Reviewerly/exHarmony huggingface dataset:
Liver Segmentation: A Weakly End-to-End Supervised Model

Liver segmentation in CT images has multiple clinical applications and is expanding in scope. Clinicians can employ segmentation for pathological diagnosis of liver disease, surgical planning, visualization and volumetric assessment to select the appropriate treatment. However, segmentation of the liver is still a challenging task due to the low contrast in medical images, tissue similarity with neighbor abdominal organs and high scale and shape variability. Recently, deep learning models are the state of art in many natural images processing tasks such as detection, classification, and segmentation due to the availability of annotated data. In the medical field, labeled data is limited due to privacy, expert need, and a time-consuming labeling process. In this paper, we present an efficient model combining a selective pre-processing, augmentation, post-processing and an improved SegCaps network. Our proposed model is an end-to-end learning, fully automatic with a good generalization score on such limited amount of training data. The model has been validated on two 3D liver segmentation datasets and have obtained competitive segmentation results.
Log Message Anomaly Detection with Oversampling

Imbalanced data is a significant challenge in classification with machine learning algorithms.This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced.In this paper, a model to generate text log messages is proposed which employs a SeqGAN network.An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network.The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird.Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.
The way of ensuring resistant to imitation transmission information via communication channels

H&ES Research is published since 2009.The journal covers achievements and problems of the Russian infoсommuniсation, introduction of the last achievements of branch in automated control systems, development of technologies in information security, space researches, development of satellite television and navigation, research of the Arctic.The special place in the edition is given to results of scientific researches of young scientists in the field of creation of new means and technologies of space researches of Earth.
Sketch-to-Art

Sketch-to-Art is an AI tool that allows creatives to sketch an idea and get fully rendered images, stylized the way they want in real time. Users can define a style by either choosing a reference image, or a group of images, and selecting an artist, or an art movement.
Compound Chunk: Telegram Autobot Quiz to Improve Spelling on Compound Nouns

The importance of integrating mobile technology in assisting language learning is becoming more popular in the learning of ESL.Telegram is one of the messaging applications that is not as widely used as other messaging applications and can actually be used as an educational tool.Therefore, this is an action research focuses on improving the pupils' spelling skills in compound nouns words through the usage of Telegram Autobot Quiz.The participants of this research were 30 Year 5 pupils from three primary schools in three different states of Bandar Baru Bangi,Selangor , Seri Iskandar,Perak and Alor Gajah,Melaka.The "Compound Chunk" through Telegram Autobot Quiz had been used as intervention method in improving pupils' spelling compound nouns.Pupils were required to play quiz on "Two Pictures One Word","One Picture One Word" lastly two rebus activities as post-test.The findings of the research were analysed qualitatively using the Triangulation method of observation's checklist, interview and document analysis.The findings indicated that there had been an improvement in the participants' spelling performance after the intervention and gave positive opinions concerning the use of the quiz.In conclusion, the quiz
A Diary Study on the Exercise Intention-Behaviour Gap: Implications for the Design of Interactive Products

Increasingly aware of the importance of active lifestyles, many people intend to exercise more.One of the main challenges is to translate exercise intentions into actual exercise behaviour, the so-called intention-behaviour gap.To investigate barriers and enablers that affect this gap, we conducted a 7-day diary study with 16 participants.In this study, participants indicated what their exercise intentions and behaviour were per day, and whether and why they changed retrospectively during the day.Through the diary study, we gain insights into (i) the intention-behaviour interplay, and (ii) the experienced barriers and enablers that influence this interplay throughout the day.Based on the findings, we contribute new implications for design in supporting people translating their intentions into exercise behaviour, and propose three design concepts as examples.In these, the focus is on positively influencing the interplay of enablers and barriers of exercising and how these can be exemplified through design.
Integrating Technology in Training: System Dynamic-Based Model Considerations Regarding Smart Training and Its Relationship with Educational Components

Dissociating training and education is not the best approach when we refer to the integration of the technological novelties, because, up to a point, these two components of the educational system are closely related. However, their partially-different target group separation leads to a different approach of these group constituents regarding receptivity to technological integrations in educational processes. Based on the particularities of the training market and its dynamic, in this paper we present a close image of the training system by representing its main components and the impact of the technology both in the training process and the training curricula. The model construction is based on a survey conducted on a training platform and built using system dynamics principles. Finally, links with the two main output groups of the training market and the lower education are made using the same principles.
Pengaruh Strategi Guru Terhadap Minat Belajar Siswa Kelas IX dalam Pembelajaran PAI di MTs Annurain Lonrae

This thesis discusses about the influence of teacher strategy in learning PAI toward the interest of student of the 9th grade in MTs. Annurain Lonrae and has purpose of this research to analyze the influence of teacher strategy on students learning interest of 9th grade in PAI learning at MTs. Annurain Lonrae. This research is field research by using mixed method. It uses pedagogical, psychological and sociological approach. The population is all 9th grade students in MTs. Annurain Lonrae. Data are collected by observation method, questioners, interview, documentation, and reference searching. The data processing technique is done by summing the score and making the table of teacher strategy in PAI learning and students interest in MTs. Annurain Lonrae, and then the data is processed and analyzed by using simple linear regression test through statistical product and service solution (SPSS) version 18. The results showed that student center learning strategy has been implemented well by teachers in learning, although not yet optimal. Students learning interest focused on the learning process of PAI has started to look better than before. Proven with the acquisition of questionnaires that have been processed and analyzed by researchers using SPSS showed that the score of t equal to 5831 with the value of significant (Sig. score 0.05) then it can be concluded H0 rejected and H1 accepted, it means there is a significant effect of student centered strategy to students learning interest in learning PAI in MTs Annurain Lonrae.
ARgent: Web Tabanlı Dinamik İçerik Destekli Artırılmış Gerçeklik Geliştirme Altyapısı

In the modern world, people are more and more interested in interactive technologies.Despite the usefulness of augmented reality, which is a novel addition to those interactive technologies, its development still requires knowledge and experience with programming and game development tools gained by long-term education and training.People experienced in design and content creation can be deprived of the ability to create and maintain AR applications.In this paper, ARgent, an AR authoring tool developed in Unity will be introduced.This framework will supply the whole workflow pipeline which involves modules targeting the tree fronts: the server, the web interface and mobile application.The server will be responsible for packaging and optimizing objects, doing database processes and serving the data to the web interface and mobile application.The web interface will be used for content management as web applications are easy to use and easily accessible.The mobile application is the application that will be used by the end user.ARgent will provide a way to create applications entirely in a Graphical User Interface (GUI) without needing to write any line of code.The User eXperience (UX) will be familiar to the existing tools to keep users engaged with the development process.ARgent provides a way to import dynamic assets, create animations and optionally create scripts for custom behavior, all within its web interface.ARgent features "asset bundling" method, a novel way to optimize and load dynamic assets, which eliminates performance issues arising from using dynamic assets in applications built with Unity.Finally, a JavaScript engine is added to the framework so that users can write their own scripts enabling them to create custom user interfaces and to assign behaviors to them.
On the Issue of Compliance with Didactic Principles in Learning using Augmented Reality

Augmented Reality, as one of the advanced technologies, in-creases the range of applications in various spheres of life every year. The evo-lution of the development and application of augmented reality in various spheres has shown its effectiveness also for the sphere of education. The analysis of varied studies shows the expanding penetration of augmented reality in education, while noting the positive impact of augmented reality on the quality of education. However, the didactic approach to the application of augmented reality in education has not yet been sufficiently studied. The article considers the use of digital educational resources with augmented reality as a didactic teaching tool. In addition, a system of didactic principles for learning using augmented reality is proposed.
Improving on Coalitional Prediction Explanation

Machine learning has proven increasingly essential in many fields but a lot obstacles still hinder its use by non-experts. The lack of trust in the results obtained is foremost among them, and has inspired several explanatory approaches in the literature. These approaches provide a great insight on the predictions of a model, but at a cost of a long computation time. In this paper, we aim to further improve the detection of relevant attributes influencing a prediction, on the strength of feature selection methods.
The Effectiveness of Spaced Learning as a Pedagogical Strategy in Enhancing Student Learning and Motivation

The aim of this study is to explore the effectiveness of Spaced Learning technique as a teaching and learning method for students in Higher Learning Institutes (HLIs).Amidst the rapidly changing technological environment, creating long-term memories is still at the core of education.Spaced Learning is a learning method in which highly condensed learning content is repeated three times, with two 10-minute breaks during which distractor activities such as physical activities are performed by the students.It was suggested that spacing learning over time helps people learn more quickly and remember better.This study examines the impact of using Spaced Learning technique on students' performance and motivation to learn.A quasi-experimental research design was implemented.Two groups of students were chosen, and both were taught on the same pre-identified topic, albeit using two different methods: 1) the conventional way, and 2) using Spaced Learning technique.A pre-test and post-test were conducted to both groups.The students' motivation and performance results were analysed and discussed.
A Machine Learning Approach for Temporal Vibration Analysis Applied to Predictive Maintenance

The operational condition of a machine affects the quality and efficiency of its work, and letting a problem arrive in a critical state results in negative consequences, which can cause equipment loss and extensive downtime in a factory.The maintenance of machines, therefore, is a concern that came to exist along with the creation of the industry.This work shows the development of a platform that takes advantage of recent advances in sensor technology and machine learning to assist the predictive maintenance process, identifying problems in advance before serious failures can occur.The work proposes a supervisory system which receives high frequency vibration data, stores it, and analyzes the functioning of a machine to classify its behavior as normal or anomalous, generating alerts.The results achieved show that it is appropriate to use machine learning to monitor machines, since well-structured algorithms can detect possible problems before they become apparent to humans.
A Systematic Literature Review of Recommender Systems for Requirements Engineering

Requirements Engineering (RE) is the first phase of a software project development.This phase aims to help project stakeholders discover, analyze, and specify the needs for a software project.In complex projects, requirement engineers deal with substantial requirements specifications and a large number of stakeholders.Studies have shown that poorly implemented RE processes are one of the primary causes of project failures including cost/schedule overruns, and failure to deliver promised functions.On the other hand, Recommender Systems (RSs) are software tools that support users in the recognition of appropriate items in a context where the amount of an assortment exceeds their capability to reach a decision.Therefore, RSs are needed to support several processes in requirements engineering.In this paper, the utilization of RSs in RE is examined by the use of a Systematic Literature Review (SLR) through the years 2010 -2019.The results show how recommender systems can support several processes in requirements engineering.Finally, the utilization of recommender systems in requirements traceability is suggested as future work.
Information System for Railway Inspection using Drone and Image Processing

Train is the main mode of transportation that we often use, the train itself can be used as a tool for shipping goods and mobilizing passengers, this transportation is very unique and has its own path in the form of steel strings across from hundreds of kilometers, railroad bearing structures currently exist which uses concrete and wood, the railroad is very vital and is an important supporting facility. The process of railroad monitoring is complex and complicated, takes a long time, the previous method is simple and conventional by tracing the railroad tracks manually or using a geometric gauge mounted direl or also known as railpod, railpod will follow the rails and will provide report if there is a train track that is damaged, broken or shifted, this research will create an image-based monitoring system using drones as a track monitor, another way is to take pictures using satellite data that will provide clear information about road conditions before being passed by the train, the railroad data processing system by using image processing can display visual responses up to cm in size, the response appears if there is a shift in the path then the system directly provides data in the form of location and shifting paths on the main computer, this system is more c eTat in checking and analyzing train track data with high accuracy and precision up to 90%, in addition to imagery from satellite images can use drones, drones themselves are very easy in maintenance and use and are able to cut production costs and even workplace accidents in the field workers themselves can be avoided because the drone is able to reach the track and railroad that is difficult for example through the tunnel or the railroad track along the hills and densely populated.
Penerapan Model Pembelajaran TGT Untuk Peningkatan Hasil Belajar Materi Bilangan Romawi Siswa Kelas IV Sekolah Dasar

This study is aim to describe improving student learnng outcomes using models teams games tournament (TGT) on the material of Bilangan Romawi the fourth grade students 06 sintang academic year 2016/2017. The method used in this research is descriptive research method with qualitative approach, in the form of classroom action research. This study was conducted in two cycles. Each cycle consists of the stages of planning, action, observation, reflection. Data retrieval tool is used in the form of guidelines for obsevation, test question, questionnaire, documentation. Based on the research results improving student learning outcomes trough teams games tournament (TGT) models in the first cycle with a total percentage of 63,33%, while in the second cycle a significant increase, amounting to 90,90%. This shows the improvement of student learning outcomes from the first cycle to the second 27,57%.
DETECTION AND LOCALIZATION OF TRAFFIC LIGHTS USING YOLOV3 AND STEREO VISION

Abstract. This paper focus on traffic light distance measurement using stereo camera which is a very important and challenging task in image processing domain, where it is used in several systems such as Driving Safety Support Systems (DSSS), autonomous driving and traffic mobility. In this paper, we propose an integrated traffic light distance measurement system for self-driving based on stereo image processing. Therefore, an algorithm to spatially locate the detected traffic light is required in order to make these detections useful. In this paper, an algorithm to detect, classify the traffic light colours and spatially locate traffic light are integrated. Detection and colours classification are made simultaneously via YOLOv3, using RGB images. 3D traffic light localization is achieved by estimating the distance from the vehicle to the traffic light, by looking at detector 2D bounding boxes and the disparity map generated by stereo camera. Moreover, Gaussian YOLOv3 weights based on KITTI and Berkeley datasets has been replaced with the COCO dataset. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes an integrated method for improving the detection accuracy and traffic lights colours classification while supporting a real-time operation by modelling the bounding box (bbox) of YOLOv3. The obtained results show fair results within 20 meters away from the sensor, while misdetection and classification appeared in further distance.
Content Quality of Lesson Scenarios in Moscow E-School

This work is aimed at assessment of content quality of lesson scenarios in Moscow e-school, comparison of expert data with the features of application of these scenarios in order to reveal strong points and deficiencies in content of lesson scenarios. The considered lesson scenarios are characterized by high quality of material component of educational content. The lesson scenarios substantiate forms and types of control of educational results. Methodological support of the lessons, differentiation and individualization of the content, existence of nonstandard situations stimulating personal participation of students, instructions for a student have been manifested least in the lesson scenarios. High expert quality appraisals of lesson scenarios mostly do not coincide with the popularity of lesson scenarios among the users.
Web Service QoS Classification Based on Optimized Convolutional Neural Network

How to increase the performance of Web service classification is one of the hot topics in current service classification research. Web service classification approach based on traditional machine learning and deep learning algorithm are greatly influenced by the data sparsity and bad data scalability, the increase of the amount of data will affect the classification performance. At the same time, the classification result is not accurate due to the change of quality of service (QoS) without considering the time factor. Aiming at the above-mentioned problems, this paper proposes a Web service quality classification method based on convolutional neural network algorithm. The main contributions of this paper are as follows∶ (l) Based on the collaborative filtering algorithm of service, web service recommendation is made from the similarity between services themselves by considering QoS parameters. The time factor of service is considered in the classification process, achieving higher classification performance. (2) A service quality classification method based on VGG-16 algorithm is proposed. In this method, GlobalAveragePooling2D replace the full connectivity layer of the CNN, which reduces the network parameter overabundance caused by too many fully connected layers. Also, unlike the full connectivity layer, which requires a lot of training and tuning parameters, GlobalAveragePooling2D reduces spatial parameters, and its local connections, weight sharing, and pooling operations have fewer connections and parameters, making it easier to train. In this paper, the optimized network and the typical machine learning algorithm and deep convolutional network are tested on the WS-DREAM data set. Experiments show that compared with other classifiers, the CNN presented in this paper has the highest balance accuracy score in experiments. The classification accuracy of the optimized CNN classifier is 98.88% and the balance accuracy score is 99.27%.
NATURE AND METHODS OF COMMITTING FRAUD USING INFORMATION-TELECOMMUNICATION TECHNOLOGIES

The article is devoted to the consideration of issues relating to the nature and methods of committing fraud using information and telecommunication technologies. The article considers and analyzes official statistics of crimes on the global Internet for 2019, as well as material damage caused by these crimes. The concept of a method of fraud is disclosed and its most important role in the characteristics of Internet fraud is determined. Particular attention is paid to the most popular methods of committing the crime in question, such as: phishing, Nigerian letters, creating a personal business, online stores, magic wallets, free communication, improved MMM; their characteristic features are indicated and promising areas for studying the above topic are highlighted. In order to systematize the data, the author proposes the division of the considered methods into groups. The article also analyzes new, not yet fully explored, methods of fraudulent activity on the Internet, and offers protective measures to prevent the threat of information and telecommunication fraud.