Reviewerly/exHarmony huggingface dataset:
Natural Language Processing Approaches for Automated Multilevel and Multiclass Classification of Breast Lesions on Free-Text Cytopathology Reports

PURPOSE The extensive growth and use of electronic health records (EHRs) and extending medical literature have led to huge opportunities to automate the extraction of relevant clinical information that helps in concise and effective clinical decision support. However, processing such information has traditionally been dependent on labor-intensive processes with human errors such as fatigue, oversight, and interobserver variability. Hence, this study aims at the processing of EHRs and performing multilevel and multiclass classification by fetching dominant characteristic features that are sufficient to detect and differentiate various types of breast lesions. PATIENTS AND METHODS In this study, unstructured EHRs on breast lesions obtained through fine-needle aspiration cytology technique are considered. The raw text was normalized into structured tabular form and converted to scores by performing sentiment analysis that helps to decide the total polarity or class label of the EHR. Supervised machine learning approaches, namely random forest and feed-forward neural network trained using Levenberg-Marquardt training function, are used for classification of the collected EHR data set containing 2,879 records that are split in the ratio of 80:20 as training and testing data sets, respectively. RESULTS Random forest and feed-forward neural network classifiers gave the best performance with an accuracy of 99.36%, an overall receiver operating characteristic-area under the curve of 99.2%, a correlation with ground truth of 98.3%, and a histopathologic correlation of 98.6%. CONCLUSION Natural language processing has huge potential to automate the extraction of clinical features from breast lesions. The proposed multilevel and multiclass classification approach is used to classify 13 different types of breast lesions with 20 different labels into five classes to decide the type of treatment that should be given to patients by a physician or oncologist.
Signal Reconstruction by Using State Observers for Fractional-Order Chaotic Systems with Application to Secure Communications

In this chapter the authors propose to use fractional-order chaotic systems for data encryption. The encryption is a hybrid cipher, that takes elements of stream ciphers and block ciphers, making it possible to handle large messages without compromising the security of the message or severely increasing the need of computational power to process the encryption algorithm. The cipher relies on the synchronization of chaotic systems by using state observers; the observer is capable of accurately recovering states and uncertainties from a fractional-order chaotic system.
Adaptive Decision-Making Strategies in the Game with Environment

The problems that occurred during decision-making and supported by the stochastic game model with the environment are investigated and analysed. A method of adaptive choice of solutions based on stimulatory learning and application of Boltzmann distribution is developed. The algorithm and software of the decision-making system in the games with the environment are developed. The computer simulation results of stochastic choice of solutions are analysed and described. The developed decision-making method provides stochastic minimisation of the agent’s mean loss function and is based on adaptive parametric identification of the environment using Q-learning and defining a mixed strategy on the Boltzmann distribution. With proper parameter selection, the method provides close to 1 order of asymptotic convergence, the limit value for a given class of recurrent methods.
THE ESSENCE OF THE SYSTEM-ACTIVITY APPROACH AND CRITICAL THINKING IN THE EDUCATIONAL PROCESS

The article describes the structure, content and role of the system-activity approach in the development of critical thinking among students of educational institutions. The definition of the concept of "system-activity approach" is given, its main components and principles of implementation of the approach in scientific activity are highlighted. As you know, a modern student should have the skills to independently search for new knowledge, process and analyze it, develop new skills and abilities in accordance with educational goals, distinguish and independently find ways to solve existing problems, creatively approach the solution of educational goals and tasks. In the conditions of modern education, students often have to solve non-standard educational tasks in conditions unfamiliar to them, where the method of teaching schoolchildren in accordance with the requirements of a system-activity approach – as problem-based learning plays an essential role in their solution. It should be noted that as the main technological. the component of the system-activity approach becomes the condition of the actual activating difficulty, where the final result is an individual educational result obtained in the course of students' conscious learning activities. The result of this work can be put forward ideas, hypotheses, versions and methods obtained in the results of students' activities (schemes, models, experiments, texts, projects, etc.). All this is one of the key differences between the system-activity approach from traditional (knowledge) methods in teaching. Critical thinking has its own characteristics, its own conceptual system that distinguish it from other types and types of thinking. In most cases, we consider critical thinking only in relation to other types of thinking, in combination and comparison with such concepts as productive, problematic, creative, logical and systemic thinking, intelligence and with other concepts of active, purposeful mental and practical human activity. The paper analyzes the scientific approaches of foreign scientists in defining the concepts of "system-activity approach" and "critical thinking".
Deep Neural Networks for Passengers’ Density Estimation and Face Mask Detection for COVID-19 in Public Transportation Services

Public Transportation Networks (PTN) are considered as one of the essential commuting systems worldwide. Under good planification strategies, PTN improves population mobility, reduces environmental pollution, traffic congestion, and commuting time. Optimal management, control, and tracking of public transportation systems are desirable for improving service quality and users’ wellness. This work proposes a management and control system for public transportation based on passengers’ density estimation. Our final goal is to develop a communication network architecture and a Deep Neural Network for face detection to quantify the density and number of people being transported as a function of time. This collected information (density and number of passengers) is sent to public transport administrators (municipal authorities and bus owners) to take actions for adding/removing busses to/from specific route circuits and improve the users’ commuting quality of service (that is, reducing traveling times, waiting time on the bus stop, and not oversaturated units). Additionally, to control the spread of COVID-19 on PTN, our algorithm can detect passengers with or without a face mask. We provide an in-depth description of our proposed Deep Neural Network and its implementation in Python programming language.
Students' Perception of Vlog as A Self-Learning Media in Speaking

Speaking is one of the most challenging skills in learning English. Many obstacles are faced when learning to speak, such as lack of motivation, fear of making mistakes, and lack of self-confidence. These problems are often found during the teaching and learning process in the classroom. A teacher is expected to continue innovating for exciting and fun learning methods and media to increase learning interest, motivation, confidence, and creativity in speaking. It is needed not only in the classroom but also in independent learning. This study aims to discover students' perception of the learning process for their speaking skills through using vlogs as a self-learning media and the effect of using vlogs on students' motivation, confidence and creativity. The researcher took the research object of the English Language Teaching Department students in UNU Purwokerto. The data were obtained by giving a speaking project as a vlog with a specific theme. Data collection was conducted by providing questionnaires and interviews to find out students' perceptions about the effect of using vlogs for the improvement of motivation, confidence and creativity and also observing the students' vlogs to find out the learning process of speaking from the aspect of pronunciation, grammar and vocabulary. Data analysis is qualitative descriptive with theories from Dulay and Fromkin (1982). This study found that based on students' perceptions, they agreed that using vlogs improved their motivation, confidence and creativity. Meanwhile, based on the observation, it was found that there were some pronunciation errors and grammar mistakes in the students' speaking, but they showed improvement in choosing vocabulary. Some students used a variety of vocabularies that were rarely used in the class.
Literature Study Of The Effect Of Make a Match Learning Model Assisted With Index Card Match (ICM) Media to Improve Cognitive Learning Outcomes In Civics Learning Elementary School Student

This study aims to find out the reality in the field which shows that there is teaching Civics subjects without using media which can lead to a decrease in student learning outcomes. The research method used is library research using descriptive methods which are expected so that researchers can examine the development of education through data collection carried out based on relevant books, theories and journals as evidence in this study. The results of this study, among others, conclude that the make a match learning model assisted by the index card match media greatly influences student learning outcomes in Civics subjects and can increase student activity in the process of learning activities. It can be concluded that the make a match learning model assisted by index card match media has an influence on student learning outcomes, can increase enthusiasm in learning and can increase student activity in the learning process.
Improving Helpdesk Capability in Perum Peruri Through Service Catalog Management Based on ITIL V3

Perusahaan Umum Percetakan Uang Republik Indonesia (PERUM PERURI) is a government entity responsible for the printing and security of coins and bank notes of Bank Indonesia (BI) which is required to implement Good Corporate Governance (GCG). Previous evaluation of information technology governance indicates that PERUM PERURI needs to improve its helpdesk capability in providing IT services to all users. The first thing that needs to be done is developing and maintaining IT service catalogue, which serves as sole source of information regarding all of IT services provided by the IT division. The design of Service Catalog Management uses framework ITIL Version 3 best practice in services. This research uses Design Thinking Approach as a method in implementing a solution that has 5 stages, namely: Empathy, Define, Ideate, Prototype and Test. The implementation of the Design Thinking aims to produce an idea execution that meets user needs through understanding the user's character and providing a helicopter view (Prototype) of the Business Service Catalog so that users can provide experiences and feedback so that they can be a solution to the problems faced by the Business Service Catalog. This research produces two documents from Service Catalog Management, namely: 1). Business Service Catalog;, and 2). Technical Service Catalog. It is hoped that this Service Catalog document will be able to become a complete source of information, knowledge, reference regarding services to service users and service providers in order to minimize problems regarding helpdesk Peruri IT Division.
An algorithm for generalized syndrome decoding problem

<p style='text-indent:20px;'>Syndrome decoding problem has received a lot of attention due to its applications in code-based cryptography. In this paper, we study a generalization of the syndrome decoding problem. This problem was recently introduced in Khathuria, Rosenthal, and Weger's cryptosystem. We present a new algorithm for the generalized syndrome decoding problem, which is adapted from the information set decoding algorithm proposed by Finiasz and Sendrier. Furthermore, our algorithm can be severed as an attack for Khathuria, Rosenthal, and Weger's cryptosystem. New parameters of their cryptosystem are suggested for which our algorithm needs <inline-formula><tex-math id="M1">\begin{document}$ 2^{128} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M2">\begin{document}$ 2^{256} $\end{document}</tex-math></inline-formula> bit operations.
Mnemonics in Eaching Foreign Languages for Professional Purposes to Students of Economics

Problem and purpose. The paper substantiates the rationale to use empirically approved mnemonic techniques in developing the foreign language communicative competence in the professional field in students of the industry-related non-philological higher educational institution. Representation is given of the experiment conducted at the Siberian Transport University that was aimed at studying the impact of mnemonics application on embracing and perceiving specialized professional information in a foreign language as well as anticipating ways to improve such teaching techniques by means of digital educational technologies. The purpose of the study is to determine efficiency of mnemonics used by both students and teachers in the build-up of professionally-oriented foreign language proficiency as well as to identify the most effective relevant technologies and provide argumentation in favor of their implementing in practice. Methodology. The research is based on analysis and systematization of related theoretical and applied psychological-pedagogical and methodological materials, study of student research works, student questionnaire-based surveys and generalization of benchmarked teaching practices in higher education. The conclusion is made on the use of mind mapping as a productive method to acquire profession-related information in the development of algorithms for learning both foreign languages and profession-related courses.
Modeling knowledge, archival and bibliographic standards in comparison. 1st JLIS.it Seminar - May 19th 2022

The editorial gives an account of the insights that emerged during the Jlis.it seminar Modelling Knowledge, held in Florence on 19 May 2022. First, the political potential of the initiative in terms of integration and collaboration between documentary disciplines is emphasised. It then dwells on the descriptive habits of the different domains in search of more effective knowledge management models in a broad sense. Finally, the text opens up to the readiness of the journal to become a meeting place for different reflections on knowledge modelling.
The Electroweak Theory

Written for a two-semester Master's or graduate course, this comprehensive treatise intertwines theory and experiment in an original approach that covers all aspects of modern particle physics. The author uses rigorous step-by-step derivations and provides more than 100 end-of-chapter problems for additional practice to ensure that students will not only understand the material but also be able to apply their knowledge. Featuring up-to-date experimental material, including the discovery of the Higgs boson at CERN and of neutrino oscillations, this monumental volume also serves as a one-stop reference for particle physics researchers of all levels and specialties. Richly illustrated with more than 450 figures, the text guides students through all the intricacies of quantum mechanics and quantum field theory in an intuitive manner that few books achieve.
SEBARAN SPASIAL WILAYAH RAWAN KEKERINGAN LAHAN TANAMAN PADI SEBAGAI UPAYA MANAJEMEN RISIKO BENCANA KEKERINGAN DI PROVINSI JAWA TIMUR

Drought is one of the events which cause a lot of negative impact on the region, the area of agriculture drought can reduce yield and cause crop failure. East Java province is one of the largest contributors to the national rice production in Indonesia. Increased agricultural production can be done with risk management with a variety of drought mitigation strategies and disaster relief efforts preventive. One such effort is to make early control planning using drought-prone maps. Based on data analysis and processing of the obtained results that the distribution of drought in East Java province when the dry season, is very vulnerable category almost dominates the region's peak in August to October. Entering the rainy season spreading drought began to decrease, but the coastal areas are still in a very vulnerable category. The characteristics of the region is very vulnerable category during the year were in Sampang, Pamekasan, Sumenep, Tulungagung and part of the north coast of East Java Province which has the least rainfall than other areas and the climate is very dry. The movement patterns prone to drought from January to May, with a category extremely prone generally around the north coast of East Java Province, entered the month of June to November the drought extends further into the center of East Java Province, then in December the level of dryness safely started having additional area amid Province East Java. Risk management drought in East Java province, especially for areas prone and extremely vulnerable to the need for the regulation of early warning system for extreme climatic (ENSO), which affects drought, local legislation to establish the scale of priorities for the use dn water management, preparation of hazard maps of drought, determination anticipatory technology (manufacture ponds, weather modification technology, alternative energy sources, adjustment of cropping and cultivation technology) and irrigation systems adapted to climate forecasting, development of reward and punishment system for the people who do the conservation and rehabilitation of water and land resources.
Researchon Visual Modeling Technology of 3D Mining Engineering Based on Deep Learning

Deep learning is a study hotspot in the domain of man-made intelligence. It is an inevitable trend to use deep learning to support the study work of man-made intelligence, and it has shown its act advantages in the domains of picture, speech and text. Interpretive approach of deep learning is an interdisciplinary study subject of man-made intelligence, machine learning(ML), cognitive psychics, logic and many other disciplines. It has vital abstract study meaning and actual apply worth in message push, medical study, finance, message security and other domains. Deep learning is a new study direction in the domain of ML. By imitating the structure of human brain, it can efficiently course complicated input data, smartly learn divers knowledge, and availably solve many kinds of complicated smart question. In recent years, with the emergence of efficient learning LRUs for deep learning, the ML community has set off an upsurge of studying the theory and apply of deep learning. The rise of 3D modeling technology has promoted the vigorous expand of computer simulation and virtual reality technology, and various 3D modeling software platforms and simulation systemics have emerged as the times require. These platforms and systemics provide powerful design tools and intellectual support. Through the simulation analysis of real scenes, people can directly start with 3D notions and ideas and make visual design schemes and evaluation systemics.
Bayesian Optimization of Sampling Densities in MRI

Data-driven optimization of sampling patterns in MRI has recently received a significant attention. Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally optimize the sampling densities using Bayesian optimization. Using a dimension reduction technique, we optimize the sampling trajectories more than 20 times faster than conventional off-the-grid methods, with a restricted number of training samples. This method – among other benefits – discards the need of automatic differentiation. Its performance is slightly worse than state-of-the-art learned trajectories since it reduces the space of admissible trajectories, but comes with significant computational advantages. Other contributions include: i) a careful evaluation of the distance in probability space to generate trajectories ii) a specific training procedure on families of operators for unrolled reconstruction networks and iii) a gradient projection based scheme for trajectory optimization.
A differentiable approximation for the Linear Sum Assignment Problem with Edition

Linear Sum Assignment Problem (LSAP) consists in mapping two sets of points of equal sizes according to a matrix encoding the cost of mapping each pair of points. The Linear Sum Assignment Problem with Edition (LSAPE) extends this problem by allowing the mapping of sets of different sizes and adding the possibility to reject some matchings. This problem is set up by a rectangular cost matrix whose last column and last line encode the costs of rejecting the match of an element of respectively the first and the second sets. LSAPE has been the workhorse of many fundamental graph problems such as graph edit distance, median graph computation or sub graph matching. LSAP may be solved using the Hungarian algorithm while an equivalent efficient discrete algorithm has been designed for LSAPE. However, while the Sinkhorn algorithm constitutes a continuous solver for LSAP, no such algorithm yet exists for LSAPE. This lack of solvers forbids the integration of LSAPE in Neural networks requiring continuous operations from the input to the final loss. This paper aims at providing such a solver, hence paving the way to an integration of LSAPE solvers in Neural Networks.
Response to query from Roya Imani for manuscript HL-D-22–00494

STEMI Performance within six hospitals in one healthcare system
AI is Driving a New Vision for Displays

Abstract As our world constantly changes, technology provides a platform for a type of evolution around how we live, work, and experience it. Day to day, these changes may not be so evident, but look across generations at the ways our parents and grandparents conducted business and interacted. Now look at how young adults work and what their expectations of technology have become. We're achieving a level of integration with the digital world in a way that was imagined only in science fiction books a couple of generations ago.