Reviewerly/exHarmony huggingface dataset:
[Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer].

Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.特征表达和分类器的性能是决定计算机辅助诊断（CAD）系统性能的重要因素。为了提升基于超声成像的乳腺癌 CAD 系统的性能，本文提出了一种基于自步学习（SPL）的多经验核映射（MEKM）排他性正则化机（ERM）集成分类器算法，能同时提升特征表达和分类器模型的性能。该算法首先通过 MEKM 映射得到多组特征，以增强特征表达能力，并嵌入到 ERM 作为多个支持向量机的核变换；然后采用 SPL 策略自适应地选择样本，由易到难地逐步训练 ERM 集成分类器模型，从而提升分类器的性能。该算法分别在乳腺癌 B 型超声数据库和弹性超声数据库上进行了验证，结果显示 B 型超声的分类准确率、敏感度和特异性分别为 （86.36±6.45）%、（88.15±7.12）% 和 （84.52±9.38）%，而弹性超声的分类准确率、敏感度和特异性分别为 （85.97±3.75）%、（85.93±6.09）% 和 （86.03±5.88）%。实验结果表明，本文所提出算法能有效提升乳腺超声 CAD 的性能，具有投入实用的潜能。.
A Fusion Schema of Hand-Crafted Feature and Feature Learning for Kinship Verification

The rapid progress of technology is remarkable and becomes more widespread in various forms such as social networks, smart phones, and high-definition cameras. In this context, analysing facial to kinship based on digital images is a new research topic in computer vision and has been increased dramatically in recent years. In this paper, we trying to detect the relationships between pairs of face images which is reflected a verification matter: given a pairs of face images with a view to find out and infer kin from the non-kin. For this, we proposed a method define by a fusion scheme composed of feature learning (high-level feature) and hand-crafted feature (low-level feature) along with features subtracting absolute value for face pair. For hand-crafted, we apply a histogram of oriented gradients (HOG) descriptor, while, convolutional neural net- works (CNN) is to represent the feature learning. In our experiment to validate the proposed method we apply restricted protocol setting. The proposed method is tested and evaluated on the benchmark databases KinFaceW-I and KinFaceW-II, and the verification accuracies of 68.6% and 73.5% were achieved, respectively.
Compressed sensing reconstruction for wideband LFM Signal based on fractional Fourier transform

Compressed sensing is a novel theory of sampling, which samples signal directly at lower rates under the different condition from Nyquist theorem. Owing to the sampling theorem is restricted by its large bandwidth and high sampling rate requirements in dealing with linear frequency modulation (LFM) signal. As well as the existing sparse representation algorithms cannot sparse such signal well. This paper proposes resolutions to this problem. First by using the good energy characteristics of LFM signals in the fractional Fourier transform (FRFT) domain, a novel orthogonal basis dictionary based on the DFRFT nuclear matrix is proposed. Combining this dictionary with measurement matrix and orthogonal matching pursuit (OMP) algorithm, the LFM signal sampling and reconstruction scheme is built. Simulation results verify the feasibility and effectiveness of this scheme.
Cognitive Calculation Studied for Smart System to Lead Water Resources Management

This article aims to explore cognitive calculation studied for smart system to lead the innovative path of social development in the field of water resources management from an interdisciplinary perspective. The basic method steps are: first, the specific entry point of target management is defined as water resources information management; second, the water resources knowledge management is emphasized, and in particular, the eight kinds of knowledge system synergy management is emphasized; third, through software-assisted Cognitive Calculation Studied for Smart System to Lead Water Resources Management engineering practice for interpersonal and human-computer collaboration. Its significance lies in: taking advantage of the new opportunity of the second leap in human cognition, from a new perspective to explore how to apply the smart system studied ideas namely cognitive calculation studied for smart system to lead the innovative road of social development in the specific field of water resources management.
Flow-based Alignment Approaches for Probability Measures in Different Spaces

Gromov-Wasserstein (GW) is a powerful tool to compare probability measures whose supports are in different metric spaces. GW suffers however from a computational drawback since it requires to solve a complex non-convex quadratic program. We consider in this work a specific family of cost metrics, namely \textit{tree metrics} for a space of supports of each probability measure, and aim for developing efficient and scalable discrepancies between the probability measures. By leveraging a tree structure, we propose to align \textit{flows} from a root to each support instead of pair-wise tree metrics of supports, i.e., flows from a support to another, in GW. Consequently, we propose a novel discrepancy, named Flow-based Alignment (\FlowAlign), by matching the flows of the probability measures. We show that \FlowAlign~shares a similar structure as a univariate optimal transport distance. Therefore, \FlowAlign~is fast for computation and scalable for large-scale applications. By further exploring tree structures, we propose a variant of \FlowAlign, named Depth-based Alignment (\DepthAlign), by aligning the flows hierarchically along each depth level of the tree structures. Theoretically, we prove that both \FlowAlign~and \DepthAlign~are pseudo-distances. Moreover, we also derive tree-sliced variants, computed by averaging the corresponding \FlowAlign~/ \DepthAlign~using random tree metrics, built adaptively in spaces of supports. Empirically, we test our proposed discrepancies against other baselines on some benchmark tasks.
WLAN-Based Smart Door Closer Design with Image Classification

This chapter discusses an innovative mechanism for object and person recognition and detection, which utilizes a robotic control system to restrict and monitor sensitive or restricted areas from events such as intrusion. This chapter aims to initiate the surveillance or monitoring right from the first point of interaction and verification.
Greedy Scheduling: A Neural Network Method to Reduce Task Failure in Software Crowdsourcing

Context: Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply. Existing analysis reveals an average task failure ratio of 15.7\% in software crowdsourcing markets. Goal: The objective of this study is to provide a task scheduling recommendation model for software crowdsourcing platforms in order to improve the success and efficiency of software crowdsourcing. Method: We propose a task scheduling method based on neural networks, and develop a system that can predict and analyze task failure probability upon arrival. More specifically, the model uses a range of input variables, including the number of open tasks in the platform, the average task similarity between arriving tasks and open tasks, the winner's monetary prize, and task duration, to predict the probability of task failure on the planned arrival date and two surplus days. This prediction will offer the recommended day associated with the lowest task failure probability to post the task. The proposed model is based on the workflow and data of Topcoder, one of the primary software crowdsourcing platforms. Results: We present a model that suggests the best recommended arrival dates for any task in the project with surplus of two days per task in the project. The model on average provided 4\% lower failure ratio per project.
Pyramid and Similarity Based Feature Enhancement Network for Person Re-identification

Abstract Person re-identifification (Re-ID) has becomes an increasingly important task due to its wide applications in the field of intelligent video surveillance. However, Because of the camera shooting angle, the different proportions and pose changes of pedestrians in images are also different, such problem substantially hinders the model’s capability on further improving feature extraction. In this paper, we propose a new end-to-end architecture named Pyramid and Similarity Based Feature Enhancement Network (PS-Net) which includes Pyramid Joint Attention(PJA) and Similarity Feature Fusion Branch(SFF) to enhance the feature maps performance. Among them, PJA contains spatial attention at different receptive fields and channel attention to enhances the discriminative area of pedestrian features by combining the advantages of two different attentions. SFF fuses the features which have different levels of information but have congruent relationships, enriches the information contained in pedestrian features and generates more robust feature. Extensive experiments have been conducted to validate the superiority of our PS-Net for Re-ID over a wide variety of state-of-the-art methods on two large-scale datasets: Market-1501 and DukeMTMC-ReID.
A knowledge and intelligent-based strategy for resource discovery on IaaS cloud systems

Resource discovery selects appropriate computing resources in cloud systems to accomplish the users' jobs. This paper proposes a knowledge and intelligent-based strategy for resource discovery in IaaS cloud systems, called KINRED. It uses a fuzzy system, a Multi-Criteria Decision Making (MCDM) controller and an artificial neural node to discover suitable resources under various changes on network metrics. The suggested fuzzy system uses hardware specifications of the computing resources in which CPU speed, CPU core, memory, disk, the number of virtual machines and utilisation rate are considered as inputs, and hardware type is considered as output of the system. The suggested MCDM controller makes proper decisions based on users' requirements in which CPU speed, CPU core, memory, and disk are assumed as inputs, and job type is assumed as output of the controller. Furthermore, the artificial neural node selects the computing resource having the highest success rate based on both outputs of the fuzzy system and MCDM controller. Simulation results show that the proposed strategy surpasses some of the existing related works in terms of the number of successful jobs, system throughput and service price.
Methodological issues of the research of the formation of computer graphics skills in bachelor students of art and pedagogical education

The purpose of the study is to identify the needs of bachelor students of various areas in the field of fine arts in the skills of mastering computer graphics and using its various types in professional activities. The Federal State Educational Standard (FSES) in the areas of education, general and professional competencies associated with the possession of computer graphics, methods and techniques of work necessary for organizing a holistic process in this discipline are analyzed.
Technological Advances in Microfinance Banks and Economic Growth in Nigeria

The study was designed to estimate growth implications of the intermediation activities of microfinance banks in Nigeria. The study covered the period 1992 to 2016. Model estimation was based on the technique of autoregressive distributed lag (ARDL) using data from the Central Bank of Nigeria statistical bulletin. Traditional intermediation functions of microfinance banks (deposit mobilization and credit creation) were adopted as explanatory variables while inflation and asset base were introduced as controlled variables. The result showed that while deposit mobilization significantly enhanced growth, microfinance banks’ loans and advances impeded the growth process.
Tactical and Strategical Analysis in Virtual Geographical Environments

Planning tactical and strategical operations on a terrain is a well structured and complex process. It is usually executed by a group of interdisciplinary experts with different objectives. Traditional equipment for tactical analysis like paper maps or sand tables cannot transfer spatial understanding for visibility analysis and height judgment. Immersive virtual geographical environments offer additional perspectives for rapid decision making and reasoning of spatial structures. We propose a collaborative multi-user virtual reality system for the tactical analysis of terrain data to support mission planning.
Feature-Based Systems and Software Product Line Engineering with Gears from BigLever

This paper describes a demonstration of the product line engineering tool and framework called Gears from BigLever Software. Gears is the automation at the heart of a PLE Factory, which itself is the conceptual construct at the heart of Feature-based Product Line Engineering. (Feature-based PLE is the subject of an upcoming ISO standard.) Gears provides the means to create and maintain a Feature Catalog via a unified feature modeling language; the means to create and maintain a Bill-of-Features Portfolio, which is a way to specify the members of the product line by the features that each one exhibits; and a single variation point mechanism that works in Shared Assets across the entire product lifecycle. The result is an automated production line capability that can quickly produce any product in the portfolio from the same, single set of shared assets.
PROFESSIONAL PREPAREDNESS OF SPECIALISTS FOR THE JUDICIARY: GENESIS AND DEVELOPMENT OF THE CONTENT OF THE CONCEPT

The article is devoted to the problem of professional preparedness of the specialists for judicial system in the professional mediation and communication activities in the context of the transition to the sixth information-network technological paradigm. It traces the genesis of the concept of professional preparedness from the standpoint of psychological and pedagogical approaches. The authors consider the development of the concept’s content in a historical retrospective. The interrelation between the concepts of professional preparedness, competence and competency is shown. Emphasis is placed on the fact that the theoretical and methodological analysis contributes to the comprehension of existing and developing of new models of training of specialists for the judicial system in the professional mediation and communication activities, searching for the innovative pedagogical technologies and teaching methodological systems in the professional training of the future specialists in mediation and communication activities. The problem of the genesis of the concept “professional preparedness” remains in the field of research, supplementing the latter with the necessary component for specific research, obtaining previously unknown transformations of already existing interpretations, and proposing new models for the development of future professionals’ professional activity. The study of the genesis of the concept allowed the authors to come to an understanding of professional preparedness as a complex integrative formation, acting as a link between professional competence and professional competency. The analysis of psychological and pedagogical sources on the problem under study has shown that the actualization of the problem of professional preparedness of a specialist for judicial system to professional activity in general and its genesis, in particular, is of great importance for pedagogy of higher school. The theoretical significance of this study is determined by the fact that the received in the course of the study data not only contribute to clarifying the scientific interpretation of the concept “professional preparedness”, but also promote the development of higher school pedagogy theory in the context of formation of professional preparedness of specialists for the judicial system.
Advising Agent for Service-Providing Live-Chat Operators

Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce. Training enough skilled operators who are able to provide good service is a challenge. We propose a methodology for the development of an assisting agent that provides online advice to operators while they attend clients. The agent is easy-to-build and can be introduced to new domains without major effort in design, training and organizing sknowledge of the professional discipline. We demonstrate the applicability of the system in an experiment that realizes its full life-cycle on a specific domain, and analyze its capabilities.
Integrated superconducting circuit for qubit and resonator protection*

A semi-infinite waveguide acts as a mirror and helps protect the qubit in front of it from decoherence. Here, we investigate the interference effect in an open waveguide consisting of resonators with different decay rates. We find that a lossy resonator works as a mirror and changes the effective decay rate of the other. The spontaneous radiation of qubit is related to its environment, and we can control it by arranging the lossy resonator’s position or frequency. Our approach helps improving the qubit performance, as well as the quantum gate fidelities.
Low degree Lorentz invariant polynomials as potential entanglement invariants for multiple Dirac spinors

A system of multiple spacelike separated Dirac particles is considered and a method for constructing polynomial invariants under the spinor representations of the local proper orthochronous Lorentz groups is described. The method is a generalization of the method used in Johansson (2022) for the case of two Dirac particles. All polynomials constructed by this method are identically zero for product states. The behaviour of the polynomials under local unitary evolution that acts unitarily on any subspace defined by fixed particle momenta is described. By design all of the polynomials have invariant absolute values on this kind of subspaces if the evolution is locally generated by zero-mass Dirac Hamiltonians. Depending on construction some polynomials have invariant absolute values also for the case of nonzero-mass or additional couplings. Because of these properties the polynomials are considered potential candidates for describing the spinor entanglement of multiple Dirac particles, with either zero or arbitrary mass or additional couplings. Polynomials of degree 2 and 4 are derived for the cases of three and four Dirac spinors. For three spinors no non-zero degree 2 polynomials are found but 67 linearly independent polynomials of degree 4 are identified. For four spinors 16 linearly independent polynomials of degree 2 are constructed as well as 26 polynomials of degree 4 selected from a much larger number. The relations of these polynomials to the polynomial spin entanglement invariants of three and four non-relativistic spin-12 particles are described. Moreover, it is described how degree 4 polynomials for five spinors can be constructed and how degree 2 polynomials can be constructed for any even number of spinors.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PENJUALAN BAJU DISTRO BERBASIS WEBSITE E-COMMERCE DI KOTA BATAM

Analysis and design of information systems based distro shirt sales e-commerce website. Distributions to help and increase sales to make reservations online and provide information on the latest products that are sold to customers through the web page. The research method is done by two methods, namely, methods of data analysis and information system design. Methods of data analysis was done to find the information needed to research activities, analysis of the findings of the research, literature and identification system requirements. Results achieved that e-commerce system requires system-based sales e-commerce website, with their e-commerce website is expected to make it easier for both the Houses Online Distro manage sales transactions of products and customers in order the products by utilizing the Internet media so can work effectively and efficiently so that it can develop sales targets.
Mobile Application for Basic Computer Troubleshooting using TensorFlow Lite

This paper presents an application for troubleshooting computer problems using the TensorFlow and deep learning techniques. The functionality of the proposed application contains the problem lists by searching problems with text and photographs. Deep learning technique has been applied for searching information through TensorFlow Lite library. The main components of the proposed application include the classification model of computer problems and the deep learning through the TensorFlow library in order to compare the classified images, which was being learned to classify two types of images, namely, the blue screen and black screen problems. Experiments has used the 100 samples of each blue screen and black screen. The results showed that the success rate of classification of blue screen is 80%and is 90% for black screen problem.
Research and realization of crouch start correction system based on human pose estimation

For beginners in sprinting, receiving timely and effective guidance and feedback can not only help them master the movement, but also effectively avoid sports injuries. However, the existing guidance and improvement methods are inconvenient and inaccurate. This paper makes an in-depth study of the human body posture synthesis model based on deep neural network, and uses it to obtain posture and joint data of athletes, and judges whether the posture is correct and standard through the correction system. It is found that the model in this paper can obtain more accurate human body data. The position correction system has a higher posture recognition rate, which can effectively judge whether the position posture is standard or not, it is of great significance for actual position guidance.
Analysis on the Reform of Teaching Mode of Computer Animation Production in Colleges and Universities Based on Flip Classroom

With the continuous development of Internet technology, the teaching of computer animation in colleges and universities is also facing new opportunities and challenges. As a new teaching mode, flip classroom has been widely used in domestic universities, which plays an important role in improving classroom teaching efficiency. Compared with traditional classroom, flip classroom can effectively improve students' learning efficiency in computer animation production. Based on this, this paper first briefly introduces the characteristics of flip classroom, then analyzes the problems existing in animation teaching mode at present, and then analyzes the reform measures of computer animation teaching mode under flip classroom mode from five aspects, so as to provide a reference for relevant people to exchange.