Reviewerly/exHarmony huggingface dataset:
PERBANDINGAN HASIL PEMERIKSAAN LED METODE WESTERGREN ANTARA SAMPEL DENGAN PENGENCERAN DAN SAMPEL TANPA PENGENCERAN

This research is motivated by the existence of laboratories that conduct examinations on EDTA blood samples without dilution. The sample taken is the blood of patients who carry out routine examinations in the laboratory of RSUD Syekh Yusuf, Gowa Regency. The purpose of this study is to find out whether there are results of the Westergren method of sedimentary blood examination (LED) for samples with dilution and samples without dilution. This type of research uses laboratory experiments with accidental sampling techniques. The results were processed through a computer using the SPSS program version 22 statistical test methods Paired Sample t Test where t-count (-6.554)> t-table (2.025) and a probability value of 0.000 <0.05 which means Ho is rejected. The conclusion of this study was obtained from the results of the study obtained in the examination of the blood sediment rate (LED) of the Westergren method between the sample with dilution and the sample without dilution (Ho rejected) so that for the LED examination the Westergren method of the sample used is EDTA blood, it is necessary to dilute the sample first.
Pose Detection of Object on Transmission Belt Based on R-SSD

The traditional object detection algorithm can only obtain the horizontal detection frame. In this paper, to solve the problem of large background noise and missing angle information in the detection frame, a bottle target detection model based on SSD (Single Shot multi-frame Detector) algorithm is proposed. The convolutional neural network is used as the feature extraction unit, which realized the gradient bounding box regression by introducing angle prediction. In order to improve the accuracy of the model, the feature pyramid structure is introduced into the feature extraction network, feature pyramid structure enables deep semantic information in the network to be combined with shallow layers. The prior box is determined by K-means clustering algorithm, it is used to make the network model more suitable for the bottle target. The experimental results show that the proposed algorithm is better than the original SSD algorithm, the AP value reaches 90.7%, and the real-time performance of the algorithm is also greatly improved. In summary, the position + size + angle simultaneous detection is more suitable for the target pose recognition application, such as grabbing the bottle in garbage sorting.
A Framework for Analyzing the Exercise and Athletic Activities

Exercise plays an important role in maintaining the health of a person. But doing exercise is not enough until it is done in a proper manner. Therefore, in this paper, we have proposed a framework for analyzing different exercise and athletic activities efficiently. The proposed approach is based on the Human Activity Recognition (HAR) which has been used for analyzing different activities related to exercising. In this paper, analysis has been carried out using the neural network classifier and has been carried out on a number of videos of different exercises. The videos are converted into frames for the methodical analysis of the videos. This framework is able to efficiently identify and distinguish the different exercises like flic flac, running, push up, pull up, sit up, somersault and the accuracy obtained is 88.53%.
Formación de la competencia «desarrollar sistemas web en los espacios virtuales de aprendizaje»

This work studied learning, adopting the cultural-historical approach, specially the theory of subjectivity as its psychological foundation. Major aspects of the design and creation of online courses, and the failure of these to consider learning as a subjective process were examined. Online courses which will be based on the theory of subjectivity are suggested as ways for eliminating this failure.
Thematic issue of Mechanics of Materials: Mechanics, chemistry, and thermodynamics of gels

A material parameter identification is presented for a fully thermo-mechanically coupled material model based on full field displacement and temperature measurements. The basic theory of the inverse problem is recapitulated, focusing on the choice of the objective function, proposing a new formulation, and explaining in detail the necessary numerical treatment of experimental data during the pre-processing of an identification. This includes the handling of the intrinsically different data sets of displacement (Lagrangian type) and temperature (Eulerian type). Experimental data is obtained by means of a Digital-Image-Correlation (DIC) as well as by a thermography system and three algorithmic boxes are provided for the necessary pre-processing. The experimental setup is discussed, measured data presented and analysed. From this setup, a successive approach to the identification process is motivated. Based on the experimental observations, a thermo-mechanically coupled material model is chosen, the required constitutive relations summarised and the material parameters interpreted. For the fixed choice of model and experiments, the inverse problem is solved. A very good fit was obtained for both the displacement and the temperature field. Results are interpreted and remaining errors discussed.
Design and implementation of intelligent assessment system for S700K switch transaction devices in rail transit

Switch transaction device is the equipment with the highest failure rate in rail transit signaling system. Switch equipment maintenance and fault handling is the core course of rail transit signaling specialty, and also the key technology to ensure the safety of rail transit transportation. With the rapid increase of rail transit mileage, train speed and traffic density, the increase of switch equipment usage frequency leads to the increase of daily maintenance and fault handling workload, which brings great pressure to safety production. Therefore, the ability to quickly diagnose and deal with switch faults is an important guarantee for the safety of rail transit transportation. In view of these reasons, it is particularly important to strengthen the training of operation skills of students and signaling maintenance personnel on the spot, and to improve their ability to deal with equipment failures in a short time. In this paper, an intelligent assessment system based on S700K switch machine is designed and implemented, which can effectively improve the switch maintenance and fault handling level of trainees through practical training and operation.
Students’ instrumental and integrative motivation in learning English

A number of studies have indicated that it is very impossible to successfully reach goals and good achievement without sufficient motivation, i.e: Mayer in Liu (2010), Williams and Burden in Kelly (2005), and Dornyei (1998). Brown (2000) then explains that in context of learning, there are two types of motivation. They are instrumental and integrative motivations. T his current research attempted to address relevant research questions related to: (1) type of motivation that the students have in learning English and (2) the students’ reasons related to their motivation in learning English. The findings revealed that the students have instrumental and integrative motivations. When being asked about the students’ reasons related to their motivation in learning English, it comprises from many aspects or indicators related to instrumental and integrative motivation. For reasons related to instrumental motivation, they learn English because it is useful in getting a good job in the future, to study or work abroad, to pass a test (school test or TOEFL/IELTS test) and to be able to read materials in English language for school assignments or competition material. Then for reasons related to integrative motivation, they learn English because they like the language, they would like to make friends with people from English-speaking countries, they are interested in English movies and songs, they enjoy talking in English and they want to understand English life style and culture as reference for them to increase their knowledge and insight about the outside world
SHCoT: Secure (and Verified) Hybrid Chain of Trust to Protect from Malicious Software in LightWeight Devices

Looking at the speed by which the software and the hardware evolve separately, there is no surprise that the interactions of the two may result in issues and appearance of back-doors to bypass the existing security. Lately, the hardware/software co-design gained lots of interest in both academia and industry, and proposed multiple hybrid solutions to enhance software/hardware interactions, security, and safety while guaranteeing good performance. In this paper, we focus on isolation and attestation to enforce the chain of trust in lightweight devices and detect malicious data and software locally and remotely. We present SHCoT, a hardware/software co-design to renew trust in devices. SHCoT is our first attempt to develop a formally verified hybrid solution to enhance existing solutions in the literature. While the work is still in progress, the first results show a partial verification of the security properties of SHCoT and small hardware/software cost.
Use of the Developing Potential of the Subject “Foreign Language” in the Conditions of Modernization of Primary School

The article reveals the importance of primary foreign language education for the personal development of a younger school child. The psychological and pedagogical conditions necessary for school children to achieve high educational results while mastering foreign language speech activity are considered. The role of the subject “Foreign Language” at the first stage of school education, which is determined by the purpose and functions of primary foreign language education, is analyzed.
Convolutional-Block-Attention Dual Path Networks for Slide Transition Detection in Lecture Videos

Slide transition detection is used to find the images where the slide content changes, which form a summary of the lecture video and save the time for watching the lecture videos. 3D Convolutional Networks (3D ConvNet) has been regarded as an efficient approach to learn spatio-temporal features in videos. However, 3D ConvNet gives the same weight to all features in the image, and can’t focus on key feature information. We solve this problem by using the attention mechanism, which highlights more effective features information by suppressing invalid ones. Furthermore, 3D ConvNet usually costs much training time and needs lots of memory. Dual Path Network (DPN) combines the two network structures of ResNext and DenseNet and has the advantages of them. ResNext adds input directly to the convolved output, which takes advantage of extracted features from the previous hierarchy. DenseNet concatenates the output of each layer to the input of each layer, which extracts new features from the previous hierarchy. Based on the two networks, DPN not only saves training time and memory, but also extracts more effective features and improves training results. Consequently, we present a novel ConvNet architecture based on Convolutional Block Attention and DPN for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better results than other slide detection approaches.
Comparative Analysis of Zero-Knowledge Proofs Technique using Quadratic Residuosity Problem

Zero-knowledge proofs is a type of non-black box security technique and it is stated by the means of quadratic residuosity problem in this paper. We have used quadratic residuosity problem (QRP) for zero-knowledge proofs security system for the comparative analysis of this technique in comparison with discrete logarithm problem in cryptography. The upper bound of QRP with a prime modulus algorithm for quadratic residuosity problem is growing faster in comparison with discrete logarithm problem used by Henry. After that covariance between two different algorithms for different problems are calculated and obtained result is positive, which means random variables used for both functions are growing in same direction which reflect the fact, they are similar in behavior. Both functions are growing in same direction and our functional valued curve converge with Henry (Discrete Logarithm) functional valued curve at a point.
A Clustering Approach using PSO Optimization Technique for Data Mining

The cluster analysis is incredibly huge conception of information mining several researchers square live offer attention on cluster downside. The cluster is member of unattended learning technique during that teacher is absent. throughout this analysis primarily based paper we've got developed the efficient hybrid data processing algorithmic rule. The obtained algorithmic rule known as as BKPSO that contain the Bisecting K-mean algorithmic rule and Particle Swarm optimization algorithmic rule. By victimization this 2 algorithmic rule the hybrid model can get. This hybrid model will increase the accuracy of clustering. The Bisecting K-mean algorithmic rule is use for the clustering. it'll kind the cluster suggests that organizing the article into cluster according their price that is analogous in how. The bisecting K-mean algorithmic rule is modification of the fundamental K-mean algorithmic rule. \{it can$\vert$it'll\cut the most cluster into 2 sub clusters till the K-cluster will kind. It reduced the computation time and additionally reduces the complexness of the information cluster. it's a drawback like initializing the beginning cluster center. to beat this downside and realize the optimum path the particle swarm optimization rule is used throughout this analysis based mostly project the information sets square measure transfer from the UCI Repository. it's one among the general public analysis based mostly University of Golden State in Irvine.
Locally Modified Laplacian Feature Mapping

In the age of big data, the amount of data is getting larger, and the complexity of data is getting higher. Manifold learning is designed to clean the data, eliminate the noise information, and extract the effective features of the data. Traditional manifold learning still has some shortcomings in terms of manifold assumptions and the complexity of algorithm design. This paper aims to design a new manifold learning algorithm designed to make up for the shortcomings of traditional algorithms. Here we mainly improved the algorithm LEM and proposed an improved algorithm LEM, referred to as MLEM. In the aspect of experiment, we introduced three groups of integrated datasets. The experimental results show that the algorithm MLEM is superior to the traditional manifold learning algorithm.
Video699: Interconnecting Lecture Recordings with StudyMaterials

Our work is a scientific poster that was presented at the MLPrague 2019 conference during February 22–24, 2019.Recording lectures is a common practice in the academianowadays and lays foundation to massive open online courseware.Although lecture slides are often recorded along with thelecturer, machine-readable information about the lecture slidesis rarely preserved. This prevents full-text search in therecordings and makes the lectures inaccessible to the blind andpartially sighted members of the audience. In our work, we present several neural architectures that workin lockstep to segment lecture recordings and to map theindividual segments to shown lecture slides. We also present anew dataset, which has been produced at the Masaryk Universityin Brno, Czechia, and which is used to train and evaluate oursystem. We evaluate the performance of the individual neuralarchitectures.
Proposing Automated Regression Suite Using Open Source Tools for A Health Care Solution

Regression testing is very important for the delivery of high quality product.It helps to simulate a suite of critical test cases periodically and helps to identify if introduction of any new features or any source code change has adversely affected the software quality or functionality.As a result, regression testing cannot be ignored from the software testing life cycle (STLC).But just doing a regression testing cannot be beneficial until it is accompanied by automation testing.Automated regression suite not only saves time and cost by re-running test scripts again and again but it also provide the confidence that all the critical test cases has been covered, providing more confidence in the quality of the product and increasing the ability to meet schedules.IT has an ability to explore the whole software every day without requiring much of manual effort.Current software is going through continuous development which requires testing again and again to check if new feature implementation has affected the existing functionality.In addition to this, it is facing issue in validation of the installation at client site and requires availability of testers to check the critical functionality of the software manually.This paper came up with the solution of creating automated regression suite for the software.The current research will provide guidelines to the future researchers on how to create an automated regression suite for any web application using open source tools.
Artificial Social Intelligence: Hotel Rate Prediction

Artificial Intelligence has enabled new possibilities in today’s business domain from operational efficiency to smart decision making and even innovative product/service design. Still there are plenty of grey areas where human modelers are struggling to create optimal machine learning scenarios. This research is the first attempt to build machine level structuration where the human modelers’ continuous commitment to enhance machine learning models can be eliminated. In Artificial Social Intelligence Framework, those requirements are replaced at the machine level by adopting cloud native computing foundation (CNCF) with continuous integration and development. The suggested machine level structuration is demonstrated with hotel rate predictions.
Mobile danger info system (MDIS)

Since the beginning of time, humankind has needed to confront danger that are not completely comprehended before he or she has left on a game-plan. Parents are very concerned about the safety of their children. So, being sensitive to the environment is very important to ensure their child's safety is guaranteed. Teenage children are the main pillars of the nation's development and modernization. They should be sensitive and cautious with unforeseen dangers that at any time may come. The main goal of this system to alert people about the danger that happened around them. This system will help local people to aware about recent danger. The other people who witnessed the incident allow to share the incident. Rapid Application Development (RAD) methodologies are selected to be used for the development of this project. The selected methodology use minimal planning in favor of rapid prototyping. RAD model is based on prototyping and iterative development with no specific planning involved. The life cycle consists of four phases, which are Requirements Planning, User Design, Construction, and Cutover. After development, The User Acceptance Test (UAT) is done, where actual software users test the software to make sure it can handle the required tasks in real-world scenarios, according to its specifications and implementations.
Ambient Intelligence In day to day life: A survey

There is a huge amount of data available in cloud, and mobiles connected to them, now connecting it to the physical world gives rise to internet of things. These things could be chair, table, clothes, vehicles, animals, plants, anything. The data gathered from these connected devices could be used to optimize and analyze data to build better and healthier environment around us, by combining internet of things with Ambient intelligence. Thus making the ambience intelligent i.e. Ambient Intelligence. We could connect with things, monitor things, and even search for items more easily. The developing field of Ambient intelligence is a comprehensive cutting-edge discipline. We have witnessed more practical and user-centered technologies like virtual and augmented reality, in the last decade. Now current trends are going to affect our lives socially and professionally. Here Ambient Intelligence (AmI) is an environment with electronic sensors that is responsive to human presence. It supports the design of intelligent systems and introduces sensors and processors to harmonize between people and machine. In this paper we are going to talk about AmI in health care industry, Smart Homes and also there use in Shopping Malls.
Retrofitting Soft Rules for Knowledge Representation Learning

Recently, a significant number of studies have focused on knowledge graph completion using rule-enhanced learning techniques, supported by the mined soft rules in addition to the hard logic rules. However, due to the difficulty in determining the confidences of the soft rules without the global semantics of knowledge graph such as the semantic relatedness between relations, the knowledge representation may not be optimal, leading to degraded effectiveness in its application to knowledge graph completion tasks. To address this challenge, this paper proposes a retrofit framework that iteratively enhances the knowledge representation and confidences of soft rules. Specifically, the soft rules guide the learning of knowledge representation, and the representation, in turn, provides global semantic of the knowledge graph to optimize the confidences of soft rules. Extensive evaluation shows that our method achieves new state-of-the-art results on link prediction and triple classification tasks, brought by the fine-tuned confidences of soft rules.
A New Framework for Secure Outsourcing of Medical Data

Nowadays security has become one of the challenging problems in almost all fields like IT, Banking, Medical, MNC companies, and many more. Cryptography and steganography are some of the few techniques used to provide the security for the text, images and many other multimedia data which is to be transferred over the network. Although the researchers are coming up with more and more enhancements in these fields, still there is a demand for new security challenges for the present-day digital data expansion. The limitation of the Cryptography is after encryption the ciphertext looks meaningless and it is vulnerable to the attacker and thus it raises a transmission interruption. On the other hand, the limitation of steganography is once the hidden information is revealed message is disclosed. The digital users regularly transfer a lot of files from one system to other system either within the range or far range by using the internet or intranet and simultaneously they also look for more security. In this work, a merged technique is proposed to improve the security in which first the message is encrypted using primitive cryptography algorithm—BlowFish and the obtained ciphertext is embedded into an image file using Least Significant Bit (LSB) technique. Here the medical image file is also considered as sensitive data and it is encrypted using a double embedding process with another cover image. This client-side encryption framework helps in providing secure outsourcing of medical data to a third party environment like any cloud service.
Survey on IOT Security and Challenges of IoT Forensics

Internet of Things a remarkably interrelated worldwide system structure is enhancing everyday lives, intensifying professional efficiency, advancing administration competence, besides the incline unbiassedly drives on. Nevertheless, this novel realism- IoT assembled grounded on Internet, comprises newfangled encounters from a safety and confidentiality viewpoint. Custom and outmoded safety measures too cannot be unswervingly functional to IoT know-hows. This survey focuses on contribution on a study of summary of numerous research works in the domain of IoT security and forensic present day challenges.