Reviewerly/exHarmony huggingface dataset:
Application of Augmented Reality in Manufacturing

AR is a distinctive interface between humans and machines that overlays virtual information generated by computers onto a practical application environment. In the past few years, it has been discovered that AR has the potential to be used in various fields, such as military training, maintenance, assembly, product design, and other production processes. AR is now playing a key role in overcoming the problems of integrating technology to speed up Industry 4.0. It enables workers to convert from mass production to mass customization. AR technology is gaining popularity because of the ease with which it can be implemented in the production and wide accessibility of hardware devices, including smartphones and tablets, that are capable of supporting it. This chapter provides a comprehensive evaluation of the latest AR applications and advancements in the manufacturing industry. Efforts are also made to understand the major challenges in AR applications and the scope for overcoming them in the future.
Causal Inference in Recommender Systems: A Survey and Future Directions

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality , not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.
A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.
Data on investigation of potential gold mineralization sites in Tchollire and Environs (North Cameroon)

Fresh and altered rock samples were collected and analyzed during field and laboratory studies in Tchollire and Environs. This approach aimed at the delineation of hydrothermal alteration minerals which according to the geological and mining settings of Tchollire and Environs, may be associated with gold mineralizations sites. Field investigations were achieved during the dry season to ensure the representativeness and reliability of our samples whose collections were constrained by earlier remote sensing and geophysical studies as expressed in [1]. An optical microscope both in reflected and transmitted light was used for petrographic analyses of thin and polished sections of rock samples. Other rock samples were prepared for spectral measurements which were achieved using an analytical spectral device spectrometer. The data presented here are further interpreted and discussed in [1].
Improving the accuracy of predicted first-arrival times using deep learning

Recently, the task of first-arrival time picking for seismic shot gathers has been treated as an image segmentation problem, and deep-learning (DL) algorithms have been successfully used to predict first-arrival times. Currently, researchers mainly focus on leveraging cutting-edge DL algorithms to improve the performance of DL in first-arrival picking. There are few publications addressing the quality control of the results predicted by DL. We develop a three-step workflow to improve the accuracy of first-arrival time detection computed using DL algorithms. First, we obtain three predicted results (generation I) by applying the holistically nested U-net (HU-net) to seismic shot gathers, the envelope of seismic shot gathers, and the cosine of the instantaneous phase of seismic shot gathers. Subsequently, we obtain generation II picking by statistically analyzing the predicted generation I picking. Finally, we treat the first-arrival picking task as a constrained path search problem and the generation II picking function as the constraints. The developed workflow is applied to real seismic surveys to demonstrate its effectiveness.
Analysis of Effective Strategies and Teacher Motivation in Improving Literacy and Numeracy at State Elementary School Besowo 5 Kediri: Findings and Implications

State Elementary School Besowo 5, Kepung District, Kediri, is an elementary school that succeeded in achieving high literacy and numeracy results on the education report card and achieved recognition as the Best BOSP Performance in Kediri Regency, even though the school experienced infrastructure challenges such as limited road access and limited cellular signal. The aim of this research is to explore the challenges, strategies and collaborative efforts of teachers in improving students' literacy and numeracy skills. The method in this research uses case studies through observation, interviews and document analysis. The analysis technique of this research uses triangulation techniques. Through the use of educational report cards as a basis, SDN Besowo 5 teachers provide special additional hours by utilizing a variety of literacy and numeracy materials, implementing various learning strategies, including guidance on finding keywords, Polya steps, using innovative worksheet, involving students as peer tutors, and provide treatment according to students' abilities. Apart from that, teachers also collaborate. Teachers' motivation for collaboration includes, among other things, improving school achievement, developing knowledge, and strengthening the school's positive image in the eyes of the community.
Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods

Abstract High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference ( LF2I ) approach that makes it possible to construct confidence sets with the p -value function and to use the same function to check the coverage explicitly at any given parameter point. Like LF2I , this extension yields provably valid confidence sets in parameter inference problems for which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology 3 3 Code to reproduce all of our results is available on https://github.com/AliAlkadhim/ALFFI . .
EXPLORING FACTORS ASSOCIATED WITH SEROLOGICAL RESPONSE TO SARS-COV-2 VACCINES IN INDIVIDUALS WITH INFLAMMATORY BOWEL DISEASE: INTERACTIVE VISUALIZATION METHODS FOR MULTIDIMENSIONAL DATA

SARS-CoV-2 vaccine response may be reduced in seniors (age 65+) with inflammatory bowel disease (IBD) and those on anti-TNF therapy. A challenge of the COVID-19 era has been conveying rapid-evolving health information, especially for groups with higher risk of poor outcomes of COVID-19 such as seniors and immunocompromised individuals.
Edge Perception Temporal Data Anomaly Detection Method Based on BiLSTM-Attention in Smart City Big Data Environment

The improvement of edge perception layer anomaly detection performance has an immeasurable driving effect on the development of smart cities. However, many existing anomaly detection methods often suffer from problems such as ignoring the correlation between multiple source temporal sequences and losing key features of a single temporal sequence. Therefore, a new anomaly detection method using BiLSTM and attention mechanism is proposed. First, a fusion algorithm TCDCD was formed by combining Data Correlation Detection (DCD) and Temporal Continuity Detection (TCD) to preprocess Edge Perception Data (EPD). Then, BiLSTM is employed to gather deep-level features of EPD, and the attention mechanism is utilized to enhance important features that contribute to anomaly detection. Ultimately, the SoftMax classifier is employed to categorize abnormal data. The experimental findings from the SWaT and WADI datasets demonstrate that the suggested method achieves better performance than other newer anomaly detection methods. Among them, the accuracy, precision, recall and F1 of the proposed method on the SWaT dataset were 96.62%, 94.32%, 96.02% and 94.30%, respectively. In terms of performance, it is superior to traditional EPD anomaly detection models, and has good representational and generalization capabilities.
Towards Effective Solid Waste Management: A Mobile Application for Coordinated Waste Collection and User-official Interaction

Solid Waste Management is an especially important task related to human health and the environment. Due to ineffective scheduled date &amp; time, poor communication between waste collecting institutions and local house owners, people are compelled to throw waste on streets which is not good. Even if there is a routine, people tend to miss the schedule. Our aim is to develop an application for mobile phones, which consists of two parties- the user and waste management officials, where the second one acts as reminders. Officials will send a notification to the user, signaling that they are at a certain checkpoint near the user and the user can now throw waste properly and not on the streets. An incremental model was used throughout our project; basic requirements are fulfilled first and then iterated to create the final product. The proposed application includes two portals for whether you are user or waste management personnel. This application helps to improve the coordination between clients and collectors and determines whether the waste in an area has been collected or not. The survey conducted in this study involved consulting the Environment and Agricultural Department of Kathmandu Metropolitan City, which highlighted the significance of a notifying application. This application addresses the issue of uncoordinated waste disposal by providing users with information about collection schedules, leading to better waste management practices and reduced unsystematic garbage disposal.
Comparative Study: Performance of MVC Frameworks on RDBMS

The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter &amp; Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium &amp; large-scale organization in choosing the appropriate PHP Framework.
Differences in student-AI interaction process on a drawing task: Focusing on students’ attitude towards AI and the level of drawing skills

Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in performing a public advertisement drawing task. Based on the empirical evidence obtained from the think-aloud protocols of 20 Korean undergraduate students, the study first conducted a lag sequential analysis to identify statistically significant linear patterns of each group and then chronologically incorporated them into the SAI duration via coded activity alignment series to distinguish the overall SAI process of each group. The study revealed the distinctive differences in SAI processes of students with different attitudes towards AI and drawing skills. To better facilitate student-AI teams for learning, a range of implications of educational AI development and instructional design is discussed. Implications for practice or policy: Educational AI should not be limited to performing a specific task and solving well-defined problems. It should be designed with a holistic view of the end-to-end student-AI process, interconnected to different learning activities in the learning process. Educational AI should be capable of increasing students’ metacognition and emotional engagement. An educational AI system architect team inclusive of diverse stakeholders should be formed to collaboratively design the AI system.
Virtual/Augmented Reality Applications in Education &amp; Life Long Learning

Recent advancements in educational technology highlight the significance of disruptive technologies in enhancing Learning Experiences (LX) in a more targeted, personalized, and engaging context [...]
Wireless body area sensor networks based human activity recognition using deep learning

Abstract In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).
Neural networks in libraries: A new development in bibliographic services

Based on the review of professional literature and selected monitoring of Russian and foreign library websites, the author introduces readers to the Artificial Intelligence (AI) technologies for bibliographic services. She focuses on artificial neural networks as one of the most popular AI instruments. In particular, she discusses practical experiences of using AI in bibliographic services and supporting library and information areas, including information indexing. The author provides examples to demonstrate the AI potential for higher quality and performance in bibliographic and user services, e. g. interaction with users through chat bots, advanced bibliographic search, references, personalized information awareness services, etc. The AI instruments including neural networks are also used for metadata extraction and bibliographic records generation. Today, library chat bots and text intellectual analysis systems are the most developed services. The findings of the study demonstrate that AI technologies enable to accomplish user bibliographic services, though not all AI benefits and drawbacks have been revealed yet. The author suggests that expanding AI sphere would enable to improve quality and efficiency of bibliographic services, competences of bibliographers, and to introduce new types and forms of bibliographic services. The article is prepared within the framework of R&amp;D “The model of reference and bibliographic services for the national libraries” (reg. No. 122021800339-7) of the Russian State Library.
Specific aspects of the Russian federal and scientific libraries' support of the libraries in the RF new entities

The author focuses on the book charity drives to support the libraries in Donetsk and Lugansk republics, Kherson and Zaporozhye regions, and examines the contribution of state and non-government organizations. The experience of the Great Patriotic War (WWII) in restoration of the libraries in cleared areas and the possibilities to use this experience today, is explored. The need for coordination center for developing collections in the new territories is substantiated. The challenges of these libraries’ entering the RF digital space are discussed. The author analyzes the role of federal libraries in professional retraining of the librarians in the new regions, and the content of the programs offered by leading library and information centers for the librarians in Donetsk and Lugansk republics, Kherson and Zaporozhye regions. The author argues that the substantive side of the libraries in the new RF areas has to be reformatted in recognition of the hostile Ukrainian policy that the above libraries had to communicate for several decades.
Implementasi Model Digital Forensik Procces Untuk Sosial Media Investigation Dengan Tools Hunchly

Perkembangan teknologi yang semakin pesat dapat menjadi dampak posistif dan dapat negatif. Dampak positifnya adalah proses penyebaran informasi yang semakin cepat, namun dampak negatifnya adalah banyaknya informasi hoax dan hate space yang terjadi di media sosial akibat tidak adanya filter dan pengecekan keabsahan informasi. Hal ini juga mengakibatkan banyaknya tindak kejahatan yang terjadi di media sosial. Salah satu pemecahan masalah dengan banyaknya kasus yang terjadi akibat penyalahgunaan media sosial adalah dengan melakukan proses invesitgasi forensic pada social media. Proses investigasi forensik ini dilakukan dengan metode live investigation menggunakan metode digital forensik proses yang terdiri dari proses Collection, Examination, Analysis dan Reporting. Proses pengumpulan barang bukti dengan menggunakan tools hunchly sehingga dapat dilakukan proses investigasi secara langsung. Hasil dari penelitian ini adalah dengan memanfaatkan metode digital forensik proses dan penggunaan tools hunchly untuk melakukan teknik live investigation untuk melakukan pengumpulan bukti digital dari akun-akun media sosial yang melakukan tindak kejahatan di media sosial sehingga dapat di proses sesuai dengan peraturan hukum yang telah berlaku Kata kunci: Bukti Digital, Media Sosial, Digital Forensik, Hunchly ------------------------------------------------------------------- The increasingly rapid development of technology can have both positive and negatif impacts. The positive impact is that the process of disseminating information is getting faster, but the negatif impact is the large amount of hoax information and hate space that occurs on social media due to the absence of filters and checking the validity of the information. This also results in many crimes occurring on social media. One solution to the problem with the many cases that occur due to misuse of social media is to carry out a forensic investigation process on social media. This forensic investigation process is carried out using the live investigation method using a digital forensic process method consisting of Collection, Examination, Analysis and Reporting processes. The process of collecting evidence uses powerful tools so that the investigation process can be carried out directly. The results of this research are by utilizing digital forensic process methods and using hunchly tools to carry out live investigation techniques to collect digital evidence from social media accounts that commit crimes on social media so that it can be processed in accordance with applicable legal regulations. Keywords: Sosial Media, Digital Forensics, Hunchly