Reviewerly/exHarmony huggingface dataset:
Edge Perception Temporal Data Anomaly Detection Method Based on BiLSTM-Attention in Smart City Big Data Environment

Authors:
raw_author_name: Bin Xia
raw_affiliation_string: Department of Management, Chengyi College, Jimei University, Xiamen, Fujian 361021, P. R. China
raw_author_name: Fanyu Kong
raw_affiliation_string: Chongqing Engineering Technology Research Center for Development Information Management, Chongqing Technology and Business University, Chongqing 400067, P. R. China
raw_author_name: Jiarui Yang
raw_affiliation_string: Department of Management, Chengyi College, Jimei University, Xiamen, Fujian 361021, P. R. China
raw_author_name: Lin Lin
raw_affiliation_string: Department of Management, Chengyi College, Jimei University, Xiamen, Fujian 361021, P. R. China
raw_author_name: Xin Wu
raw_affiliation_string: Department of Management, Chengyi College, Jimei University, Xiamen, Fujian 361021, P. R. China
raw_author_name: Qiong Xie
raw_affiliation_string: Department of Management, Chengyi College, Jimei University, Xiamen, Fujian 361021, P. R. China

Abstract: 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

Authors:
raw_author_name: Manandhar A.

Abstract: 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

Authors:
raw_author_name: M. H. Rahman

Abstract: 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

Authors:
raw_author_name: Jinhee Kim

Abstract: 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

Authors:
raw_author_name: Ioannis Paliokas
raw_affiliation_string: Faculty of Economics & Business Administration, International Hellenic University, 65404 Kavala, Greece

Abstract: 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

Authors:
raw_author_name: Ehab Essa
raw_affiliation_string: Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
raw_author_name: Mohamed Handosa
raw_affiliation_string: Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
raw_author_name: Samir Elmougy
raw_affiliation_string: Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

Abstract: 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

Authors:
raw_author_name: M. Y. Neshcheret
raw_affiliation_string: Russian State Library

Abstract: 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

Authors:
raw_author_name: A. M. Mazuritsky
raw_affiliation_string: Moscow State Institute of Culture; Moscow State Linguistic University

Abstract: 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

Authors:
raw_author_name: Hamid Wijaya
raw_affiliation_string: Universitas Sembilanbelas November Kolaka
raw_author_name: Rima Ruktiari Ismail
raw_affiliation_string: Universitas Sembilanbelas November Kolaka

Abstract: 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
OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION

Authors:
raw_author_name: Imam Nawawi
raw_affiliation_string: Universitas Bina Sarana Informatika

Abstract: Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.
Interaction of Digital Trends and Sustainable Development: The role of Contemporary Art

Authors:
raw_author_name: Olesia Stoliarchuk
raw_author_name: Svitlana Khrypko

Abstract: The purpose of the work is to analyze the impact of digital technologies on the sustainable development of contemporary art - an overview of the main trends that have emerged as a result of the integration of new media into artistic practices. The article methodologically classifies various genres of digital art by means of a critical qualitative analysis of practices, artworks, installations, exhibitions, collectives and events at the forefront of artistic innovation with new technologies. The scientific novelty lies in a comprehensive analysis of digital art through the prism of the concept of sustainable development. The work considers digital art not just as another technological innovation, but as a full-fledged artistic phenomenon of our time. The systematization of artistic practices associated with the use of the latest technologies is carried out, their common conceptual foundations are revealed. In addition, the article deals with issues of environmental sustainability, social responsibility, inclusiveness of digital practices. The dilemmas around the balance of technological innovation and artistic freedom from the perspective of sustainability ethics are outlined. The conclusions provide a conceptual understanding of optimal pathways for developing digital art in relation to societal sustainability.&#x0D; Keywords: digital art, sustainable development, new media art, network art, generative art, interactive art, AI, VR, AR, digital culture
Sentiment Analysis Using Convolutional Neural Network (CNN) and Particle Swarm Optimization on Twitter

Authors:
raw_author_name: Erwin Budi Setiawan
raw_affiliation_string: Telkom University

Abstract: Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline.
Analisis Instrumen Model Mitigasi Banjir di Indonesia

Authors:
raw_author_name: Toha Saleh
raw_affiliation_string: Departemen Teknik Sipil, Fakultas Teknik, Universitas Indonesia

Abstract: One of measures to reduce the flood impact in Indonesia has been done by flood emergency response action plan. The action plan is generated from decision making process assisted by instrument such computer models which have capability in producing scenarios and decision to be taken. This research is conducted to identify how the written procedures (delivered by international and national agencies) related to flood emergency response, being accommodated by those (computer) models. By using qualitative analysis, the element procedures of an effective flood emergency response model have been reviewed. The result shows that the detailed information such human and material resources, facilities information, and warning time-lag, have not been properly and entirely accommodated by the (computer) model. Highlights are also given to propose more effective instrument to improve the management of flood crisis.