Reviewerly/exHarmony huggingface dataset:
Chaotification of Discrete Dynamical Systems Governed by Continuous Maps and Evolved Fuzzy NN Control

The truth of the false extraction method of digital control system is developed in the false reality to overcome the challenge and continue to design controllers for real systems. However, the scheme to control the false extraction of real system is complex, so it is not suitable for use in practice. In order to verify the asymptotic state with a false reality can be considered by discrete systems, this article calculates a neural neural network (NN) controller with a mean bat algorithm. This view of this evolved fuzzy NN control is performed using the domain of false reality can be considered from model to model transformation polylinear linear rule of thumb, and the new measures on asymptotic stability. Simulation results show that the proposed method can make the system asymptotically stable in discrete time. The dual model of strength and EBA also provides flexibility and efficiency in finding solutions for managed systems.
Divergence Issues in Machine Translation for English-Punjabi Language

Machine Translation is an important field of research in artificial intelligence sector. It is the process of converting one natural language into another with the help of machine learning. This area of research is at bloom now-a-days due to regularly new emerging techniques of machine translation. The motive of machine translation is to give accurate output without human assistance. The quality of output translation can be improved by part of speech tagging, controlled vocabulary etc. However, Machine translation is a hard method to implement. Along with a lot of profits, its execution is a much controlling task. It carries numerous issues with it. Divergence issues are one of the larger challenges in the path of efficient execution of machine translation. The effective study and identification of such issues must be carefully done. Further based on the studies, ways to solve the divergence issues must be planned and executed. Though work has been done on the divergence issues but still it requires more emphasis and study. The paper elaborates the various studies of divergence issues. We have considered Dorr's classification (1993-1994) as a basis of study in this paper. In the paper, we have studied the divergence patterns between English-Hindi-Punjabi languages. The motive of this paper is to discuss the various points of considerations in the divergence issues and study the work which has been done on the issue till now and to suggest the further ideas on the basis of studies of translational divergence.
Effective Refinement of Distinctive Analysis of the Facial Matrices for Automatic Face Annotation

We deal with real world images which contains numerous faces captioned with equivalent names, it may be wrongly annotated. The face naming technique that we propose, exploits the weakly labeled image dataset, and aims at labeling a face in the image accurately. We propose this efficient face naming technique which is self regulated and aims at correctly labeling a face in an image. This is a challenging task because of the very large appearance variation in the images, as well as the potential mismatch between images and their captions. This paper introduces a method called Refined Low-Rank Regularization (RLRR) which productively employs the weakly named image information to determine a low-rank matrix which is obtained by examining many subspace structures of the recreated data. From the recreation method used a discriminatory matrix is deduced. Also, Large Margin Nearest Neighbor (LMNN) method is used to label an image, which further leads to another kernel matrix, based on the Mahalanobis distances of the data and the two consistent facial matrices can be fused to enhance the quality of each other and it is used as a new reiterative method to infer the names of each facial image. Experimental results on synthetic and real world data sets validate the effectiveness of the proposed method.
Utilization of unmanned aerial vehicle (UAV) in dam disaster management: evacuation planning

With the increase of world population and eventually the increase in populated areas at disaster prone areas, a disaster that previously would have been low in risk, is getting more catastrophic each year. Various counter measures are being undertaken to ensure that the impact of a disaster is being well mitigated to ensure the safety and sustainability of the population at risk. This paper will be looking at more depth on the use of Unmanned Aerial Vehicle (UAV) in emergency preparedness, particularly towards the identification effective evacuation routes, in order for the targeted population to safely reach the identified safe haven.
게이미피케이션을 활용한 문화예술교육과정 설계에 관한 연구 - 게임 프로토타입 을 중심으로

This study is about the planning and design of a new art-culture curriculum with the characteristics of Gyeongbuk that raises awareness and interest in “Dokdo,” the representative cultural resource of Gyeongsangbuk-do, and that links Dokdo and art-culture education. The educational goals were set to improve the intimacy of Dokdo for elementary school students in the fifth and sixth grades in Gyeongbuk, to cultivate production creativity, and to deepen their digital literacy. As a solution to the problem defined here, we propose an “art-culture curriculum based on gamification linked to a digital board game.” This curriculum is divided into two sections. Section 1 is an art-culture education section in which students learn game principles and production methods and create game elements. Section 2 is a game in which students play the game they constructed in Section 1 and enjoy reviewing the contents learned in Section 1. As a corresponding research methodology, we applied the nonlinear iteration method of design thinking to our study. Through two nonlinear iterations, we designed a curriculum with minimal functions and a game prototype, . Based on detailed user studies and interviews, actual users in Gyeongbuk were discovered and developed by reviewing the actual situation and requirements of the expected target users. In addition, the draft curriculum was improved based on opinions obtained through four expert consultations. Subsequently, the game platform, a Lo-Fi prototype that reflected these advancements, was simulated and evaluated to design the final curriculum, thereby preparing a Gyeongbuk-style art-culture curriculum with technology fused into it. Our research has several advantages; it is in-depth user research, provides a unique curriculum that links game production and game play, includes a systematic reward system, and uses appropriate game difficulty levels. However there are is a need to improve the degree of 'gameability', such as by deepening the storytelling in the game, strengthening the strategic elements of the gameplay, and strengthening the connection between Dokdo information and the game elements.
ROLE OF HUMAN-COMPUTER INTERACTION

Human-Computer Interaction (HCI) stands at the forefront of our interaction with digital technology, shaping the way we engage with computers, smartphones, and other devices. This research paper explores the historical development, theoretical foundations, and realworld applications of HCI, highlighting its pivotal role in enhancing user experiences and system efficiency. We delve into the theoretical frameworks underpinning HCI, examining its evolution from early usability studies to the modern user-centered design approach.
Correction to: Multilevel Graph Partitioning for Three-Dimensional Discrete Fracture Network Flow Simulations

The publication of this article unfortunately contained a mistake. The assignment to the affiliations of author Satish Karra was not correct
Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.
Library: Science Mentorship Program: Library 101: Finding Articles

This guide was developed for students in the Science Mentorship program, but is a useful resource for any student who wants to learn more about the library and about library research.
Knowledge Management Guidelines in Government Organizations: A Long Way to Go

Following the need for knowledge management in organizational structures, more than 50 years since the beginning of the knowledge management discourse in public and private sector organizations in the world, about 20 years since the first whispers of knowledge management in the country's scientific literature, and also with the announcement of knowledge management in the government organizations in January 2021, these organizations entered the new year with a new agenda but an old conceptual sphere. For a long time, this decree will integrate the spheres of the sector with the concepts related to knowledge management. There are many potential positive aspects in the decree in terms of paying attention to knowledge assets and transferring experiences and lessons learned and best practices along the length and breadth of organizational processes and inter-organizational communication. Translating these potential strengths into actual benefits will help to enhance the organizational environments of the government sector organizations, improve productivity and client satisfaction, and reduce repetitive errors and develop innovation. Achieving these benefits requires careful planning, the use of collective wisdom, the capabilities of scientific centers and the participation of staff and stakeholders. The heart of this process is the transformation from potential to actual, the attention and benefit of the competencies of knowledge management experts who have been trained for many years and are ready to be employed by universities and higher education centers of the country . In this paper, while explaining the decree, the challenges facing the organizations are also examined and solutions are provided.
أثر تطبيق الموبايل البنكي على إدارة علاقات عملاء بنك فلسطين بقطاع غزة (The Impact of Applying Mobile Banking on the Management of Bank of Palestine's Customers Relations in the Gaza Strip)

Arabic Abstract: يهدف البحث إلى التعرف على أثر تطبيق الموبايل البنكي على ادارة علاقات عملاء بنك فلسطين بقطاع غزة من وجهة نظر العاملين فيه. استخدم الباحث المنهج الوصفي التحليلي، وتكون مجتمع الدراسة من جميع العاملين في بنك فلسطين بقطاع غزة، وقام الباحث بتوزيع (400) استبيان بطريقة العينة العشوائية، وتم استرداد (280) استبانة صالحة بنسبة استرداد (70%). أظهرت نتائج البحث وجود علاقة طردية بين الموبايل البنكي وإدارة علاقات العملاء في بنك فلسطين بقطاع غزة من وجهة نظر العاملين. ووجود أثر ذو دلالة إحصائية بين الموبايل البنكي (الفائدة المدركة، الثقة، الخصوصية، سهولة الاستخدام، الأمن، التوافق، التعقيد) وادارة علاقات العملاء (قيمة العميل، وتفاعل العميل، ورضا العميل) كانت ذات تأثير كبير. وأوصى الباحث: بضرورة توفير البنك الإمكانات المادية المتطورة، ومواكبة البرمجيات بما يتلاءم مع التطور العالمي في هذا المجال، واستخدامها في كافة اوجه العمل لرفع كفاءة أداء الموبايل البنكي وإدارة علاقات العملاء وتحسين جودة الخدمة البنكية الإلكترونية المقدمة للعملاء، وكذلك تطوير المتطلبات البشرية من خلال تدريب العاملين على البرامج المتقدمة لإدارة علاقات العملاء وإدارة قاعدة البيانات التسويقية، والترقية المستمرة لوسائل التفاعل مع العملاء وتعظيم قيمتهم، وزيادة رضاهم وولائهم.English Abstract: The research aims to identify the impact of mobile banking on the management of Palestine Bank's customer relation in the Gaza Strip from its employees’ perspectives. The researcher used the analytical descriptive approach, the study society consists of all employees in Bank of Palestine in the Gaza Strip. The researcher distributed 400 questionnaires randomly; 280 valid ones were recollected (70%). The research results showed that there is a direct relation between mobile banking and customer relations management in Palestine Bank in the Gaza Strip from the employees’ perspectives. There is a strong statistically significant impact between mobile banking (perceived benefit, trust, privacy, ease of use, security, compatibility, complexity) and customer relationship management (customer value, customer interaction, customer satisfaction). The researcher recommended:- The Bank should provide advanced hardware and keep up with the software in line with the international development in the field, and their use in all aspects of business to improve the performance of mobile banking, customer relation management, and improve the quality of e-banking service to customers. - Developing human requirements through training the staff on advanced software of customer relation management, Marketing Database Management, continuous upgrading of customer interaction and maximizing their values, and increasing their satisfaction and loyalty.
An Approach of Hidden Markov Model for Offline Yoruba Handwritten Word Recognition

This paper presents a recognition system for \(Yor\grave{u}b\acute{a}\) handwritten words using Hidden Markov Model(HMM). The work is divided into four stages, namely data acquisition, preprocessing, feature extraction and classification. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing, such as: greyscale, binarization, noise removal and normalization accordingly. Features were extracted from each of the normalized words, where a set of new features for handwritten \(Yor\grave{u}b\acute{a}\) words is obtained, based on discrete cosine transform approach and zigzag scanning was applied to extract the character shape, underdot and the diacritic sign from spatial frequency of the word image. The \(Yor\grave{u}b\acute{a}\) handwritten words were subjected to some level of preprocessing to enhance its quality and Discrete Cosine Transform was used to extract the features of the \(Yor\grave{u}b\acute{a}\) handwritten image. A ten(10) state left-to-right HMM was used to model the \(Yor\grave{u}b\acute{a}\) words. The initial probability of HMM was randomly generated based on the model created for \(Yor\grave{u}b\acute{a}\) alphabet. In the HMM modeling, one HMM per each class of the image feature was constructed. The Baum-Welch re-estimation algorithm was applied to train each of the HMM class based on the DCT feature vector for the handwritten word images. Viterbi algorithm was used to classify the handwritten word which, gave the corresponding state sequences that best describe the model. Our experiments reported the highest test accuracy of 92\% and higher recognition rate of 95.6\% which, indicated that the performance of the recognition system is very accurate.
Understanding Internet: How We Shape the New Media and How It Shapes Us?

In this paper, the authors seek to critically questioning the relationship between technology and the mind, machine and man, we also try to understand the medium of the Internet, its nature and the way in which its integration into everyday life is changing everyday life. The For 30 years, Internet has been a technology, but also a medium of mass communication in society, but the discussions about it are mostly reduced to either laud of technology or skepticism, which, as Silva Vaidhyanathan (2018) says, are representing those named as „‘orthodox’ media ecologists“. Therefore, in this paper, we are trying to understand the relationship between technology (media) and the mind (man), starting from the observations made by Mumford, Nietzsche, McLuhan, Freud, to the contemporary thinking of McChesney, Carr, Keene, Vaidhyanathan, Searl. We are considering the ways in which the previous media, including speech, letter, photography, were changing society and human needs, but also new, changed patterns of behavior in everyday life, which were partly created due to our use of the dominant technology of contemporary age. We also study the source of man’s need for new technologies, inventions, machines, his desire for the control of nature and technique, which, seemingly, is easily transformed into the control of the man himself. In this paper we do not give the final answers to the growing problems and issues of today’s networked and technologized world, but we are dealing with reflections on the development of the Internet, communication technologies and artificial intelligence.
Network Traffic Monitoring and Real-Time Risk Warning Based on Static Baseline Algorithm

In this research, firstly the historical data of the sample is analyzed, and the Gaussian process static baseline algorithm is used to study this historical data. The ant colony algorithm is applied to optimize the structure and, finally, the basic value of the Gaussian process static baseline algorithm is realized. The adjustment method achieves real-time monitoring of network traffic and provides early warning of network risks through a series of studies. The research results indicate that the baseline calculation method used in this study produces greater accuracy than the traditional limitcalculation method, and can also make better use of the statistical characteristics of noise. Later, we will study the algorithm of data distribution according to time series in a noisy environment, compare this method with other kinds of prediction algorithms, and promote it. In summary, the network traffic monitoring system developed and designed in this study meets the basic daily usage needs, and also meets the index requirements of the cooperative project.Keywords: Baseline algorithm; Gaussian process; Traffic monitoring; Risk warningCite AsL. Fu, Z. Jingli, Network Traffic Monitoring and Real-Time Risk Warning Based on Static Baseline Algorithm, Engineering Intelligent System, vol 29 no 3, pp. 183-189, 2021.
Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access management over encrypted knowledge within the cloud. However, previous CP-ABKS schemes were designed to support single multi-owner setting, and can't be directly applied within the shared multi-owner setting (where every record is commissioned by a set variety of information owners), while not acquisition high process and storage prices. Additionally, thanks to privacy issues on access policies, most existing schemes square measure susceptible to off-line keyword-guessing attacks if the keyword house is of polynomial size. what is more, it's tough to spot malicious users United Nations agency leak the key keys once quite one knowledge user has a similar set of attributes. during this paper, we tend to gift a privacy-preserving CP-ABKS system with hidden access policy in Shared Multi-owner setting (basic ABKS-SM system), and demonstrate however it's improved to support malicious user tracing (modified ABKS-SM system). we tend to then prove that the projected ABKS-SM systems bring home the bacon selective security and resist off-line keyword-guessing attack within the generic additive cluster model. we tend to conjointly appraise their performance mistreatment real-world datasets
Copy Move Forgery Detection Using CNN on Pre-Trained Model: BusterNet

With advancing technologies and plethora of editing tools it has become cumbersome to differentiate between real image and forged image. We introduce a Deep Neural Network for detecting passive image forgery. More precisely, this model is used for Copy Move Forgery Detection i.e. CMFD. Copy-move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The architecture addresses two major limitations of older algorithms, firstly, it is end to end detection and secondly it produces source and target masks more accurately with varying threshold values. In recent years many models are being developed to detect forgery, In our model we have changed many existing things either by upgrading old data to newer version or by adding new technology to increase the efficiency of model. For example we will be using new libraries of Tensorflow and Keras to build whole model, also we will be adding some more data sets available publicly or from our side. We will change the pre existing values of data to newer one like standardization pixels of images from data set with the help of Convulational Neural Network. Experiment results are expected to change by developing new change in pre-existing model.
Peer Review - Quality analysis of a learning media analog electronics on the Android platform with ISO 25010

The design of analog electronics learning media applications needs quality analysis. ISO 25010 is one of the standard references in measuring the quality of an Android-based application product. Analysis that can be done is by testing the compatibility and performance efficiency aspect of the media being developed. This study uses a Research and Development (R&D) method with the ADDIE development model which will produced a quality analog electronics learning media application. Data collection was conducted used a cloud testing and direct testing. Before data collection was conducted validity of media by expert judgement. The results showed that media is valid and the quality of the compatibility aspect this media learning was supported on more than 208 smartphones android device and performance efficiency predicate “Satisfied” so that the application could be used for the learning process during the COVID-19 pandemic or tested on other quality aspects.