Reviewerly/exHarmony huggingface dataset:
A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting

Hospitals have their own database structures and maintain their data in a closed manner. For this reason, it is difficult for researchers outside of institutions to access multi-center data. Therefore, if the data maintained by all hospitals follow a commonly shared format, researchers can analyze multi-center data using the same method. To safely analyze data using a common data model (CDM) in a distributed multi-center network environment, the objective of this study is to propose and implement the processes for distribution, executing the analysis codes, and returning the results. A secure CDM-based data analysis platform (SCAP) consists of a certificate authority (CA), authentication server (AS), code signer (CS), ticket-granting server (TGS), relaying server (RS), and service server (SS). The AS, CS, TGS, and RS form the central server group of the platform. An SS is stored on a hospital server as an agent for communication with the server group. We designed the functionalities and communication protocols among servers. To safely conduct the intended functions, the proposed protocol was implemented based on a cryptographic algorithm. An SCAP was developed as a web application running on this protocol. Users accessed the platform through a web-based interface.
CERT Training Platform over the Event-Recordable Container

The current COVID-19 pandemic has resulted in many changes in the IT systems and services of institutions, which also heightened the concerns regarding the potential increase in intrusion incidents, especially when most works in institutions are performed at home. The need for pre-training against intrusion incidents has then become extremely necessary. Unfortunately, current learning methods in existing studies are insufficient for application in the present demand because these methods were originally designed for environments that are tailored-fit for learners and not in actual environments. This paper proposes a training system, namely, computer emergency response team (CERT), that can be specifically designed for learners in an institution to provide intrusion-incident cases using a Web-based training system. CERT can easily replicate the service or system in an institution to a honeypot environment to automatically collect and classify intrusion incidents using diverse evaluation criteria so that learning can be achieved from different perspectives. Hence, the institution operating service and system can easily be replicated. Artifacts of intrusion incidents are collected using the Docker container technology and event-recordable container, which are analyzed using a Web browser without installing a separate program. Thus, optimal learning results from the analysis of actual attacks are expected.
Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance

Machine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) is applied to facilitate Machine Learning. For effective and efficient decision-making in Machine Learning, the environment’s own experience is captured, stored and reused using the DDNA technique. The proposed approach is implemented on practical test cases like a Chatbot. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The experimental test and results of the presented implementation of Decisional DNA Chatbot case studies support it as a technology that can improve and be applied to the technology, enhancing intelligence by predicting capabilities and facilitating knowledge engineering processes.
Cross Different Time Periods Detection Algorithm based on YOLOv4

With the development of autonomous driving vehicle target detectiontechnology, multi-target detectionincomplex environment has gradually become the focus of attention. In this paper, based on the YOLOv4 algorithm, the target detection problem is carried out on the images from different time periods in the training and test set. Firstly, we train the unsupervised CycleGAN model to get some synthetic images at night. Secondly, these synthetic night images combined with the images collected in the sunny weather are input to train the YOLOv4 detector and then the final model is used to detect the targets at night. The experimental results show that the detection accuracy of our model for car, person and traffic sign is improved by 3.65%, 4.29% and 5.42% respectively when compared to the baseline model, that is, only training the images from the daytime and testing the targets during the night. Inaddition, the recall values of these three targets are all increased by more than 5%, which indicates that our model reduces the missing detectionrate. The above experimental results have fully demonstrated the effectiveness of the combination of supervised and unsupervised algorithm for target detectioncross different time periods. And the proposed model greatly improves the safety of the autonomous driving vehicles, which is of great significance for target detection in the road.
Application of Computer Aided Language Learning in College Russian Teaching

With the development of computer technology. Computer Assisted Language Instruction (CALL) system is becoming more and more mature. More and more CALL systems have been applied in teaching and examination to assist teachers in teaching and improve students’ learning efficiency. This paper explores a more efficient pronunciation error detection algorithm under the framework of deep neural network (DNN). In view of the particularity of oral learning, this paper firstly improves the original acoustic model, and then studies the pronunciation error detection algorithm from three different perspectives, namely, posterior probability calculation, hypothesis testing model and supervised learning classification, expands and improves the traditional method, and proposes a new method under the framework of DNN. The deep neural network model was built to model the eigenvalues to get the final score.
Application of Random Vector Functional Link Network for Software Defect Prediction

Software defect prediction (SDP) aims to develop predictive techniques that identify software modules’ default proneness using structural and quality attributes. The use of randomized neural networks, especially random vector functional link (RVFL) network, has been limited in SDP. This study proposes a novel SDP model based on principal component analysis (PCA) and RVFL. The model uses PCA for dimensionality reduction of the data and employs RVFL to classify the defective modules. Extensive experiments are conducted on 17 PROMISE repository datasets, and the proposed model is compared with ten classic machine learning (ML) techniques for within-project and inter-release scenarios. The experimental results indicate that PCA–RVFL outperforms the classic ML techniques for most of the datasets, proving the predictive capability of the proposed model in the field of SDP.
Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions During Clinical Evaluation of Medical Images

Semantic segmentation of medical images is an essential first step in computer-aided diagnosis systems for many applications. However, given many disparate imaging modalities and inherent variations in the patient data, it is difficult to consistently achieve high accuracy using modern deep neural networks (DNNs). This has led researchers to propose interactive image segmentation techniques where a medical expert can interactively correct the output of a DNN to the desired accuracy. However, these techniques often need separate training data with the associated human interactions, and do not generalize to various diseases, and types of medical images. In this paper, we suggest a novel conditional inference technique for DNNs which takes the intervention by a medical expert as test time constraints and performs inference conditioned upon these constraints. Our technique is generic can be used for medical images from any modality. Unlike other methods, our approach can correct multiple structures simultaneously and add structures missed at initial segmentation. We report an improvement of 13.3, 12.5, 17.8, 10.2, and 12.4 times in user annotation time than full human annotation for the nucleus, multiple cells, liver and tumor, organ, and brain segmentation respectively. We report a time saving of 2.8, 3.0, 1.9, 4.4, and 8.6 fold compared to other interactive segmentation techniques. Our method can be useful to clinicians for diagnosis and post-surgical follow-up with minimal intervention from the medical expert. The source-code and the detailed results are available here [].
Development and Testing of Motion-Detection Techniques for People with Cerebral Palsy

This paper describes several computer access methods tested by Eva, a woman with choreoathetosic cerebral palsy. This disease prevents her from controlling the peripherals and configurations that normally give access to information and communication technologies, further limiting her independence. To make Eva access a computer, we focused our efforts on the methodologies that Eva could control by just moving her neck and head. These sensors were: Kinect, inertial measurement units (IMU), and video. Kinect, composed of a system of cameras and sensors, gives the option to interact and control the devices contactlessly. The IMU is a device consisting of an accelerometer and a gyroscope that measure velocity, orientation, and gravitational forces. For live image processing, a common webcam was used. During the development of the experiment, Eva must follow a sequence shown on the computer screen that alternates movement of the head with rest. These movements involved moving the head up, down, right, or left. Our results showed that the Kinect system could not be used effectively, while the image-processing algorithm obtained the best performance.
CAD Cut-piece Retrieval Method Based on Representation Learning

The accuracy of retrieving the CAD file corresponding to the cut-piece from the CAD library determines the efficiency of the cut-piece automatic detection system and the digital management of the cut-piece inventory. However, the materials of the cut-piece are complex and diverse (including cowhide, artificial leather, fabric, etc.), contain multi-layered shapes and structures, and are prone to deformation. The traditional registration algorithms cannot satisfy the accurate registration between CAD and cut-piece. In order to solve the problem of high-frequency translation and complex shape of the cut-piece, we propose an image retrieval algorithm based on representation learning. This paper takes FaceNet as the basic framework. First, the loss function combining Softmax loss and center loss is used to maintain the discriminant ability between classes and optimize the cohesion of the ternary loss function. Second, the BlurPool module is used to enhance the anti-aliasing of the downsampling process and enhance the translation invariance of the network while maintaining network performance. Finally, the multi-feature deep fusion (FRE) module designed in this paper is used to solve the problem of reduced model expression ability caused by different sizes of cut-piece. A series of experiments on our dataset show that the proposed representation learning network is more accurate in the SVM classifier, and it only takes 30ms to predict a single cut-piece.
Active Learning Strategies and Convolutional Neural Networks forMammogram Classification

Deep learning has been used successfully in a variety of applications due to the large data availability and the growth in computing power. However, some domains present a shortage of both samples and labels, for instance, the medical area. In this work, we propose machine learning approaches that include traditional supervised classifiers and active learning methods for the breast lesion domain, in order to aid breast cancer diagnosis. We propose the introduction of active learning strategies in this process, to sort out the most informative samples in the dataset. The active learning process reduces the burden of the dataset annotation, while also improving the robustness of our models. Hence, we achieved considerable gains with fewer labeled training images, minimizing the specialist’s annotation effort. The validation of our proposed methodology is done on a public breast lesion-related dataset and our results show considerable accuracy gains over the traditional supervised learning approach and reductions of up to \(68\%\) in the labeled training sets.
A Theoretical Approach For Developing A Semantic Analysis IIR Tool

An IIR (intelligent information retrieval) is a machine learning system used by users to retrieve the massive volume of information available through the internet. IIR offers personalization and efficiency in the current internet development allowing users to examine and acquire appropriate information. The paper will focus on the keyword searching issues of the current digital library system. The proposal is to choose a model that solves the issue using metadata case base and concept-based approach. The aim is to introduce a set of categories and concepts in the domain field that demonstrates some relations between them. As a result, poor quality of information retrieval in digital library system will be solved. The experiment has demonstrated that the already developed domain-specific ontology can be efficient for query advancement. Many researchers have used semantic retrieval technology using concepts to solve problems that lack semantics in traditional retrieval technology. Using concepts in ontology enhances search results.
Enhancing Cloud Performance Using File Format Classifications

Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM). The adopted approach is based on SVM one to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy. In CSO, grouping phase is used to divide the data files as audio, video, image, and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing. Overall, proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%, migration time by 9%, and optimization time by 10%, in the presence of competitor baselines.
Control and evaluation activity of primary schoolchildren and its formation in mathematics lessons

The article actualizes the problem of the formation of control and evaluation activities of junior schoolchildren in mathematics lessons, reveals the essence of the basic concepts of the study. Various aspects of control and evaluation activities are described. The problem of readiness to use modern means of assessment is considered. Methods and means of forming control and evaluative independence of primary schoolchildren are systematized. The problems that are encountered when using innovative assessment tools are identified, as well as the advantages of modern assessment tools in comparison with the traditional system. The stages of the formation of control and evaluative activity in younger schoolchildren, implying the development of evaluative independence, have been determined. Control and evaluation activities should be considered as an element of formative assessment, containing control and evaluation activities of the teacher and the student. The federal state educational standard of primary general education is focused on achieving the planned results: personal, metasubject and subject. When considering metasubject results, universal educational actions are distinguished, consisting of regulatory universal educational actions, including elements of control and assessment. During the formation of control and assessment, junior schoolchildren develop control and assessment independence. Until now, the assessment system was built within the framework of a subject-oriented approach, therefore it reflects only the result of mastering knowledge in each subject, and not educational and cognitive activities or, moreover, the assessment of personal and metasubject educational achievements of students. In the new assessment system, criteria are developed jointly by the teacher and students and are aimed at ensuring that students participate in assessment and assessment activities, thereby developing the skills of control, mutual control and self-assessment. The development of pedagogical science, the experience of Russia’s participation in international procedures for assessing the quality of education, the introduction and implementation of the Federal State Educational Standard and the resulting changes in the system for assessing learning outcomes determined the relevance of the topic under consideration.
A Covid-19 Response with Years in the Making: The Contribution of EAFIT University to Basic and Secondary Education in Colombia During the Pandemic

Abstract During the health emergency generated by Covid-19, EAFIT University has worked closely with the National Ministry of Education (MoE) and its Vice-Ministry of Preschool, Basic and Secondary Education, providing ideas and guidelines to 96 certified Secretaries of Education (In Colombia, Secretaries of Education are responsible for managing resources and activities for the educational sector at the province and municipality level. Province governments coordinate educational service in all their municipalities except those over 100,000 inhabitants, which can apply to be certified and become autonomous in the management of their resources) in Colombia and strengthening their capacities to autonomously address the emergency in educational institutions. This work includes the development of a national project focused on collectively defining the actions needed for the successful return to academic activities. This experience is derived from the development, since 2012, of the UbiTAG (The name UbiTAG derives from “ubiquitous learning,” followed by tecnología–aprendizaje–gestión (“technology”–“learning”–“management”). The label was introduced in Zea et al. (2012)) model, a holistic approach to digital maturity and change management in schools that has been implemented through ongoing long-term projects in the Itagüí municipality (24 schools) and the Bogotá Capital District (383 schools). This case study will focus on the work that EAFIT has been doing during the emergency, supporting both the MoE and specific Secretaries of Education. In addition, the case will describe some aspects of the UbiTAG model that have helped to address the educational communities’ current challenges.
Optical-Flow-Based Visual Servoing for Robotic Moving Control Using Closed-Loop Joints

This paper studied an optical-flow-based visual servoing method for eye-in-hand robotics task manipulation without system calibration. Firstly, considering the object feature points are easy to be lost with the traditional Lucas-Kanade(LK) optical flow method in the process of camera fast moving. We propose an improved pyramid optical flow tracking algorithm which iterates the vision image by layered, this algorithm obtains a hierarchy image by building a Gaussian pyramid, which can reduce the image motion speed and improve the tracking robust performance. In this work, we need to use the information obtained in the optical flow tracking process for visual servo control to drive the robot to complete the task manipulation, thus we use joints closed-loop controller and joint-image Jacobian matrix to construct a new visual servoing system for robotic motion control based on improved optical flow method. Finally, we have completed the visual servoing task manipulation on an eye-in-hand uncalibration robotics with six degrees of freedom (six-DOF). The experimental results show the effectiveness and robustness of the proposed scheme.
Document Classification for Recommender Systems Using Graph Convolutional Networks

Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles.
Solving Problems in Physics in the Training of Technical Engineers and Connecting Its Efficiency to a Competent Approach

In this article, a methodology for solving the following problems has been developed and applied in practice, with high efficiency achieved during the course.  This requires the active use of teaching methods, the widespread use of non-traditional forms and methods of education, the study of objects and phenomena on the basis of their interconnectedness, movement and development.  In particular, it is necessary to increase the activity of students in solving problems, the acquisition of new knowledge, the formation and development of their learning and skills, increase the efficiency and effectiveness of the process of studying physics in general.  Thus, it is ensured that the formation of personal competence of students as a result of education, which ultimately creates the necessary conditions for the full development of students at different stages of continuing education.