Reviewerly/exHarmony huggingface dataset:
Talk to Me: Artificial Intelligence “Virtual Friend” for Depression Sufferers Using Term Frequency-Inverse Document Frequency (TF-IDF) and Finite State Machine Method

Depression refers to mental health in which a person experiences a bad mood and has a decreased quality of life. In Indonesia, there are quite a lot of challenges in dealing with depression problems such as lack of education on how to treat depression, lack of mental health personnel, and the emergence of a bad stigma against depression sufferers. Voice-based artificial intelligence technology for people with depression was developed to play a role in filling the gap by acting as a support system. In this research, the Natural Language Processing (NLP) method is used to enable computer to be able to understand the user's input. TF-IDF (The Term Frequency-Inverse Document Frequency) method is also used to weight documents and the Finite State Machine (FSM) method used to classify the results of document weighting against a predetermined dialogue scenario. To be able to interact with the system, the author uses the Google Cloud Speech API technology to convert speech and text. As for testing of this system, it is done by calculating the level of accuracy of the answers given by the system to users. The level of accuracy of the system answers obtained from the test results is 96.5%. The accuracy value indicates that the answer given by the system is in accordance with what the user's input.
Globally Admissible Inference Rules

Setting the basic rules of inference is fundamental to logic. The most general variant of possible inference rules are admissible inference rules: in logic 𝐿, a rule of inference is admissible if the set of theorems 𝐿 is closed with respect to this rule. The study of admissible inference rules was stimulated by Friedman’s problem: Is there an algorithm for recognizing the admissibility of an inference rule in intuitionistic logic? For a wide class of non-classical logics the problem of recognizing with respect to the admissibility of inference rules was solved in 1980s. Another way of describing all admissible rules in logic goes back to the problem of A. Kuznetsov (1975): specifying a certain (finite) set of admissible rules, from which all other admissible rules in logic will be derived as consequences, i.e. setting a (finite) basis. It turned out that most basic non-classical logics do not have a finite basis for admissible inference rules. In the early 2000s, for most basic non-classical logics and some tabular logics, the Fridman-Kuznetsov problem was solved by describing an explicit basis for admissible rules. The next stage in the study of admissible inference rules for non-classical logics can be considered the concept of a globally admissible inference rule. Globally admissible rules in the logic 𝐿 are those inference rules that are admissible simultaneously in all (with finite model property) extensions of the given logic. Such rules develop and generalize the concept of an admissible inference rule. The present work is devoted to the study of globally admissible rules of logic 𝑆4. Conditions for global admissibility in the logic 𝑆4 were obtained, a characteristic (universal) model was constructed (checking global admissibility is reduced to checking the truth of a rule on its submodels), a basis was described (all globally admissible rules are derived from it) and an anti-basis (from which all rules not available globally in 𝑆4).
Open Source Software Architecture for Computer Based Procedure Systems

In the US and globally Advanced Reactors are being developed. These Nuclear Power Plants (NPP) will utilize modern engineering and simulation tools, as well as modern control systems. New plants are safer and more flexible than previous generations. New opportunities exist for how plants could manage and operate reactors. Specifically, future plants could utilize fleet management and reduced staffing to decrease operational expenses. Tools are needed to support the development of computer-based procedure systems for advanced reactors. Here we find inspiration from scientific open source software tools (Python, Jupyter Notebook, Jupytext, Jupyter Book, Rmarkdown, Rstudio, MarkDown, MyST, Sphinx, Git) for rapid prototyping and evaluation of computer based procedure systems to support the design and evaluation of novel concept of operations for new reactors, conducting engineering tests, validating and optimizing operations using machine learning.
Identification of Leukemia Cells in Microscopy Digital Image Using Improved Canny Edge Detection Method

Leukemia is a blood cancer mutates inside the bone marrow. If unidentified at an early stage, it can lead to severe consequences and later death of a person. To diagnose leukemia, machines and manpower skills are required to identify if the illustrative image is healthy or unhealthy. The manual observation of the large number of images may lead to error in the results. One of the biggest barriers in the result's accuracy is identification of concerned region in the overlapped cells. Motivated by the challenges, framework is developed based on image processing for automatic detection of White Blood Cells in the peripheral blood smear image. In this paper, canny edge detection with Circular Hough Transform is applied to count the number of white blood cells. The outcome is to obtain the classification of the sample images whether the sample is unhealthy or healthy sample image. Total 108 multi-cell Acute Lymphoblastic Leukemia images are considered. It was found that the proposed cell separation method yields an accuracy of 98% in comparison to state of the art segmentation technique.
Background on STIT and Related Logics

In this dissertation, we develop formal frameworks to reason about causal responsibility in multiagent scenarios and analyze how agency influences both counterfactual and normative reasoning.
Combating Luxury Counterfeiting Through Blockchain Technology

The new rapid growth of counterfeiting in the luxury apparel industry has built a global and highly sophisticated market. The Global Brand Counterfeiting Report in 2018 has reported losses incurred by luxury brands because of the sale of counterfeiting through the Internet only to 30.3 billion USD, demonstrating the growing need for taking action. When luxury products are counterfeited, the sense of exclusivity of brands is lost, and the reputation built on craft, origin, and quality is certainly diluted. As the luxury resale market continues to grow and counterfeit culture persists, the ability to verify quickly and easily that a product is authentic is becoming a necessity for any luxury brand. In attempts to reassure consumers about the originality of their purchases and improve their experience in luxury consumption, luxury brands need to find innovative ways that ensure the protection of their authenticity. The distributed ledger technologies like blockchain technology enabled in the fashion industry have provided solutions to increasing transparency by allowing stakeholders, such as fashion houses, retailers, and consumers, to claim authenticity, track ownership, and make effective decisions based on unified and reliable information. In this context, this chapter aims to discuss the negative impact of counterfeiting on the luxury industry and identify the potential role of blockchain technology in combating luxury counterfeiting globally. Furthermore, the chapter will discuss some cases of the luxury companies that have already integrated successfully the blockchain technology in the luxury industry.
Comparison of Machine Learning Algorithms and Neural Network for Breast Cancer Prediction

Breast cancer is mostly a woman-specific illness, although it can also afflict men, albeit in a much smaller percentage. Recently, tremendous progress has been made by doctors, in the fields of breast cancer diagnosis and therapy at an early stage to reduce the amount of deaths from the disease. Before, detection of breast cancer intended removal of the entire breast; however, due to the vast spectrum of therapies available today, these surgeries are rarely undertaken in rare situations. Further, the machine learning and artificial intelligent play the important role for early prediction for breast cancer and determined if this benign or malignant tumor. There are more researchers presented papers about breast cancer and the most of them obtained on the good results. In this work, we used the most popular algorithms of machine learning algorithms (e.g., Logistic Regression, CatBoost, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost) and Neural Network of deep learning prognosis breast cancer, and we used Wisconsin Breast Cancer Dataset (WBCD). To evaluate classification in terms of accuracy, recall, precision, and f1-score, we obtained the experimental results from all algorithms above 95%, but Random Forest gives the highest accuracy of 99.50%.
Between English and Mandarin: A Contrastive Study of Interrogative Sentences among Indonesian Textbooks

Indonesian students in senior high school in the language program found it difficult to make both English interrogative sentences and Mandarin interrogative sentences. To assist the English and Mandarin teachers in overcoming the problem, a contrastive study of both English and Mandarin interrogative sentences needs to be investigated. Hence, this article purposes to reveal the similarities and differences between English and Mandarin interrogative sentences found in Indonesian textbooks used by teachers. Qualitative descriptive research was used by describing both similarities and differences. The result generates several similarities and differences in interrogative sentences between English and Mandarin. Both languages are similar because they both have five kinds of interrogative sentences which have the same meaning and required similar answers. Among five types of interrogative sentences in English or Mandarin, some of them use several predicates and object simultaneously in one question. The use of the auxiliary verb in English and Mandarine's interrogative sentence is dissimilar. In English interrogative sentences, auxiliary verbs are used accord with the tenses used, while in Mandarin they are not. Besides, auxiliary verbs in English interrogative sentences must precede the subject, but in Mandarin, they are at the end of the sentence.
Deep Echo State Network Based Neuroadaptive Control for Uncertain Systems

This work presents a deep echo state network (DESN) based neuroadaptive control approach for a class of single-input single-output (SISO) uncertain system. In which, a DESN based on multiple reservoirs is applied for approximating the uncertain parts of the control system and the rigorous stability condition under the presented control strategy is analyzed. The availability of the approach is proved by comparison with the control technique using radial basis function neural network (RBFNN) and the control scheme using traditional echo state network (ESN) via numerical simulations, demonstrating that superior tracking performance is achieved by the proposed method.
Entropy-Based Early-Exit in a FPGA-Based Low-Precision Neural Network

In this paper, we investigate the application of early-exit strategies to fully quantized neural networks, mapped to low-complexity FPGA SoC devices. The challenge of accuracy drop with low bitwidth quantized first convolutional layer and fully connected layers has been resolved. We apply an early-exit strategy to a network model that combines weights and activation with extremely low bitwidth and binary arithmetic precision based on the ImageNet dataset. We use entropy calculations to decide which branch of the early-exit network to take. The experiments show an improvement in inferred speed of $$1.52\times $$ using an early-exit system, compared with using a single primary neural network, with a slight accuracy decrease of 1.64%.
Applying swarm intelligence and data mining approach in detecting online and digital theft

Various methods have been proposed to deal with phishing attacks. Using machine learning along with data mining, such as MLP techniques, is one of the practical approaches to detect these attacks. To detect phishing attacks by the neural network with proper accuracy, it is necessary to intelligently do feature selection. In this research, the emperor penguin optimiser algorithm has been used as feature selection in detecting phishing attacks by a MLP. Experiments show that the error of the proposed method for detecting phishing is less than those of WOA, BOA, and SHO algorithms. The results show that the population increase in the proposed method reduces the value of the feature selection function and phishing detection error by about 69.57% and 24.56%, respectively. The RMSE error in detecting phishing attacks in the proposed method occurred to a lesser degree in comparison with MLP, DT, SVM, and BN. The accuracy, sensitivity, and precision of the proposed method in detecting phishing attacks are 98.12%, 97.92%, and 97.88%, respectively. The proposed method is more accurate in detecting phishing attacks than methods such as GA and PSO algorithms and is more accurate than BPNN, SVM, NB, C4.5, RF, and kNN.
Digital Model of Mechanical Interaction and its Application

A digital model of mechanical interaction is proposed, in which the motion of virtual objects in one-dimensional space is determined by their information content. The correspondence of the mathematical description of motion in the digital model to the physical laws is shown. Using a digital model of mechanical interaction, a method for calculating the reaction to the interaction in reflex intelligent systems is proposed. A brief description of the response to the interaction of reflex intelligent systems created using the method of calculation is given. Their effectiveness in solving problems of natural language communication, forecasting, management, expert evaluation, etc. is shown.
Proposal for Formation Evaluation Based on Different Scenarios for Clay Volume Calculation in the Atapu Field

Summary A well in the Atapu field, in the Santos basin, was the largest producer with 65,000 boe/d in the period from July to September 2022. The potential of an oil field like this allows the use of several studies to characterize the oil reserves. One of the studies applied to characterize reservoirs is the formation evaluation. A work like this carried out in an environment of carbonate sedimentation can be a challenge. To evaluate the formations of the 9-BRSA-1284-RJS well, a methodology was used to calculate Net Pay in three scenarios. The scenarios were different methodologies for the calculation of clay volume, being these with the gamma ray curve, uranium free gamma rays and with the magnetic resonance tool. The evaluation was done in an integrated manner with well logs, petrophysical laboratory data and well reports. The interpretation of the data showed that the Net Pay scenario generated by the clay volume cutoff performed from the uranium free gamma ray curve generated a more realistic result, proven by the production intervals based on the final well report. Zones 1 and 2, characterized as non-reservoir bearing hydrocarbons, were indicated as the Jiquiá stage (Z2) and a “dirtier” carbonate interval or marl.
ASSESSMENT OF MOTIVATIONAL COMPETENCE OF PHYSICAL EDUCATION IN GENERAL EDUCATIONAL INSTITUTIONS AND A CLASSICAL UNIVERSITY

The article gives an assessment of the formation of the components of motivational competence (physiological and psychological) of senior students of general educational institutions and bachelors of a university, athletes and non-athletes, within the framework of increasing physical education. The leading motives of the groups of subjects for health, health protection and physical culture are singled out. The levels of motivational competence are characterized. The percentage increase in changes in motivational competence as a result of the implementation of the sports and educational platform among the subjects of the experimental group was revealed.
Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification

Applications from Remote Sensing (RS) unveiled unique challenges to Deep Learning (DL) due to the high volume and complexity of their data. On the one hand, deep neural network architectures have the capability to automatically ex-tract informative features from RS data. On the other hand, these models have massive amounts of tunable parameters, re-quiring high computational capabilities. Distributed DL with data parallelism on High-Performance Computing (HPC) sys-tems have proved necessary in dealing with the demands of DL models. Nevertheless, a single HPC system can be al-ready highly heterogeneous and include different computing resources with uneven processing power. In this context, a standard data parallelism strategy does not partition the data efficiently according to the available computing resources. This paper proposes an alternative approach to compute the gradient, which guarantees that the contribution to the gradi-ent calculation is proportional to the processing speed of each DL model's replica. The experimental results are obtained in a heterogeneous HPC system with RS data and demon-strate that the proposed approach provides a significant training speed up and gain in the global accuracy compared to one of the state-of-the-art distributed DL framework.
Mathematics on Cirebon Woven Fabric with Lanang Motifs

This study aims to know the mathematical elements in the lanang motif woven fabric, construct a mathematical model on the increasingly lanang patterned weaving, and construct a new mathematical model for the new lanang motif woven fabric. The data was obtained in the form of qualitative data using interview data collection techniques, observations, documentation and field notes with data analysis, namely domain analysis, taxonomic analysis, component analysis and theme analysis. The results showed that the lanang-patterned woven fabric has geometric shapes, namely pentagons, rhombuses and triangles, to make pentagons, rhombuses and triangles using a pattern formed from small polygons called a rhombus-shaped unit lattice. The mathematical model used in this research is the matrix; to create a pentagon requires a matrix of order 103 × 210, to form a rhombus requires a matrix of order 103 × 338, and to develop a triangle involves a matrix of order 103 × 348. These patterns can create new designs by manipulating the rows contained in the matrix owned by each shape.
VIRTUAL GEOLOGY FIELD TRIPS: BRINGING THE STORY OF ROCK OUTCROPS TO TODAY'S CLASSROOM

Have you ever wanted to take your students into the field but couldn’t because the nearest outcrop was hours away or time and funds were lacking? This free website (https://GeologyVirtualTrips.com) was created to bring geologic field localities to your classroom, illustrating both large-scale rock formations as well as smaller-scale geologic structures. Due in part to changes brought about by funding challenges and the pandemic, this website is already being used to supplement the Geology Curricula of several High School Dual Enrollment programs and universities. In addition to numerous photographs and accompanying descriptions, students are provided with a condensed geologic history of the field locality as well as Student Questions pertaining to each Field Trip Stop. Some questions can be answered from the provided content while others require outside research. A comprehensive Index is provided and an Answer Guide to the over 325 questions is available to instructors. To offer an upgraded alternative to current online options, this author decided to use personally taken photos of nearly 60 field localities visited in the past. Each site contains descriptive text simulating an actual trip to the outcrops which are now brought into the classroom. The user-friendly descriptions, high quality photography and expanded locations give students the experience of “being there.” All rock types, igneous (plutonic and extrusive), metamorphic (regional and contact), and sedimentary (detrital and chemical) are thoroughly illustrated along with large-scale structures (folds, faults, glacial processes, etc.) and smaller-scale structures (cross-beds, slickensides, ripples, mudcracks, etc.). Tectonic settings including ancient sedimentary environments are noted where appropriate. Locations include nearly 30 National Parks and Monuments of the Western U.S. and Canada including the Grand Canyon, Yosemite, Glacier, Zion, Arches, Mt. St. Helens, Hawaii and Banff, as well as NYC and surrounding counties, and the Texas Hill Country. More locations are added yearly. This unique website provides an extension of learning to classroom work increasing student interest and motivation. It provides the user with the opportunity for self-paced learning and provides an interactive experience that brings Geology to life.
A Task-Specific Remote Sensing Image Manipulation Based on Deep Detection Network and Mask Seam Carving

It is usually necessary to remove the important man-made objects about state security or change their GPS locations, before the remote sensing images are released to the public. Seam Carving, a technique based on content-aware, can be used to deliberately manipulate images. Nevertheless, it’s still a tough challenge to generate Seam Carving forgeries without aliasing artifacts. This paper presents a task-specific Seam Carving approach based on ternary mask map. First, we introduce a deep detection network to detect the targets containing sensitive information. Then we convert the detection results into the ternary mask maps, where the ternaries respectively represent the targets that should be deleted, preserved and pending. Finally, we redesign the energy function of Seam Carving so that it can fix the seam’s path based on the mask maps. The proposed method is evaluated on a large collection of images, demonstrating that the improved algorithm can better delete specific objects or change their GPS locations with well-maintained structure and shape of other important content.
Few-shot Learning based on Multi-Attention and Prototype Correction

The goal of few-shot learning is to learn a classifier that performs well even without a large amount of labeled data for training. Recently, many models have performed well in the few-shot domain. However, there are still some problems in these few-shot learning methods. First, due to the scarcity of labeled data in few-shot scenarios, the feature extraction is not representative enough when extracting sample features. In addition, there is a certain bias between the prototype calculated only using the support set and the real prototype, which will affect the classification accuracy. Therefore, in order to solve the above problems, we propose the Few-shot Learning Based on Multi-Attention and Prototype Correction(MAPC). Specifically, We use two attention mechanisms to help the feature extraction module extract more representative features. After obtaining the extracted features, we use pseudo-labels to fuse the query set sample features to correct the basic prototype to obtain a class prototype that is closer to the real prototype. We evaluate our MAPC on Mini-ImageNet dataset, it is a few-shot learning public dataset. The classification accuracy of our method improved in every case on Mini-ImageNet dataset.