Reviewerly/exHarmony huggingface dataset:
Analyzing Trends in Medical Imaging Using Intelligent Photonics

The integration of photonics and artificial intelligence (AI) has led to the emergence of intelligent photonics, which offers significant advancements in medical imaging. In this paper, a Photonic Crystal Fiber (PCF)-based sensor is presented for tumor detection. The finite element method is used to simulate the proposed sensor. By varying the geometrical parameters of the proposed sensor, an optimized sensor is proposed. Meanwhile, the latest AI techniques used in medical imaging, such as deep learning (DL) and convolutional neural networks (CNN), are also analyzed to improve upon the ability of the sensor. This paper highlights the potential of intelligent photonics in improving efficiency, sensitivity, specificity and accuracy of medical imaging, particularly in the areas of tumor detection and treatment. The results show that DL has an efficiency of 95%, and CNN has shown an accuracy of 98%. Additionally, this paper discusses the challenges and limitations that need to be addressed in order to fully realize the potential of these technologies. This paper demonstrates that the integration of photonics and AI has great potential to revolutionize medical imaging.
FAIR Additive Manufacturing Data Management Principles

Abstract This article details findability, accessibility, interoperability, and reusability (FAIR) additive manufacturing data management principles and examines related motivations, benefits, and challenges. It explains opportunities to advance the state of the AM community efforts in fostering FAIR data management practices/principles and outlines the consequence of such efforts on technology maturation and industrialization for AM technologies.
Personalized Learning Resource Recommendation Framework Based on Knowledge Map

The knowledge space theory can better understand the knowledge concept and its internal connection by analyzing and understanding the connection and combination of knowledge concepts. A personalized learning resource recommendation framework based on knowledge space theory is designed to help students better understand and master subject knowledge in this paper. The framework mainly includes the following three steps: firstly, analyze the students' learning behavior, and establish the student knowledge space model; secondly, establish the subject knowledge space model by analyzing the structure and relationship of subject knowledge; finally, according to the student's The knowledge space model and subject knowledge space model design personalized learning resource recommendation algorithms to provide students with the most suitable learning resources. Experiments are conducted to verify the validity and feasibility of the framework, as well as its impact on student learning outcomes.
Classification of User Reviews for Software Maintenance in Indonesian Language Using IndoBERT-BiLSTM (Case Study: MyPertamina)

Useful app reviews play an important role for developers in maintaining app quality and performance. However, with the increase in application usage and the number of reviews provided, not all reviews are constructive, some reviews only contain insults or praise without providing constructive suggestions or feedback. Manually sifting through thousands of reviews takes a lot of time and effort. Therefore, there is a need for efficient and effective methods to identify useful reviews. This study used the IndoBERT word embedding method and the BiLSTM classifier for classification of useful reviews. Experimental results show that the IndoBERTBi-LSTM model with a learning rate configuration of 2e-5, dropout probability of 0.2, and batch size 16 achieves the best results with an accuracy value of 95.49% and shows an increase in accuracy of 1.16% compared to the fine-tuned IndoBERT model as classification method.
Improving Classification Accuracy Using Hybrid Machine Learning Algorithms on Malaria Dataset

Machine learning algorithms are integrated into computer-aided design (CAD) methodologies to support medical practitioners in diagnosing patient disorders. This research seeks to enhance the accuracy of classifying malaria-infected erythrocytes (RBCs) through the fusion of machine learning algorithms, resulting in a hybrid classifier. The primary phases involve data preprocessing, segmentation, feature extraction, and RBC classification. This paper introduces a novel hybrid machine learning algorithm, employing two combinations of supervised algorithms. The initial combination encompasses stochastic gradient descent (SGD), logistic regression, and decision tree, while the second employs stochastic gradient descent (SGD), Xgboost, and random forest. The proposed approach, implemented using Python programming, presents an innovative hybrid machine learning algorithm. Through a comparative analysis between individual algorithms and the proposed hybrid algorithm, the paper demonstrates heightened accuracy in classifying malaria data, thus aiding medical practitioners in diagnosis. Among these algorithms, SGD, logistic regression, and decision tree yield individual accuracy rates of 90.63%, 92.23%, and 93.43%, respectively, while the hybrid algorithm achieves 95.64% accuracy on the same dataset. The second hybrid algorithm, combining SGD, Xgboost, and random forest, outperforms the initial hybrid version. Individually, these algorithms achieve accuracy rates of 90.63%, 95.86%, and 96.11%. When the proposed hybrid algorithm is applied to the same dataset, accuracy is further enhanced to 96.22%.
Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia.
IT vertical: concept and implementation of studying information technologies in basic school

The article presents a description of the concept and model for the study of information technologies at the level of basic general education in the context of the implementation of the Federal State Educational Standards. Based on the example of the implementation of the project in grades VII—IX of general education organizations in Moscow, the content of the educational trajectory of a teenager is revealed, taking into account the request for early profiling in the field of "Informatics and information technologies". Features of the educational trajectory are implemented through a special bilingual programming course, new content of the course of technology (with the inclusion of special sections on mechatronics, technologies for creating digital twins, electronics and circuit design) and an ecosystem of extracurricular activities courses in forms specific to software developers. A feature of studying informatics and physics at an advanced level is the presence of interdisciplinary cases using digital laboratories. Mechanisms and tools for integrating specialized knowledge on current issues in the IT industry into the educational program are presented. A special place is occupied by the issues of creating the necessary infrastructure for the implementation of the project: material and technical base, personnel training and methodological support of the project. The unique functional and didactic capabilities of digital tools for active immersion of schoolchildren in the process of mastering the content of the educational program are presented. Examples of author's assignments are given for developing the skills of schoolchildren, diagnosing and monitoring acquired knowledge.
Considerations and Challenges

As with any new technology, continuous biometric authentication systems have a variety of considerations and challenges that must be addressed before they are deployed for real-world use. This chapter explores some of these key considerations and challenges. First, the consideration for contextual awareness is described. Then the issue of bias is discussed as well as the impact of power usage of continuous biometric authentication systems. Next, some considerations and challenges arising from attacks, privacy concerns, and regulations are provided. The last consideration and challenge discussed is the issue of drift that can occur from the original biometric profile. Finally, a brief summary provides an overview of the chapter.
Biometrics for Continuous Authentication

This section discusses the concept of biometrics and the requirements generally needed for a biometric used for authentication. Then the different physiological and behavioural modalities used in continuous biometric authentication systems are discussed. Then, multibiometrics and systems where they are used are surveyed. Finally, the chapter ends with a summary of the biometrics discussed.
Human Pose Estimation with Combined Feature Maps and Joint Embeddings

In recent years, Human Pose Estimation task, a typical computer vision task, still remains room for further improvement in overcoming complex scenarios (e.g., occlusion), data flaws (e.g., motion blur), etc. Recent researches often equip deep learning models with human prior knowledge to obtain enhanced accuracy. In this study, we present a novel model that simultaneously maintains both joint graph embeddings and feature maps as two separated branches and communicates information between them, aiming to enable the model to leverage both long-range semantic relationships and local textile information effectively. We evaluate our experiment on PoseTrack18 dataset and compare its performance with existing state-of-the-art methods. Our model achieves a mean Average Precision (mAP) of 80.2, which, while not out-performed, is competitive with existing state-of-the-art methods. As most previous methods solely use either feature maps or joint embeddings to generate their prediction, further research will be conducted to explore if parallelly combining feature maps and joint embeddings with them can further enhance the result.
Virtual Reality as a Possible Means to Meet the Psycho-spiritual Needs of Older Adults in Nursing Homes

This article presents how virtual reality can contribute to meeting the needs of residents in nursing homes. As part of the VIREAS project, we conducted a qualitative study in January 2021 in a South Bohemian nursing home. The research sample consisted of 12 residents aged 63 to 100 with different cognitive and sensory-motor levels. Our results show that a properly designed virtual experience can support the fulfilment of self-esteem and value, meaning and continuity of life story, the need to be part of a community, and the need to transcend everyday life. To meet these needs, several factors are important: high-quality content; user-friendly form; proper time for the virtual experience; sufficiently educated activity workers; appropriate communication with older persons before, during, and after the virtual experience; and the opportunity to share their virtual experience with others. We also define some factors that may limit the use of this method in nursing homes.
An ATR Method for Imbalanced Data SAR Images Based on CCEGAN

Synthetic aperture radar (SAR) automatic target recognition (ATR) is an important application of SAR. In military applications, it is unrealistic to be able to acquire balanced SAR target images due to the non-cooperative nature of the targets. SAR ATR systems often face the problem of unbalanced data, which leads to the deterioration of the recognition performance of the ATR model. we propose a SAR ATR method based on a complementary cross-entropy generative adversarial network (CCEGAN), aiming to improve the robustness of the ATR model under imbalanced data. Firstly, features are extracted from the imbalanced data using a deep convolutional neural network (DCNN). Next, a classifiable GAN model is designed and then trained using features extracted from the DCNN. With the adversarial training of the GAN, the classification task is automatically accomplished while enriching the training sample set without the need for secondary screening of the generated samples. Finally, to further cope with the ATR model's preference for majority classes, we introduce CCE as the classification loss for classifiable GAN training. This will result in a relatively balanced weighting of minority and majority classes in the loss computation, reducing the dominance of the majority class over the overall loss. The method is an end-to-end model without manually designed features. Experiments on the measured MSTAR dataset validate the robustness of the method under different imbalance rates and different minority classes.
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks

Within the realm of deep learning, the interpretability of Convolutional Neural Networks (CNNs), particularly in the context of image classification tasks, remains a formidable challenge. To this end, we present a neurosymbolic framework, NeSyFOLD-G that generates a symbolic rule-set using the last layer kernels of the CNN to make its underlying knowledge interpretable. We find groups of similar kernels in the CNN (kernel-grouping) using the cosine-similarity score between the feature maps generated by various kernels. Once such kernel groups are found, we binarize each kernel group’s output and use it to generate a rule-set using a Rule Based Machine Learning (RBML) algorithm called FOLD-SE-M. We present a novel kernel grouping algorithm and show that grouping similar kernels leads to a significant reduction in the size of the rule-set generated by FOLD-SE-M, consequently, improving the interpretability. The rule-set can be viewed as a stratified Answer Set Program wherein each predicate’s truth value depends on a kernel group in the CNN. Each predicate in the rule-set is mapped to a semantic concept using a novel semantic labeling algorithm that utilizes a few semantic segmentation masks of the images used for training. The last layers of the CNN can then be replaced by this rule-set to obtain the NeSy-G model which can then be used for the image classification task. The goal-directed ASP system s(CASP) can be used to obtain the justification of any prediction made using the NeSy-G model.
Digital economy competencies: experience and stages of formation among university students

Problem statement. In accordance with the goals and indicators of federal projects, the leading role in the formation of key competencies of the digital economy is assigned to institutions of secondary general, vocational and additional education. To form digital competencies in the field of creating algorithms and computer programs, digital departments have been created in a number of universities in the country. Based on this, the problem of the formation of these competencies among students studying in various fields of training, in the absence of a digital department at the university, becomes urgent. The purposes of the study are to analyze the existing experience of students' formation of competencies necessary for the implementation of the national program “Digital economy of the Russian Federation” and formulate proposals for improving this process. Methodology. A set of measures was carried out, including the analysis of educational training programs in order to identify disciplines, the study of which provides students with the knowledge and skills to develop algorithms, create computer programs, theoretical analysis of research in this area and the development of a methodological approach that ensures the formation of digital competencies in the field of creating algorithms and computer programs. Results. The analysis of the process of formation of digital competencies among university students, the theoretical analysis of research on this problem made it possible to identify and disclose the stages of formation of digital competencies in the field of algorithms and computer programs development. Conclusion. The result of the analysis of educational programs of universities was the conclusion about their insufficient focus on the formation of digital competencies of students. Based on the theoretical analysis of research in the field of the formation of competencies for the creation of algorithms and computer programs and their own experience, an approach to the formation of students of this competence is proposed.
Computer Knowledge Improvement Training Students Dropping Out of School in Buluh Apo Village

Buluh Apo village is a village that is located far out of town Duri, however, the Head of Buluh Apo Village has a vision to develop his village through the implementation of computer technology science, so that this village also has the nickname Net Village, because it has an internet network developed with the community. The purpose of this service is a collaborative program between the Apo Buluh village apparatus and the AMIK Mitra Gama campus to improve the quality of computer science in the current pandemic (COVID-19) conditions which require students to study more online (online) and for students dropping out of school. can Buluh Apo Village is a village located far on the outskirts of Duri city, however, the Head of Buluh Apo Village has a vision to develop his village through the implementation of computer technology knowledge. The purpose of this service is a collaborative program between Buluh Apo village officials and the Mitra Gama Institute of Technology campus to improve the quality of computer science for dropout students to be able to take advantage of this training to provide skills in the computer field so that they can become entrepreneurs in the field of informatics so that they can still participate in advance Buluh Apo village. The training participants totalled 30 dropout students with the achievement of target material including an introduction to computers and Microsoft Office. In this computer introduction training the results of student participation were achieved with an attendance of85%, an increase in student knowledge reaching 75%, students' skills in using Ms Office reached 78%, the ability to use the internet increased by 87%, and the understanding of entrepreneurs in the field of informatics by 84%, and the development of hardware and multimedia reached 77%
Construction and Application of Enhanced Transfer Neural Network for Multiple Time Sequence Signal Recognition

During the work of the equipment, the controlling parameter should be derived from the multi one-dimensional time serial signals. Aiming at this requirement, a novel kind of AU-TL-RES (augment transfer learning residual) neural network is proposed. And its learning algorithm is also introduced. This network can identify the input time serial signals and output the control parameter. AU-TL-RES network includes the data transformed layer, convolutional layer, fully connected layer and ALEXNET base network. AU-TL-RES network can recognize the multi one-dimensional data by transferring the ALEXNET network and introducing the modified residual module. A kind of spatial transformed algorithm for one dimensional data is introduced, which makes the transformed two-dimensional data can adapt the input characteristics of neural network. In addition, transfer learning algorithm, residual algorithm and the defined loss function are studied. The effect of the AU-TL-RES hyper-parameters on its properties has been analyzed. The experimental results show that the loss is 0.00012 for the training data sample, and the training error is 0.1717. The AU-TL-RES neural network is also suitable for intelligent control of other similar devices based on one-dimensional data.
Rhyme: A Data-Centric Expressive Query Language for Nested Data Structures

We present Rhyme, an expressive language designed for high-level data manipulation, with a primary focus on querying and transforming nested structures such as JSON and tensors, while yielding nested structures as output. Rhyme draws inspiration from a diverse range of declarative languages, including Datalog, JQ, JSONiq, Einstein summation (Einsum), GraphQL, and more recent functional logic programming languages like Verse. It has a syntax that closely resembles existing object notation, is compositional, and has the ability to perform query optimization and code generation through the construction of an intermediate representation (IR). Our IR comprises loop-free and branch-free code with program structure implicitly captured via dependencies. To demonstrate Rhyme’s versatility, we implement Rhyme in JavaScript (as an embedded DSL) and illustrate its application across various domains, showcasing its ability to express common data manipulation queries, tensor expressions (à la Einsum), and more.
Getting a Better Sense of Data Drift in Dynamic Systems: Sequence-Based Deep Learning for Monitoring Slowly Evolving Degradation Processes

Deep Learning (DL) for monitoring slowly evolving degradation processes typically involves overcoming data drift, complexity, and unavailability issues resulting from dynamic and harsh conditions and the rarity of labeled failure patterns, respectively. While degradation patterns are mostly hidden in such complex data, observation-based DL is prone to producing uncertain predictions and/or overfit models during the training process. This problem is usually caused by the insignificance of certain data representations. Therefore, and particularly due to the sequential nature of data in such a degradation process, it is necessary to consider neighboring observations to judge the accuracy of a representation or improve it. In this context, instead of providing traditional observation-based learning philosophy, this paper presents data-driven sequential mapping while additionally showing that health indices can also be represented as a vector of sequential data and not as a single regressor output changing a model’s architecture. Using a dataset generated from a mathematical model mimicking bearing degradation life cycles and responding to the aforementioned three main challenges, a comparative study built on investigating observation-based and sequence-based learning paths was conducted. According to a well-defined visual and numerical evaluation criterion, a sequence-based methodology reflects a better understanding of data representations through parameter tuning, achieving better approximation and generalization. Such results support the necessity of such a learning mechanism, especially for sequential data, dealing with some sort of correlation and degrading controversy. The files required to reproduce the findings of this work have been made publicly available.