Reviewerly/exHarmony huggingface dataset:
Quantum Algorithm for the Simulation of Squamous Cell Lung Carcinoma tested through IBM Quantum Composer

Classical algorithms for lung cancer exist based on immunotherapy but are quite prone to error. Non-small cell is a major type of lung cancer, frequently caused by smoking. A classical algorithm to treat non-small-cell lung cancer is compared to a quantum algorithm created. Using superposition of qubits, Born’s rule, and the general qubit rule, an algorithm simulates the pathway of certain driver genes, by finding the angle required of the qubit to be able to result in the probability needed to simulate the pathway, tested in IBM Quantum Composer.
Ensemble Machine Learning Models for Simulating the Missile Defense System

This paper simulated the missile engagement situation using a simulator and conducted a machine learning study based on the generated data. The simulator simulates missile engagements between the enemy and our forces and collects data. The collected data is learned using random forest, XGBoost, and LGBM models after preprocessing. In addition, hyperparameter adjustments were performed for each model to find the optimal parameters. Different metrics for accuracy, F1-score, and ROC-AUC were used for performance comparison. As a result of the experiment, XGBoost showed the best performance in performance indicators, and LGBM was the fastest in terms of learning speed. This paper suggests that XGBoost, which is slow in learning speed but has the best accuracy and performance indicators, is suitable for one-to-one interception situations, and LGBM, which is fast in learning and has excellent performance indicators, is suitable for many-to-many interception situations.
Memetic Algorithm with GPU Optimization

Implementation of memetic algorithms on GPU was proven as an efficient way to speed up the time required to find a solution for diverse problems. In recent years, thanks to the evolution of GPUs, heavily parallelization can be used and the most time-consuming parts of the algorithm were redefined. This paper aims to prove that. It implements memetic algorithms on GPU using CUDA and identifies the computationally demanding parts. It shows that generally known drawbacks are slowly shifted towards new parts of the algorithm. Obtained results are a step forward in the applicability of memetic algorithms on various and high dimensional problems, where they could not be used due to time and computational resource restrictions.
A Study on the Metaverse-Based Environments for Future Vocational Education

The prolonged COVID-19 pandemic has changed offline-oriented education by increasingly employing online methods. Although in the early days, the metaverse was operated to be used as a one-time event rather than for education, it is now gaining attention as a new virtual learning space that interacts with reality. In this study, the metaverse platform Poly Meta and content use cases built by Korea Polytechnics were analyzed.&#x0D; Based on this, we intend to go beyond the usage of the metaverse in general education and further develop metaverse environmental requirements and content in a direction necessary for vocational education. In future research, quantitative and qualitative analysis will be performed on the metaverse platform’s usability and accessibility and on the effect of applying it to vocational education and training
Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud Network

With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.
Renewable Energy Source Technology with Geo-Spatial-Based Intelligent Vision Sensing and Monitoring System for Solar Aerators in Fish Ponds

This chapter’s significance is that it informs readers of how to increase the efficiency of solar panels equipped with indigenous solar tracking devices. If the solar panel efficiency is increased, it becomes easy to power the fish ponds using solar energy, a non-hazardous, renewable, inexhaustible energy source. The key objective is to design a hybrid maximum power tracking algorithm for solar photovoltaic systems with optimal power output to back up the electrical energy crisis for aeration systems in fish ponds. Conversely, the execution of this technique entails an exact control, which is essential to construct a sophisticated system of tracking. By merging the global positioning system (GPS), the artificial neural network (ANN) model with image processing (IP) techniques is utilized to estimate the astronomical characteristics of the sun. This GPS gives the geo-spatial information along with the images of the sun captured by an image sensor. The hybrid algorithm proposed here combines meta-heuristics and conventional ANN using backpropagation algorithm (BPA) with whale optimization perturb and observe (WOPO) and salp swarm perturb and observe (SSPO) algorithms for optimization of weights. The features collected from the sun’s image acquired by the camera utilizing IP approaches are employed as inputs for hybrid algorithms used in a decision-making AI process to distinguish between sunny and cloudy weather situations. The sun tracking system determines the approximate use of astronomical calculations based on the data acquired. Experimentation results are made available on the cloud service for coordination, allowing the proposed high-tech arrangement to be examined and validated.
Alejandro Toledo Manrique, Education and the Future of Latin America. Boulder: Lynne Rienner, 2021. Figures, tables, illustrations, abbreviations, notes, bibliography, index, 246 pp.; hardcover $95, ebook $95.

Alejandro Toledo Manrique, Education and the Future of Latin America. Boulder: Lynne Rienner, 2021. Figures, tables, illustrations, abbreviations, notes, bibliography, index, 246 pp.; hardcover 95. - Volume 64 Issue 4
Database Semantics vs. Predicate Calculus

A content like Fido found a bone. may be viewed from two basic perspectives. One takes the view of an outside observer. For example, Predicate Calculus analyzes the truth relation between a formal sign and a formal model (14.1) or model structure (14.8). This approach is called sign-based.
Laboratory Set-Up of Database Semantics

The analysis of natural language in today’s linguistics, analytic philosophy, and computer science is either (i) agent-based data-driven or (ii) sign-based substitutiondriven. A sign-based ontology has the apparent advantage that it obviates any need for an interface component with sensors for vision and audition, and actuators for manipulation and vocalization. In an age when artificial vision, audition, manipulation, locomotion, and computers did not exist, this was a necessity. The question is how to adjust today’s language research to the age of computers and artificial intelligence by changing from a sign-based substitution-driven to an agent-based data-driven ontology?
Discontinuous Structures in DBS and PSG

In linguistics, grammatical constructions with semantically connected word forms are called ‘discontinuous’ if the word forms are not adjacent. For example, in (i) Peter looked the number up, the semantically related ‘looked...up’ are separated by ‘the_number’, and in (ii) Yesterday Mary danced the semantically related ‘Yesterday... danced’ are separated by ‘Mary’.
Introduction

For long-term incremental upscaling to be successful, the computational reconstruction of a natural mechanism must be input-output equivalent with the prototype, i.e., the reconstruction must take the same input and produce the same output in the same processing order as the original. Accordingly, our computational reconstruction of natural language communication uses a time-linear derivation order for the speaker’s output and the hearer’s input. The surfaces serving as the vehicle of content transfer from speaker to hearer are raw data, e.g., sound waves or pixels, without meaning or any grammatical properties whatsoever, but measurable by natural science.
A psychometric approach to email authorship assertion in an organization

Email services have become an integral aspect of modern communication.Emails can be transmitted digitally without the adequate authentication of the sender.As a result, there has been a considerable surge in security threats coming from email communication, such as phishing, spear phishing, whaling, and malware deposition through emails where recipients can be duped into acting.Authorship assertion of the sender can prevent several security issues, particularly in an organizational setting where an employee's trust can be compromised by faking an email from a colleague or senior without exposing any specific system weakness.A psychometric approach to determining the authorship of an email in an organization is proposed in this research.Machine learning (ML) models have been developed using four classification algorithms.The performance of these ML models has been compared.
Pengaruh Literasi Media terhadap Kemampuan Mahasiswa Mengenal Pernyataan Opini Pada Teks Berita Editorial Kasus Korupsi di Media Massa Baru

This study aims to determine the effect of new media literacy and the ability to recognize opinions in editorial news texts in new mass media. This study used a quantitative research method by distributing questionnaires to 98 students at Mulawarman University. In this study it was found that the media literacy variable had a significant effect on the ability to recognize opinions. This is evidenced by the results of research using a simple linear regression test. First, the significant results must be less than 0.05, in this study sig. is 0.017 and the second, from the results of t count must be greater than t table, namely 2.433 &gt; 1.984. Because of the results of this study, it was concluded that there is an effect of media literacy on the ability to recognize opinions in editorial news texts in new mass media.
The Effect of Using Digital-Based Comic Learning Media on Reading Literacy for Grade 4 Elementary School Students

In this study, it aims to determine the influence of the use of digital-based comic learning media on the reading literacy of grade IV students of SD Negeri Damarsi. This research uses experimental research methods which are part of quantitative approach research is usually used to find the influence of a treatment on a subject or object. Experimental research with a quantitative approach in this study was used to determine the influence of the use of digital-based comic learning media on the reading literacy of grade IV students of SDN Damarsi. Learning media in experimental classes and reading text learning media in control classes. Both classes are assumed to be homogeneous in terms of equal learning ability and differ in terms of the treatment given. This research uses experimental research with a Quasi Experimental Research design. Data collection in this study used tests in the form of pre-test and post-test. And from the results of this study showed that there was a positive and significant influence after learning for 4 times and with the pre-test &amp; post test activities, it was concluded that class IV A during the pre-test produced an average score of 72.27. For the results of the pre-test class IV B, the average score is 69.51. Then the two classes held a post test. Class IV A average resulting score was 88.53. For class IV B the average resulting score is 73.60.
The Role of the School Literacy Movement in Understanding Indonesia Texts for Grade 3 Elementary School

This qualitative research explores the role of the school literacy movement in improving reading comprehension of Indonesian texts among grade 3 students at SD Muhammadiyah 2 Tulangan. The study involves interviews, observations, and documentation as data collection techniques. The analysis follows Mules and Hubermen's theory of data analysis, including data reduction, presentation, conclusion drawing, and verification. The research findings indicate that the school literacy movement at SD Muhammadiyah 2 Tulangan positively impacts reading comprehension through implemented programs. Supporting factors include the active involvement of school members, students' enthusiasm, and adequate facilities. However, hindering factors include limited time for literacy activities due to the Covid-19 pandemic and children's reluctance to read. This research aims to provide valuable insights for readers and serve as a reference for future studies.&#x0D; Highlights:&#x0D; &#x0D; The school literacy movement plays a crucial role in improving reading comprehension among grade 3 students.&#x0D; Supporting factors include active participation of school members, students' enthusiasm, and adequate facilities.&#x0D; Hindering factors include limited time due to the Covid-19 pandemic and children's reluctance to read.&#x0D; &#x0D; Keywords: literacy culture, reading comprehension, Indonesian texts, school literacy movement, grade 3 students
Development of Digital Flipbook Media to Improve Students' Understanding of Social Studies Subjects Grade 5 Madrasah Ibtidaiyah

The purpose of this study is to analyze the effectiveness of the development of digital flipbook learning media to improve student understanding of social studies subjects for class V at MI Muhammadiyah 04 Moropelang. This study uses the type of R&amp;D research with the ADDIE model. using data collection techniques, namely: Questionnaires or questionnaires, tests, and documentation. Meanwhile, the data analysis technique used percentage quantitative analysis technique, and paired T-test analysis. The results showed that in the digital flipbook media media for the distribution of flora and fauna in Indonesia, 90% of media experts, 85% of linguists and 96% of material/content experts found the results so that the media could be said to be very feasible. While the results of the effectiveness show the probability of sig. (2-tailed) of 0.000. So &lt;0.05, it can be concluded that digital flipbook media material on the distribution of flora and fauna in Indonesia has an influence on increasing students' understanding.
AN EVALUATION FRAMEWORK FOR BLOCKCHAIN ADOPTION IN SUPPLY CHAIN

Blockchain technology has been growing in importance and acceptability over the past several years.Yet, there is limited research on the organizational and technology specific factors that play a critical role in driving its adoption.The purpose of this paper is to develop a comprehensive framework for blockchain adoption in the supply chain by identifying the enablers and evaluating their interdependencies and impact on adoption.Enablers of blockchain adoption in the supply chain are identified using an extensive literature review and theoretical lenses from the Diffusion of Innovation (DOI) theory and Technological, Organizational and environmental (TOE) model.To our knowledge, there is only one framework in literature somewhat similar to ours, yet only uses TOE and doesn't consider any alternative blockchain platforms.We know from our earlier research that importance of factors influencing the decisions are impacted by the alternatives.Our research fulfills this research gap in the literature.We, therefore, assert that frameworks not incorporating alternatives have limited applicability.
Development of DNN Accelerator and Its Application in Avionics System

Future avionics systems must have high-performance intelligent data processing capability, especially for various intelligent application scenarios, such as image/radar target recognition, big data analysis, control and decision. Deep neural network (DNN) is a widely used AI algorithm due to its accurate fitness of nonlinear functions. It can introduce huge computation and memory costs as the increase of network depth. Recently, DNN hardware accelerator is proposed as a domain-specific accelerating platform. It can provide a flexible and reconfigurable operating environment for different algorithms, including convolutional neural network and recurrent neural network, with the advantages of high performance, low latency, and low power cost. The key technologies of DNN accelerators mainly include: parallel computing architecture, intelligent processing elements design, efficient memory structure, and dataflow scheduling optimization. This paper studies the design methodology of DNN accelerators, summarizes the key technologies, and discusses the prospective applications in avionics computing systems. This paper will provide some technique supports for the design of next-generation intelligent airborne computing systems.
Topic-Wise Speech Summarization with Emotion Classification

In the era of Internet, there is a tremendous amount of textual and audio data spread all over the place, and it becomes very important to develop a method to fetch the most important information efficiently and quickly. Extracting summary manually is a very redundant and time-consuming process. A good summarizing technique is one where we discern all the important points and topics of a speech or document without leaving out any valuable information. Summarizing a speech without losing the actual context has always been a challenge for programmers for a long time. This paper explores a method to divide a large speech into multiple small speeches to summarize them individually to generate an efficient and precise summary. Each sub-speech is further processed to predict the emotion of the speaker at various points during the speech. These individual emotions are used to classify a generalized emotion of the speaker throughout the speech.
Automatic Machine Learning Model Construction and Performance Validation in Face Recognition

Traditional face recognition models based on deep learning are often affected by the subjective ideas and prior knowledge of developers in terms of feature extraction and model construction. They cannot achieve the most effective results and consume a lot of human resources. In this paper, we proposed a way to build a face recognition model based on Automatic Machine Learning. We performed manual data augmentation on the original dataset called FaceScrub, used the EasyDL automatic machine learning platform, and found the relatively optimal model by adjusting the hyperparameters of the platform. Experiments showed that the effect of data training after manual augmentation is significantly better than that of platform automatic data augmentation. The top-1 recognition accuracy of the final model reached 99%, and the F1-score, Precision Rate and Recall Rate all reached 99%.
Localized Super Resolution for Foreground Images Using U-Net and MR-CNN

Images play a vital role in understanding data through visual representation. It gives a clear representation of the object in context. But if this image is not clear it might not be of much use. Thus, the topic of image super resolution arose and many researchers have been working toward applying computer vision and deep learning techniques to increase the quality of images. One of the applications of super resolution is to increase the quality of portrait images. Portrait images are images that mainly focus on capturing the essence of the main object in the frame, where the object in context is highlighted whereas the background is occluded. When performing super resolution the model tries to increase the overall resolution of the image. But in portrait images the foreground resolution is more important than that of the background. In this paper, the performance of a Convolutional Neural Network (CNN) architecture known as U-Net for super resolution combined with Mask Region-Based CNN (MR-CNN) for foreground super resolution is analyzed. This analysis is carried out based on localized super resolution, i.e., we pass the LR Images to a pre-trained image segmentation model (MR-CNN) and perform super resolution inference on the foreground or segmented images and compute the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics for comparisons.
Data Generation Method and Training Mode of Relationship Extraction Based on Neural Network

As one of the most common and effective techniques for obtaining information, relation extraction is a key task in machine learning. Understanding the current development status of computer technology, we know that the technical methods of extracting information mainly rely on a large number of manual operation and processing features, and with the full promotion of neural networks, it provides a new perspective for actual relationship extraction and data generation. Therefore, on the basis of understanding the current relationship extraction and data generation methods, this paper conducts an empirical analysis of the Chinese character relationship extraction method with convolutional neural network as the core and designs and proposes a Chinese character relationship extraction system scheme based on the convolutional neural network model. Using the convolutional neural network model to automatically obtain features and classify the relationship between the characters mastered, the actual accuracy rate can reach 92.87%, and the average recall rate can reach 86.92%. The final result proves that the relationship extraction data generation method based on neural network has unique application advantages.
Stacked Multi-branch Networks for Single Image Super-Resolution

With the recent superior progress in CNN-based single image super-resolution (SISR), the effectiveness of hierarchical features for SISR has been demonstrated. However, few works concentrate on extracting hierarchical features with multi-scale cases. Furthermore, different practical application scenarios have different requirements on the model size. The realization of resizable model design is still far from ideal. To address these issues, we propose Stacked Multi-branch Network (SMBN) for high-quality SISR. Our proposed multi-branch blocks incorporate features in different scales. We designed two models with different parameter levels based on the proposed method. Both quantitative and qualitative comparisons demonstrated the superior performance and efficiency of our proposed SMBN.