teacher education accreditation standard, we study the characteristics of Chinese teacher education professional accreditation (TEPA) and the differences between Chinese and American accreditation standards. The aim of TEPA, which is drawing lessons from the idea of engineering education professional accreditation, is to improve the overall quality of Chinese teachers team, ensure the quality of teacher education, make the quality of pre-service teachers cultivation institutions continue to improve, and meet the needs of the society for qualified and excellent teachers.
Factors Influencing Salespeople’s Consistency to Enhance Sale Performance: A Moderating Role of Self-Efficacy

The purpose of this analysis is to examine the impact of emotional intelligence (EI) and market orientation (MO) as an antecedent of sales performance (SP). Furthermore, this paperinvestigates the moderating role of self-efficacy ontherelationship between EI, MO,and SP. To achieve this, 285 questionnaires were sent to ten pharmaceutical companies' top, middle, and low-level managers in two Pakistani cities. The choice of these companies was dependent on simple random sampling methods and minimal sample size estimation techniques. Structural equation modeling (SEM) is then applied to analyze the model and hypothesis,and partial least squares regression is used for data processing in Smart-PLS. The findings presented here suggest that the two dimensions – emotional intelligence and market orientation – positively impacttheperformance of sales staff. The results also support the moderation effect of self-efficacy on the relationships between EI, MO, and SP. Both EI and MO are identified as vital influencers of sales resultsconcerning return on revenue and future industry insight, evident market patterns, and business predictions. Sales staffbehaviorneeds toadjust based on these changes, especially considering large differences in the competitive trade and market climate and due to the severe rivalry between businesses and clientusage habits. This research adds to the literature by identifying the unexplored moderation impact of self-efficacy on EI, MO, and SP. Furthermore, it expands the literature on self-efficacy effects, currently limited to attitudes and performance.
Pedagogical conditions of correctional and developmental education of children with mental retardation of puberty by means of visual arts as an element of socialization

In this paper, the author addresses the problem of socialisation of children with a history of disabilities and mental retardation by means of visual arts. It is important to look at the very sphere of life of these categories of children. As a rule, these children, for the most part, live in dysfunctional families, which makes it almost impossible to provide them with a form of socialization. This article focuses on the extent to which and how a programme of socialisation of these children can be achieved through the medium of visual arts. At the moment there are works of modern pedagogues who have devoted their scientific researches to this problem, but there are few teachers-artists who deal with this problem. In this article the author devotes his creative attention to this issue and reflects on what means of fine arts and visual literacy can develop and guide in the right direction the socialization of children and young people with disabilities by means of fine arts. A number of scientific works devoted to this problem are seriously reviewed. The author is actively trying to draw the attention of the teaching community to the problem of prevailing social conditions, to reach an educational and cognitive level and, as a consequence, to a better product of the set task of socialization of children with special needs and disabilities in the learning process by means of subject disciplines: drawing, painting, composition, printmaking, etc.
Reliability Monitoring of Laser Scanner Based Navigation

Autonomous guided vehicles have great advantages over rigidly track-guided conveyor technology, as they can react flexibly to changes in the application area. Temporary obstacles can be easily avoided. The vehicles can circumnavigate bottlenecks and areas at risk of congestion switch to alternative routes. To avoid accidents, safety-relevant position detection is necessary in many areas. The current speed is derived from this driven trajectory and this is safely reduced in the working areas. Minimum distances can also be safely maintained. Therefore it is necessary to permanently control the measured position with regard to disturbance variables and to monitor the reliability of the position detection in real time.
A Statistical and Deep Learning-Based Daily Infected Count Prediction System for the Coronavirus Pandemic.

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Conversation and Recommendation: Knowledge-Enhanced Personalized Dialog System

Traditional recommender systems are usually single-shot systems, lacking real-time dialog with customers. Using dialog as an interactive method can more accurately capture user preferences and enhance system transparency. However, building such a goal-oriented dialog system suffered many challenges as the system itself needs to collaborate with various sub-tasks, such as collecting user needs through interaction, recommending appropriate products to users. Most existing work of dialog systems does not comprehensively consider this scenario and the challenges caused. In this paper, we propose a novel memory network framework for conversational recommendation, which harness dialog historical information to endows our model with adaptability in different dialog scenarios, and leverage the knowledge base and user profiles to reweight candidates, to reduce the ambiguity during interactions and improve the quality of conversational recommender systems. Through the experiments on the personalized bAbI dialog dataset and restaurant recommendation application, we demonstrate that the proposed method can achieve state-of-the-art performance in a few classical tasks, such as options display and information provision, etc.
Pedestrian Attribute Recognition Based on Category Probability Loss Function

In order to improve the recognition performance of pedestrian attribute recognition algorithm, based on the DeepMAR algorithm framework, an end to end attribute recognition algorithm is proposed. Firstly, after the output of the ResNet50 backbone network, a lightweight channel attention module is added to weight high level semantic information to improve the accuracy of global attribute recognition, secondly, in terms of loss function, the network output category probability is used as an index to measure the difficulty of sample training, to adjust the weight of each attribute task in the loss function. The average accuracy rate of the experiment on the PETA dataset reached 85.28%, and the average accuracy rate on the PA100k dataset reached 79.49, which was 1.29% and 1.58% higher than the DeepMAR algorithm.
Explicit bounds for composite lacunary polynomials

Let f, g, h ∈ C [x] be non-constant complex polynomials satisfying f (x) = g(h(x)) and let f be lacunary in the sense that it has at most l non-constant terms.Zannier proved in [9] that there exists a function B 1 (l) on N, depending only on l and with the property that h(x) can be written as the ratio of two polynomials having each at most B 1 (l) terms.Here, we give explicit estimates for this function or, more precisely, we prove that one may take for instanceMoreover, in the case l = 2, a better bound is obtained using the same strategy. IntroductionLet f, g, h ∈ C [x] and f = g • h be a lacunary polynomial with l nonconstant terms, i.e. f is of the form f (x) = a 0 + a 1 x n1 + • • • + a l x n l .Note that only the number of terms is viewed as fixed, while the coefficients and the degrees may vary.In [9], it was shown by Zannier that there exists a function B 1 (l) such that h(x) can be written as the ratio of two polynomials in C [x] both having no more than B 1 (l) terms.In order to give explicit estimates for B 1 (l), we are following the strategy of [9, Prop.2].Therefore, we will recall those parts of Zannier's proof, which are relevant to our arguments, in the very beginning of the paper.We prove Theorem 1.1.Let f, g, h ∈ C [x] be non-constant complex polynomials such that f (x) = g(h(x)) has at most l non-constant terms.Then h(x) can
Expression of Concern: A white-box cryptographic SM2 scheme based on space hardness

The operating environments of a cryptographic algorithm have important impacts on its security. Traditional cryptographic algorithms need to run in a secure and trusted environment, in which case the internal state of these algorithms cannot be accessed by the outside. Namely, the cryptographic programs need to run under the black-box model. With the development of science and technology, the application scenarios of cryptographic algorithms are diversified, resulting in increasingly uncontrollable operating environments for cryptographic algorithms. White-box attack is a kind of terminal-based attack, which assumes that an adversary has complete control over the terminal. With this kind of attack, secret data can be easily exposed. In this paper, we propose a defense method based on strong (M,Z)- space hardness. The core idea is to put the secret value into the look-up table through some mathematical transformations. When the volume of the look-up table obtained by an adversary is limited, the secret value such as the private key cannot be recovered in polynomial time. Therefore, our proposed method is safe and efficient based on space hardness. We have designed a white-box implementation of SM2 digital signature algorithm to protect the private key under white-box attack. The main ideas presented in this paper can also be applied to constructing the white-box implementations of other asymmetric cryptographic algorithms.
Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization

Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work presents an end-to-end model based on attention-based GCN and semantic matching optimization, which first employs the attention mechanism to enable the central node to learn more discriminating representations from its neighbors in the knowledge graph. Moreover, a coarse-to-fine semantic matching optimization approach is proposed to progressively bridge the domain gap. Experimental results validate that the proposed model not only has superiority on recognizing the images of known and unknown classes, but also can adapt to various openness of the target domain.
Adaptive High Boost Filtering for Increasing Grayscale and Color Image Details

Generally, the high boost filtering often produced a lot of unpleasant noise, especially the flat area. Moreover, it also gives color distortion in the edge regions when applied for the color image. In this paper, we propose the adaptive sharpening filter based on the high boost filtering for increasing the image details. This filter consists of two parts as the original and the High-frequency Component (HC) parts. The HC part was controlled by using the adaptive weight matrix, which was constructed from the non-linear combination. The Laplacian operator was employed as the high-pass filter for generating both the HC and the adaptive weight matrix. The simulated step-edge image, grayscale images, and color images were applied for observing our filter performance. The traditional bilateral filter and high boost filter are used as the comparison filters. The experimental results show that our proposed filter can increase the edge sharpness while it did not enhance the unpleasant noise for the flat area. Moreover, it also reduces the color distortion and edge ringing when applied to the color images.
Factor analysis of ruangguru application use on high school students in Jember

Abstract The quality of education in Indonesia is oriented toward the education in Jakarta. PISA 2015 Results in Focus published in 2018 indicates that Indonesia occupies the 62nd place of 70 countries. On the other hand, COVID-19 pandemic has a significant impact in education. All schools in the world are simultaneously doing online learning, which leads to a shift in interaction patterns in the world of education from face-to-face to technology-based learning. The aim of the research is to know the dominant factors in influencing high school students in Jember on the use of Ruangguru Online Tutoring application. The research used the quantitative method. The sampling technique in the research was the incidental sampling technique to 100 respondents. We have found the most dominant factor of some other influencing factors for Ruangguru users was the product, besides that there was a significant influence between product, price, location, promotion and service with purchasing decision. We recommend that the Ruangguru application is more innovative in technology-based education contents.
Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning

Distributed messaging systems (DMSs) are often equipped with a large number of configurable parameters that enable users to define application run-time behaviors and information dissemination rules. However, the resulting high-dimensional configuration space makes it difficult for users to determine the best configuration that can maximize application QoS under a variety of operational conditions. This poster introduces a novel, automatic knob tuning framework called DMSConfig. DMSConfig explores the configuration space by interacting with a data-driven environment prediction model(a DMS simulator), which eliminates the prohibitive cost of conducting online interactions with the production environment. DMSConfig employs the deep deterministic policy gradient (DDPG) method and a custom reward mechanism to learn and make configuration decisions based on predicted DMS states and performance. Our initial experimental results, conducted on a single-broker Kafka cluster, show that DMSConfig significantly outperforms the default configuration and has better adaptability to CPU and bandwidth-limited environments. We also confirm that DMSConfig produces fewer violations of latency constraints than three prevalent parameter tuning tools.
StrongPose: Bottom-up and Strong Keypoint Heat Map Based Pose Estimation

The adaptation of deep convolutional neural network has made revolutionary advances in human body posture estimation. Various applications utilizing deep neural network for pose estimation have drawn considerable attention in recent years. However, prediction and localization of keypoints in single-person and multi-person images is still a challenging problem. Towards this, we propose a bottom-up approach to pose estimation and motion recognition. We present StrongPose system that deals with object-part associations using part-based modeling. The convolution network in our model detects strong keypoint heat maps and predicts their comparative displacements, allowing keypoints to be grouped into human instances. Further, it utilizes the keypoints to generate body heat maps that can determine the position of the human body in the image. The StrongPose system is based on fully convolutional engineering and makes proficient inferences while maintaining runtime regardless of the number of individuals in the image. We train and test the StrongPose on the COCO dataset. Evaluation results show that our framework achieves average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152. Our results considerably outperform prior bottom-up frameworks.
Sequence Analysis and Generation of Filipino Ethnic Textile Patterns using Finite Automata

The study presents an analysis and generation of pattern sequences acquired from northern Luzon ethnic textiles. The textile sample images were collected, analyzed, and processed into a grayscale raster shapes. The analyzed data for pattern sequences was converted into a deterministic finite automaton which includes the transition tables and diagram. The experiment revealed that some patterns have similar sequences which were grouped together. Based from each grouped sequence, the sequences were analyzed, and an algorithm was developed. Each algorithm was used to generate a new pattern that is closely comparable to the grouped sequences. The study observed the following patterns which are a(bc)nbma, abna, and (ab) nam representing the common sequences appearing in the textile patterns.
Exploration on the Construction of Teaching and Research Group under Background of New Engineering

The research is based on the background of new engineering, taking the teaching and research group of software engineering of Anhui Sanlian University as an example, this study is devoted to exploring a feasible way of industry-education integration, students participate in the project research and teaching effect for teachers for construction of the teaching and research group. Furthermore, further work is put forward to promote the deep integration of information education technology with smart campus, to achieve smart management of the teaching and research group gradually.
Deployment of Compressed MobileNet V3 on iMX RT 1060

Deep Neural Networks (DNN) are prominent in most applications today. From self-driving cars, sentiment analysis, surveillance systems, and robotics, they have been used extensively. Among DNNs, Convolutional Neural Networks (CNN) have achieved massive success in computer vision applications as the human visual system inspires their architecture. However, striving to achieve higher accuracies, CNN complexity, parameters, and layers were increased, which led to a drastic surge in their size, making their deployment challenging. Over the years, many researchers have proposed various techniques to alleviate this issue-one of them being Design Space Exploration (DSE) to minimize size and computation with little compromise to accuracy. MobileNet V3 is one such architecture designed to achieve good accuracy while being mindful of resources. It produces an accuracy of 88.93% on CIFAR-10 with a size of 15.3MB. This paper further reduces its size to 2.3MB while boosting its accuracy to 89.13% using DSE techniques. It is then deployed into NXP's i.MX RT1060 Advanced Driver Assistance System (ADAS) platform.
Collaborative filtering recommendation algorithm based on improved denoising auto encoder

Aiming at the problems of sparse scoring matrix and low recommendation accuracy of traditional collaborative filtering algorithms, this paper proposes a collaborative filtering recommendation algorithm based on improved denoising auto encoder. First of all, this topic adds a balance matrix to the encoding and decoding process of the denoising auto encoder to compress the high-dimensional and sparse user behavior vector into a low-dimensional and dense user feature vector. Then, the user similarity is calculated in the process, celebrity factors are considered to obtain user similarity based on celebrity effect. Finally, a program recommendation list is generated based on the final user similarity. Experimental results show that the algorithm enhances the performance of scoring prediction, and improves the accuracy and recall rate of recommendation results.
Social Distance Detection Using Yolov3

Social distancing detection using machine learning to evaluate the distance between people to mitigate the impact of this coronavirus pandemic. The detection tool was developed to alert people to maintain a safe distance with each other by evaluating a video feed. The video frame from the camera was used as input, and the open-source object detection pre-trained model based on the YOLOv3 algorithm was employed for pedestrian detection. Later, the video frame was transformed into top-down view for distance measurement from the 2D plane. The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red frame and red line. The proposed method was validated on a pre-recorded video of pedestrians walking on the street. The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video. The developed technique can be further developed as a detection tool in real-time application.