Reviewerly/exHarmony huggingface dataset:
Analysis of observing and recognition profile facial images using eye tracking system

Facial images have been the subject of research for many years, using the eye-tracking system. However, most researchers concentrate on the frontal view of facial images. Much less research has been done on faces shown at different angles or profile views of faces in facial images. However, as we know, in reality we often view faces from different angles and not just from a frontal view. In our research we used a profile presentation of facial images and analyzed memory and recognition depending on the display time and dimensions of the facial images. Two tests were performed, i.e. the observation and the recognition test, and we used the well-known yes/no detection theory. We used four different display times in the observation test (1, 2, 4 and 8 seconds) and two different dimensions of facial images 640 × 480 and 1280 × 960). All facial images were taken from the standardized face database Minear&amp;Park. We measured the recognition success which is mostly presented as a discrimination index A’, incorrect recognition (FA – false alarm) and time-spatial method based on fixation duration and saccade length. In this case, eye tracking provides us with objective results when viewing facial images. In the results it was found that extending the display time of facial images improves recognition performance and that the dependence is logarithmic. At the same time, wrong recognition decreased. Both parameters are independent of the dimensions of the facial images. This fact has been proven by some other researchers also for frontal facial images. It was also discovered that with an increase of the display time of facial images an increase of the fixation duration and saccade lengths occurred. In all results we detected major changes at the display time of four seconds, which we consider as a time, where the subjects looked at the whole face and their gaze returned to the center of the face (in our case eye and mouth).
A Novel Computer-aided Breast Cancer detection method based on Convolutional neural network

Breast cancer is one of the main causes of cancer death worldwide.The diagnosis of cancer from eosin stained images is non-trivial and specialists often disagree on the final diagnosis.Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process.Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge.To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives.A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed.Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma.The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization.This design allows the extension of the proposed system to whole-slide histology images.Accuracies of 77.8% for four classes is achieved.The sensitivity of our method for cancer cases is 95.6%.
Utilization of Augmented Reality as an Interactive Media in The Learning of Fine Arts in Elementary School Education Students

Utilization of Augmented Reality (AR) as interactive learning media can stimulate students' mindset in thinking critical because AR can visualize abstract concepts for understanding and the structure of an object model. This allows AR as a more effective medium. Augmented reality applications show good potential in giving students more active, effective and meaningful learning. Augmented Reality (AR) can be defined as a technology that can combine virtual objects two-dimensional or three-dimensional into a real environment and then bring it or project it in real time. The purpose of this study was to describe the ability of students to appreciate the results of fine art. It also aims to examine user acceptance of augmented reality applications within an art learning at primary teacher education program, from students’ opinion when AR is used in class. The research design was descriptive. The study was conducted with 30 primary school teacher education students in Universitas Sebelas Maret. Data collection techniques used in this study are portfolio and questionnaire. The results show that (1) 75% ability of students’ appreciation in appreciating art works is very good, especially in the stages of analysis and assessment, (2) Students’ opinion about AR media 87.66% said it was beneficial for art learning, because AR can motivate to produce creative works of art. These results indicate that the application of AR as an interactive learning media can improve the ability to appreciate fine art
LibGuides: It's a Doggy World: Working Dogs

Hi everyone, wow this experience of creating a LibGuide has been so interesting and rewarding to me. I am an avid animal lover, and it saddens me to know how many animals that people have brought into their lives and then discovered how much work one take
Static and configurable tabs

In the previous chapter, we created a static tab to get hands-on experience with the Teams Client JavaScript SDK. In this chapter, we will explore more of the different Tab options. We will see what specific items we need to take into consideration when creating our own Tabs. Let us navigate through the inner workings of Teams Tabs starting with how they are constructed.
Microring resonator based single qubit unitary for photonic quantum information processing

Here we propose an architecture based on microring resonators that implement arbitrary single qubit unitary transforms (i.e., gates) useful in path encoded photonic quantum information processing. The proposed device offers improved scalability in terms of size and operation when compared to the Mach-Zehnder Interferometer approach.
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model

Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents.Moreover, it requires paired training data that are usually expensive to collect.Template-based methods have good fidelity and controllability but require heavy human involvement.We propose a novel template-based data2text system powered by a text stitch model.It ensures fidelity and controllability by using templates to produce the main contents.In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability.The text stitch model can be trained in self-supervised fashion, which only requires free texts.The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.
Казахстан — Россия: библиотечное сотрудничество без границ

The article describes the forms of cooperation between Kazakhstan and Russia in the field of expanding the information space on the basis of libraries: ensuring wider access of readers to the achievements of national cultures and improving professional competencies of employees. The basis of the partnership is the agreement on cooperation between the regional scientific libraries of Kostanay and Chelyabinsk. The article considers the project activity of the Chelyabinsk regional universal scientific library, in which colleagues from the Kostanay regional scientific library proposed to expand the boundaries of individual projects and make them mutually beneficial; the experience of teleconferences of various formats; the creation of joint electronic collections. The author emphasizes that partnership is based on the willingness of libraries to interact for the benefit of their readers, the ability to convey the consolidated opinion of the library community to the authorities and society as a whole, the desire of library professionals to continue professional development
Flexible configuration update delivery for large clusters

Efficient configuration management plays an important role in managing large-scale clusters in public cloud services. Because a cluster may contain millions of containers, it is challenging to guarantee that configuration updates will be delivered to interested containers reliably and in time to meet the requirements of different scenarios. Existing solutions to this problem have limitations: consensus algorithms limit the scalability of the cluster and may not work for large clusters; epidemic algorithms face the challenge of long-tail latency, which means the overall response time is too long to deliver critical configuration updates effectively. To overcome the limitations of existing solutions, in this paper, we present a novel flexible approach for delivering configuration updates for large-scale clusters. This pub/sub approach uses a configurable, complete $N$-ary tree as the overlay and introduces flexible, two-phase configuration update delivery. This method of update delivery uses a portion of subscribers resources to improve its performance. Furthermore, it is fault-tolerant when it encounters node failures and network partitions. The strategies and the parameters of the overlay can be changed to meet performance and reliability requirements for different scenarios. Evaluations show that our approach significantly reduces the latency of update delivery compared to existing solutions. It also performs well in cases of node failures and network partitions.
SELECTION OF MANAGEMENT STAFF AND ASSESSMENT OF THEIR LEADERSHIP POTENTIAL IN THE EDUCATION SYSTEM

Today, the spirituality of the leader also plays an important role in ensuring the stability of socio-economic life as an indicator of the spirituality of society. The cohesiveness, morale, work ethic, leadership skills, organizational skills, management skills, as well as their selection for the position and assessment of their leadership potential are described, which unites the members of the society with the great future current
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multishot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.
Media Pembelajaran Stocklab Berbasis Multimedia

This study aims to develop multimedia-based learning media for StockLab; StockLab games are generally played by 3 to 5 people. This game is often played by students at the Faculty of Economics and Business as a simulation medium in building stocks where the card introduces investment, stocks, and the capital market. Stockland is a game using card media such as playing, domino or Uno with game instructions such as players must compete with each other to develop assets by investing in various sectors of stocks and mutual funds, and can take various actions optimally to become an investor with the most coins at the end of the game.
Empowerment Academic Data as the Basis of Mapping Priority Program of Quality Assurance Organization

This study was inspired by the confusion of academic data at Postgraduate Program.Improving data by relying on the SIAKADU program as the mainstay of PPTI State University of Surabaya (Unesa) is just a pile of data that has no meaning.Therefore, through this evaluative research, the data collected in SIAKADU program is evaluated and processed to obtain information and have meaning.Through the technique of collecting data from SIAKADU application, the results can be obtained as follows: (1) SIAKADU academic data that can be studied includes; (a) menu of Course Lesson Plan (RPS), (b) menu of print order by lecturer, and list of Final Test/Midterm Test questions, (c) menu of lecturers questionnaire and registration per semester; (2) The programs produced in this study include; (a) planning an intensify learning devices program to the lecturers through massive collection by the head of the Study Program with UPM, (b) limitation on the number of course subjects that postgraduate lecturers must teach based on the scientific capabilities, (c) optimizing the doctoral staff potential to assist professors in teaching, so that a mature scientific development occurs from their seniors, (d) increasing an intense communication between heads of study programs and UPM in order to carry out balanced academic functions; (e) academic mentoring program by optimizing the role of academic supervisors for postgraduate and doctoral students who had run out their maximum time of courses.
Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder

Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impressive performance in many clinical natural language processing (NLP) tasks. In this work, we present a novel extension to the Transformer architecture, by incorporating signature transform with the self-attention model. This architecture is added between embedding and prediction layers. Experiments on a new Swedish prescription data show the proposed architecture to be superior in two of the three information extraction tasks, comparing to baseline models. Finally, we evaluate two different embedding approaches between applying Multilingual BERT and translating the Swedish text to English then encode with a BERT model pretrained on clinical notes.
Weakly-Supervised Reinforcement Learning for Controllable Behavior

Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. Can we instead constrain the space of tasks to those that are semantically meaningful? In this work, we introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical chaff tasks. We show that this learned subspace enables efficient exploration and provides a representation that captures distance between states. On a variety of challenging, vision-based continuous control problems, our approach leads to substantial performance gains, particularly as the complexity of the environment grows.
THE INTEGRAL DEVELOPMENT OF PROFESSIONAL AND METHODOLOGICAL COMPETENCIES IN SPECIAL EDUCATION TEACHERS OF BASIC SCHOOLS WHEN TEACHING THE UNITS OF MEASUREMENT TO PERSONS WITH INTELLECTUAL DISABILITIES

In the article, the authors consider the strategic changes in the students’ educational process from the perspective of the development of professional integral methodological competencies among teachers in basic school. A special emphasis is placed on the description of the technology for the development of the integral methodological competencies in special education teachers who train children with intellectual disabilities in basic school.
FUTURE PREDICTION OF COVID-19 IN INDONESIA USING DEEP LEARNING

Lack in machine learning methods are the inability to predict more than one step forward, because in each step forward machine learning must calculate the error value with real data to be used to correct the weight value in the next round. This study aims to predict the long-term future of Covid-19 in Indonesia. The method used is to make the results of one-step predictions from machine learning as the first new data. Then in the next iteration machine learning will produce one more prediction step as the second new data, so we get two prediction results going forward. This process is repeated until it reaches the desired long-term prediction of 2 months (50 days). Long prediction for the next 2 months, is done by using 2 Deep Learning (DL) methods, namely Long Short Term Memory (LSTM) and Gate d Reccurent Unit (GRU) in the 4 hidden layers of learning machine used. The GRU model on the four hidden layers gives the best results with a value of RMSE = 206,632 at epoch = 5.
Penerapan Augmented Reality Dalam Pembuatan Aplikasi Buku 3D Sebagai Pendorong Minat Belajar Anak Usia Dini

AbstractAR and Android-based 3D smart books have enormous benefits for children's learning, especially PAUD and kindergartenchildren, namely that they can learn easily, fun, interactive and look real to get to know their surroundings. In addition, thebenefits are reducing the use of cellphones, gadgets, smartphones to play with, with this smart book, smartphones are moreused for learning. This AR and Android-based 3D smart book uses technology that turns static 2D objects into dynamic andlifelike 3D which makes static books viable and provides information information not only in the form of images, but sound,animation, and dynamic for learning. The market potential for this activity is very wide open and promising, because thetarget of this activity is users of early childhood learning books (PAUD) and in kindergartens (TK) in the Jombang district,especially East Java Province and throughout Indonesia in general. AR and Android-based 3D smart books are packaged intotextbooks or textbooks for early childhood and kindergarten children with a QR code application that plans for the future thisstartup company will collaborate with the ministry of education or education agencies for marketing so that the market can becontrolled optimally.Keywords : AR; android; smart books; children
Continuous Boundary Approximation from Data Samples Using Bidirectional Hypersphere Transformation Networks

Given a set of multidimensional data samples, we propose a way to approximate a continuous boundary that compactly contains all the samples. The method optimizes two residual neural networks to transform a unit hypersphere surface to a continuous boundary in the data space and vice versa. The networks are trained by shrinking the boundary while containing all the training samples inside through a loss function. The learning results in a bidirectional relationship, where the mapping from the data space to the spherical space can be used for one-class classification, while the inverse mapping can be used for generative tasks. The method is tested on 2D toy datasets, 3D, and 6D human joint pose to illustrate the results. The source code and the data are made publicly available.
Audit Keamanan Sistem Informasi Pada Data Center Menggunakan Standar SNI-ISO 27001

system security management is very important for Aceh Government institutions in managing information assets that refer to a standard. This will have a negative impact on the sustainability of information services, especially e-mail services managed by the Aceh Communication, Information, and Encryption Service. So the need for good management governance according to national and international standards to create good management of electronic mail information system services by conducting an internal audit process on its management in terms of physical and environmental security where the electronic mail service information system is managed. This study discusses the Information System Security Audit on Data Centers Using SNI-ISO 27001 Standards (Case Study: Aceh Government). The results showed the need for data and documentation of the final evaluation of the maturity level of the audit process to reduce the risk of threats to the information system generated so that problems can be overcome by making efforts to minimize the possible risks that have been caused. The auditing stages have been carried out on the information system in the data center using the SNI-ISO 27001 standard resulting in a level of maturity still at the repeatable but intuitive level that still requires further supervision in the management of the security side.
Recommendation Based on Java Code Analysis and Search

How to improve the efficiency and quality of software development is an ongoing concern in the field of software engineering. As a useful auxiliary function, code recommendation is embedded in almost all integrated development environments. There has been increasing interest and research in the area of code recommendation in recent years due to its convenience for project development. Existing research has made a lot of contributions to this field, but there are still many issues that need further study. One of the key points is the low success rate of recommendation. Focusing on this problem, this paper proposes a method to recommend Java source code after parsing massive amounts of source code information. We propose a new source code analysis algorithm for the scraped source code data. A source file is parsed into classes, methods, and attributes as recommendation objects. At the same time, the annotation information is bound to the annotated objects. Finally, the parsed information is indexed at the project, class, and method levels for code recommendations in a hierarchical recommendation manner. A code recommendation system is implemented by combining this with full-text retrieval technology for class library, class, and method level recommendation. The experimental results show that the method proposed in this paper has better performance in recommendation accuracy than existing code recommendation engines.
CDN Loop Detection

This document defines the CDN-Loop request header field for HTTP. CDN-Loop addresses an operational need that occurs when an HTTP request isintentionally forwarded between Content Delivery Networks (CDNs), butis then accidentally or maliciously re-routed back into the originalCDN causing a non-terminating loop. The new header field can be usedto identify the error and terminate the loop.
Penerapan Clustering Dengan Fuzzy C-Means Dalam Mendiagnosa Radang Asam Urat

Inflammation of uric acid is a disease that occurs due to excess uric acid in the blood, which then accumulates and accumulates in the form of crystals in the joints. The high-accuracy uric acid inflammation test must be done several times to treat the inflammation of uric acid in the body. Several indicators in the body can be a starting point for treating gout inflammation. Also, errors, the limitations of medical staff in handling huge amounts of data manually are a problem. One solution for this is to use a computer as a mathematical calculation in the Fuzzy C-Means grouping method. The criteria specified are joint pain, swollen joints, stiff joints, sprained feet and lumps in the joints. The grouping consisted of groups with gout inflammation and non-gout inflammation. Testing was carried out on 14 data.