Reviewerly/exHarmony huggingface dataset:
The program of art therapy classes for the correction of child-parent relations

The presented article is devoted to the consideration of the possibilities of using art therapy methods in psychological work with families to correct child-parent relations. The object of the study is the child-parent relationship. The subject of the study is to identify the effects of the use of art therapy in the correction of child-parent relations. The article presents the results of an experimental study of the possibilities of correction of child-parent relations through the use of art-therapeutic techniques. The article contains a description of the program of art therapy classes, which was tested in working with a parent-child group. The description of the main art-therapeutic techniques when working with families, including the author's, is presented. The research methods were testing, questioning, psychological experiment, observation. The theoretical significance of the study lies in the systematization of theoretical provisions on the problem of research and substantiation of the possibilities of using art-therapeutic techniques in the psychological correction of child-parent relations. The practical significance of the study lies in the fact that the results obtained can be used by psychologists in working with families.The scientific novelty of the research consists in the development of a program of art therapy classes for the correction of child-parent relations. The results obtained in the study can have a significant impact on the expansion of ideas about the possibilities of psychological correction of child-parent relations through the use of art-therapeutic techniques. According to the results obtained, after the implementation of the set of measures, there is an improvement in child-parent relations: a decrease in the degree of rejection of the child; an improvement in emotional contact of the parent/child, a decrease in emotional distance; an increase in the authoritative strategy of family education; an increase in the indicators of parental attitude (acceptance), the degree of rejection of the child in the family has decreased; the manifestation of types of improper upbringing has decreased; there has been an optimization of parental installations.
Analysis of Credit Card Fraud Detection Using Machine Learning Algorithms

For the past decade of year’s credit card holders are facing a problem that the card had been swiped and cash has been withdrawn in an ATM (Automatic Teller Machine) or it has been swiped in a shopping mall by purchasing a product as creating a fake credit card. These transactions are considered as illegal activity, it is also one of the cybercrime theft activities. Credit card fraud detection has been increasing highly in the world. Fake credit cards can be tackled by applying data science along various machine learning algorithms. This research work focus on analysis of various credit card fraud transaction using machine learning algorithms. Also, this research focuses on detecting the fake credit cards by approaching Artificial Intelligence (AI), Data mining and Big data analytics etc. by using machine learning algorithms. Machine learning algorithms are applied by training the dataset which are collected from the fake credit dataset and original credit card dataset. Computer operations are handled by the data as data’s are ruling the world. Predicted card details are stored on the server of the banker. From the server information has been passed to the cybercrime in order to catch the culprit. Different kinds of machine learning algorithms that has been used for credit card fraud detection has been studied on which it gives the more precision.
Comparison of Attention Models and Post-hoc Explanation Methods for Embryo Stage Identification: A Case Study

An important limitation to the development of AI-based solutions for In Vitro Fertilization (IVF) is the black-box nature of most state-of-the-art models, due to the complexity of deep learning architectures, which raises potential bias and fairness issues. The need for interpretable AI has risen not only in the IVF field but also in the deep learning community in general. This has started a trend in literature where authors focus on designing objective metrics to evaluate generic explanation methods. In this paper, we study the behavior of recently proposed objective faithfulness metrics applied to the problem of embryo stage identification. We benchmark attention models and post-hoc methods using metrics and further show empirically that (1) the metrics produce low overall agreement on the model ranking and (2) depending on the metric approach, either post-hoc methods or attention models are favored. We conclude with general remarks about the difficulty of defining faithfulness and the necessity of understanding its relationship with the type of approach that is favored.
Measured high-resolution directivities of guitar amplifiers

Guitar amplifiers comprise specialized powered loudspeakers with distinct configurations and directivity characteristics. Recently, the authors measured the directivities of six guitar amplifiers using a new measurement system. The system employs a rotating semicircular microphone array in an anechoic chamber, with 2522 unique microphone positions over a surrounding sphere (i.e., 5-deg resolution in both the polar and azimuthal angles). This presentation will describe the measurement and processing procedures and compare the measured results to those produced by theoretical directivity models. An online archival database provides easy access to the guitar amplifier data and other directivity data from the human voice, played musical instruments, and other sound sources. The data are available in several commonly used file formats and should provide valuable information for many applications, including spatial audio, architectural acoustics modeling, and microphone placement.
SaaS application mashup based on High Speed Message Processing

Diversified SaaS applications allow users more choices to use, according to their own preferences.However, the diversification of SaaS applications also makes it impossible for users to choose the best one.Furthermore, users can't take advantage of the functionality between SaaS applications.In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment.The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework.Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques.We present the theoretical basis of implementing the high-performance messageprocessing function using Kafka.The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function.The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.
Analisa dan perancangan berorientasi objek pada sistem informasi geografis untuk menentukan lokasi irigasi sungai

The government in Indonesia is starting to change the perspective so that all levels of government use a computerized system called e-government. Especially in the government environment in West Sumatra Province. Currently, West Sumatra Province is still far behind other provinces that have utilized information technology. Therefore, the Province of West Sumatra tries to utilize technology for existing agencies throughout the province of West Sumatra. One of them is at the Water Resources Management Office of West Sumatra Province. The application technology needed by PSDA is a geographic information system wherein building the application requires a work system framework design method, OOAD, the modeling uses UML and uses the PHP programming language as well as storage media for all irrigation data and coordinates using a MySQL database so that all applications will be implemented. built to make it easier for the PSDA to enter website-based coordinate data.
Monocular Camera Calibration using Projective Invariants

Camera calibration is a crucial step to improve the accuracy of the images captured by optical devices. In this paper, we take advantage of projective geometry properties to select frames with quality control points in the data acquisition stage and, further on, perform an accurate camera calibration. The proposed method consists of four steps. Firstly, we select acceptable frames based on the position of the control points, later on we use projective invariants properties to find the optimal control points to perform an initial camera calibration using the camera calibration algorithm implemented in OpenCV. Finally, we perform an iterative process of control point refinement, projective invariants properties check and recalibration; until the results of the calibrations converge to a minimum defined threshold.
Design and Build Web and API on “Absenplus” with Face Recognition using Deep Learning Method

This research has background cause have not maximum yet of attendance’s system for now. “Absenplus” is an application attendance android based which has two features of system such as face recognition and geolocation. With technology who can help for developing “Absenplus” with design and build web and API as a web server who belong to integration into “Absenplus”’s application. So therefore the author decides to named “Design and Build Web and API on “Absenplus” using Deep Learning’s methods” to give a integration database to “Absenplus” apps. This research will take advantages of computing library of deep learning named TensorFlow and Keras. Besides, this research uses MTCNN for detection face image, Facenet Model to help model gets the extraction feature, and SVM for classification model image train and test. In geolocation’s system use geofence library to help development function geolocation’s system. This research also use Laravel framework in design and build web and API. Throughout this research give the results on “Absenplus” that user can use attendance online with face recognition and geolocation. In this result of face recognition, it can be conclude that average of predict probability is 67% with light room normally.
Weighted constrained based optimization for secured medical data using Vertex Magic Total Labeling of Complete Graphs along with RSA algorithm

Medical image security is used by many suppliers for healthcare systems such as hospitals, medical practitioners, doctors, etc. the main aim of the research is to secure the medical images using the private key access for the secured images. The identification of white cell in images of microscopic is so tedious time taking and the results depends on experience of hematologist. The convolution neural network (CNN) segments the cytoplasm and nucleus of WBCs from the microscopic, spectral and hyper spectral images and classifiers them as white cells with nucleus. These diagnosed with B- lineage Acute Lymphoid Leukemia(B-ALL) and Multiple Myeloma (MM) images are secured using private RSA key generation using Vertex Magic Totaling Labeling (VMTL). The proposed CNN based weighted constrained based optimization showed accuracy of 93%.
Loan Approval Prediction Model Using Customer Behaviour

Nowadays bank and bank data are very important. In this covid-19 era, people need money a lot, so people are applying for more loans now than before. It is necessary but it is difficult to check all this. Often wrong selections of data can result in loss to the bank. If this data is accurate the bank gets profit. Banking industry always needs a more accurate predictive modelling system for many issues. The bank can earn money from the interest on the credit card used in the loan approval process. All this require a real based application model to be accurate and this model is made using machine learning (ML). In India, the large number of people applying for the loans for various purpose of reasons. The bank employees are not able to analyses or predict whether the customer can payback the amount or not (good customer or bad customer) for the given interest rate. Besides of Credit Score, we believe there are other factors which should be taken in consideration while taking a decision on loan approval. This model is capable of doing that using machine learning algorithm to predict that whether a customer should be provided loan or not.
Supporting Users’ Decision-Making Experiences through Hyper-Personalized Human-Technology Interactions

Influencing user behavior has been increasingly stated as an explicit design objective in HCI and design research. However, designers often face difficulties in their practices resulting from users’ different preferences about how they are guided to perform certain behaviors when using interactive systems. To tackle this issue, this doctoral research focuses on the concept of users’ different decision-making styles and hyper-personalization. Using Research through Design (RtD) as an overarching approach, this research develops an understanding of how users’ different decision-making tendencies affect their experiences in technology use and the implications of these tendencies for design (Thread 1). This research also explores how users’ behavioral decision-making experiences can be supported through design, especially at the intersection of user experience (UX) design and behavioral intervention technology (Thread 2). Further, this research develops a new approach to supporting users’ daily decision-making in technology use by conducting a series of deployment studies (Thread 3).
Solution to the problem of minimum distance detection of various objects in shallow water depth

Object and purpose of research. The object of research is arrangement of various items on the bottom of water bodies. The purpose of research is achieving maximum invisibility for such items. Anticipated search or accidental detection of bottom objects can be carried out by the bathymetric method, i.e. assessment of water column over such objects. Materials and methods. It is expected that low flying UAVs (unmanned aerial vehicles) equipped with bathymetric laser emitter are used for detection of underwater objects. With consideration of some simplifications, optimization is carried out for the operation of bathymetric laser detector of objects located on the sea bed. The task of detecting an underwater object is considered to be solved when the difference between the signals reflected from the sea bed and underwater object is reliably recorded. Minimum external detectability is achieved at minimum of the said difference. Main results. An objective functional is obtained to characterize the total signal from multiple underwater objects. As a result of performed optimization, the condition is determined at which the minimum of the objective functional is achieved. According to the obtained result, the total signal reflected from an underwater object reaches its minimum if the height of underwater objects and their reflection coefficient vary paraphase, i.e. the growth of one is accompanied with the reduction of the other. Conclusion. A method for minimum detectability of objects stacked on the sea bed is suggested and validated. Practical procedures of storing various objects on the sea bed are defined, which ensures their invisibility for the bathymetric laser detector onboard the low-flying UAV.
A Driving Assistance System for Solving A-Pillar Blind Spots Through Gaze Detection and Field-of-View Estimation

The blind spots brought by a car’s A-pillar are main reasons of most of accidents. In this work, a vision system that focuses on eliminating A-pillar blind spot of a car without any affection of driver’s operation is investigated. The driver’s facial features are captured by a binocular vision system that is mounted on A-pillar, head poses and gaze line directions are reconstructed. The generated blind spot by A-pillar is then simultaneously calculated according to the position of driver’s gaze. A field-of-view of the blind spots is displayed in a screen system mounted on the A-pillar. The screen conjointly with front and side windows thus provides a full view field for the driver, which can effectively reduce the occurrence of accidents.
Comparing Methods for Extractive Summarisation of Call Centre Dialogue

This paper provides results of evaluating some text summarisation techniques for the purpose of producing call summaries for contact centre solutions. We specifically focus on extractive summarisation methods, as they do not require any labelled data and are fairly quick and easy to implement for production use. We experimentally compare several such methods by using them to produce summaries of calls, and evaluating these summaries objectively (using ROUGE-L) and subjectively (by aggregating the judgements of several annotators). We found that TopicSum and Lead-N outperform the other summarisation methods, whilst BERTSum received comparatively lower scores in both subjective and objective evaluations. The results demonstrate that even such simple heuristics-based methods like Lead-N can produce meaningful and useful summaries of call centre dialogues.
Potentials and challenges for mega events: mobile devices usability for virtual reality, augmented reality, and visual effects

Due to the rapid technical development of computer and internet technologies, more and more new applications are becoming possible. Virtual Reality (VR), Augmented Reality (AR) is very often realized with technologies from the field of visual effects, for example, through real-time rendering, artificial intelligence, and high-quality 3D assets. As part of the digital transformation, VR and AR have also been developed for the event industry in recent years, especially for mega-events. As a result, the technologies are increasingly being used. However, processing on mobile devices in these qualities, like smartphones, can still be a challenge, but more and more solutions are being described and developed. On the occasion of the planned 300th anniversary of Immanuel Kant's birth in 2024, there are excellent visualization possibilities for AR applications, for example, the creation of 3D models.
Instance level analysis on linked open data connectivity for cultural heritage entity linking and data integration

In cultural heritage, many projects execute Named Entity Linking (NEL) through global Linked Open Data (LOD) references in order to identify and disambiguate entities in their local datasets. It allows users to obtain extra information and contextualise the data with it. Thus, the aggregation and integration of heterogeneous LOD are expected. However, such development is still limited partly due to data quality issues. In addition, analysis on the LOD quality has not sufficiently been conducted for cultural heritage. Moreover, most research on data quality concentrates on ontology and corpus level observations. This paper examines the quality of the eleven major LOD sources used for NEL in cultural heritage with an emphasis on instance-level connectivity and graph traversals. Standardised linking properties are inspected for 100 instances/entities in order to create traversal route maps. Other properties are also assessed for quantity and quality. The outcomes suggest that the LOD is not fully interconnected and centrally condensed; the quantity and quality are unbalanced. Therefore, they cast doubt on the possibility of automatically identifying, accessing, and integrating known and unknown datasets. This implies the need for LOD improvement, as well as the NEL strategies to maximise the data integration.
A ResNet based multiscale feature extraction for classifying multi-variate medical time series

We construct a deep neural network model named ECGResNet.This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy.We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively.We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method.This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes.The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age.The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting.The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.
PENGGUNAAN APRIORI PADA REKOMENDASI PAKET MENU DAN DILENGKAPI FITUR CHATBOT

The growth of the food and beverage industry is the pillar of national and manufacturing economic growth. At present the food and beverage sector continues to be excellent, especially in the tourism industry sector which develops culinary tourism. In the sale of food and beverages in the world of tourism, it is made to attract the attention of consumers by making promotional packages. This promotional package aims to attract buyers to want to buy products from a restaurant. One way to make a promotion is by using association rules. Association rules are the results that will issue a combination of numbers from each transaction data. This was done to help increase the sales of Ling Ling Restaurant. To find the results of association rules whose combinations are used for promotion, here we use the Apriori method. The results that come out will be in the form of a food or drink menu that has been combined into two, three, or more according to the number of association rules that came out by going through the Apriori method. The results of the association rules that come out later will be displayed on the chatbot display contained in the program. The chatbot here will display the results of the association rules obtained using the Apriori method and also display several question options using questions that have been created in the chatbot.
Pedagogical counseling in the process of development the subject-professional potential of cadets in an educational organization Ministry of Internal affairs of Russia

The article examines the importance of pedagogical consulting in the process of developing the subjective and professional potential of cadets in an educational organization of the Ministry of Internal Affairs of Russia. The purpose of the study is to substantiate and reveal the conditions of pedagogical counseling based on helping cadets develop their abilities for self-knowledge, overcoming difficulties that arise, and a responsible and conscious attitude towards their lives. Scientific novelty lies in the development of theoretical approaches and practical recommendations of the main directions of pedagogical consulting in the educational process of a departmental university. As a result, stages were identified that contribute to the identification of existing difficulties; analysis of events that affect the behavior of cadets and require the advice of a teacher; modeling and consideration of the reasons that prevent the positive solution of emerging problems.