Reviewerly/exHarmony huggingface dataset:
Efficient Content Exploration on YouTube: Automatic Speech Recognition-Based Video Summarization

The exponential growth of online video content has resulted in an overwhelming amount of data that requires efficient organization and accessibility. YouTube, being the largest video-sharing platform, contains an immense collection of video content. So, the main aim is to build a system which is focused on developing a YouTube summarizer that utilizes Hugging Face's Automatic Speech Recognition (ASR) technology and incorporates native language support. The YouTube summarizer system, with its integration of Hugging Face's ASR and native language support, aims to enhance accessibility and user experience by providing automated, accurate, and concise summaries of YouTube videos. The system serves as a valuable resource for individuals seeking efficient content exploration and information retrieval, irrespective of language barriers or audio content complexity.
Parallel Computing Approach for Preprocessing AIS Data in Transshipment Feature Extraction

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality.
Book review: Socially just pedagogies, capabilities and quality in higher education: Global Perspectives. Walker, M. &amp; Wilson-Strydom, M. (eds.) 2017.

Review of: Socially just pedagogies, capabilities and quality in higher education: Global Perspectives. Walker, M. & Wilson-Strydom, M. (eds.) 2017.
Peningkatan Keterampilan Pengelolaan Proyek Sistem Informasi Melalui Pelatihan Pembuatan Website Bagi Mahasiswa Universitas Muhammadiyah Jambi

This community service aims to enhance the information systems project management skills of students through website development training. The methods employed include initial study, planning, implementation, and evaluation. The results of the community service demonstrate the successful improvement of students' information systems project management skills through the training and website development activities. Evaluation using questionnaires reveals that all students rated the training as "good". Moreover, the community service proved to be effective in achieving the goal of enhancing students' skills. Recommendations include the continuation of sustained training, further exploration of theoretical concepts, collaboration with industry partners, and ongoing evaluation. The theoretical implications highlight the applicability of information systems project management concepts in website development.
Random State Initialized Logistic Regression for Improved Heart Attack Prediction

One of the primary causes of death in Indonesia is heart attacks. Therefore, an effective method of pre-diction is required to determine whether a patient is experiencing a heart attack. One efficient approach is to use machine learning models. However, it is still rare to find machine learning models that have good performance in predicting heart attacks. This study aims to develop a machine learning model on Logistic Regression algorithm in predicting heart attack. Logistic Regression is one of the machine learning meth-ods that can be used to study the relationship between a binary response variable [0,1] and a set of pre-dictor variables, and can be used directly to calculate probabilities. In this study, a random state is ini-tialized in the Logistic Regression model in order to stabilize the training of the machine learning model and increase the precision of the proposed method. The results of this study show that the proposed model can be a method that has good performance in predicting heart attack disease.
THE PROFESSIONAL CONTEXT OF LEARNING AS A COMPONENT OF THE TRAINING OF THE FUTURE TEACHER OF PHYSICS AT THE UNIVERSITY

The main task of the professional training of the future physics teacher is not only to acquaint him with modern methods, means and forms of education in institutions of general secondary education, as well as to solve the situational tasks of the profession that will take place in his future professional activities. Such activity provides an understanding of real professional situations, has educational and professional aspects and enables feedback between teacher and student, student and student. This activity helps future physics teachers build their own pedagogical trajectory, which in the future can develop into original pedagogical technologies.
Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain

This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatiotemporal vehicle interaction graph, an observed traffic scenario can be transformed into the graph spectral domain by means of the multidimensional Graph Fourier Transformation. Since these spectral scenario representations have shown to successfully incorporate the complex and interactive nature of traffic scenarios, the beneficial feature representation is employed for the purpose of predicting vehicle trajectories. This work introduces GFTNNv2, a deep learning network predicting vehicle trajectories in the graph spectral domain. Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIM shows a performance gain of up to 25 % in comparison to state-of-the-art prediction approaches.
Feature Extraction using Deep Learning and Analyses of Curvature on Facial Shapes across Two Races and between Males and Females

In plastic surgery for facial reconstruction and gender conformity, the aspect of appearance of a natural-looking male/ female face is an important factor in the perfection of the surgery. However, there is a problem that the perfection of the postoperative facial shapes after surgery is greatly influenced by the skill of each plastic surgeon. Therefore, it is useful to verify the male/female areas of each patient’s face in order to create an appropriate shape for each patient. In this study, we generated 100 cross-sectional images per person from 3D models of male and female faces, and trained a convolutional neural network (CNN) using gender and race as the classification criteria The trained CNN was then used to visualize the acquired facial features using Grad-CAM and analyze the feature curves. The results revealed that the characteristics of the curves in specific facial regions represent the gender and racial traits.
Has Open Access become a 'band-aid' for an historical 'innovation-gone-wrong'?

&lt;strong&gt; Open access: a change in publishing with a limited reach &lt;/strong&gt; Open Access is a movement and policy directive dedicated to reforming the closed or subscription-based gatekeeping of scientific research.
The First Pathloss Radio Map Prediction Challenge

To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview paper, we briefly describe the pathloss prediction problem, the provided datasets, the challenge task and the challenge evaluation methodology. Finally, we present the results of the challenge.
Hierarchies of Feeling Structures of Sentiment and Refusal

This collection of works takes the form of a visual essay, an attempt to further explore ideas and themes found in Xine Yao’s Disaffected: The Cultural Politics of Unfeeling in Nineteenth-Century America (2021), which presents unfeeling as a method for disrupting colonialism and other oppressive hierarchies. This collection of images comprises a series of digitally rendered collages that encourage and value uncertainty and mystery. The images in this series are speculative, abstract and non-linear, created in ‘opposition to clarity’, with the intention to reflect the idea of ‘unfeeling’ by withholding or refusing easy understanding.
System Analysis to UML – Restaurant Case Study

The paper presents a detailed examination of the application of Unified Modeling Language (UML) for system analysis within the context of a restaurant business case study. It begins with the articulation of business requirements through user journey understanding and user story definition. The importance of a good user story is emphasized, with a particular focus on the INVEST characteristics that ensure a user story's effectiveness. The paper further delves into the basics of UML, exploring fundamental concepts such as classes, and the various relationships within class structures including association, aggregation, and composition. Additionally, it discusses the significance of polymorphism and interface design in object-oriented programming. The paper also highlights Object-oriented design (OOD) heuristics, which provide developers with a set of guidelines for making informed design decisions. A comprehensive user story, constructed from the restaurant use case, demonstrates the practical application of these concepts. This is followed by the development of corresponding UML diagrams, including a use case diagram, an activity diagram, and a state diagram, which collectively showcase the robust modeling capabilities of UML in capturing complex system requirements and providing a clear pathway for system design and implementation.
Internet of Things: Security and Privacy standards

Ensuring security in the interaction of the Internet of Things (IoT) has focused the attention of many researchers. At present, the issue of standards is a very complex and important one that directly affects both the development and implementation of the Internet of Things in everyday life. There are many standards and protocols that may not be consistent across different layers of the architecture. This paper provides an overview of the current International Internet of Things Security Standards, which are discussed in various aspects such as terminology, architecture models, security and integration scenarios, classification and protocols. A comparative analysis of standards and related research is conducted to help inform decisions about the security of IoT systems during their development and production. This research aims to overcome the difficulties encountered and develop practical recommendations for the selection of controls and security of connected IoT devices. The research methodology included identification of gaps in IoT standards, analysis of existing problems and proposed solutions.
Implementing ALiS: Towards a Reference Architecture for Augmented Living Spaces

Augmented reality (AR) is currently discussed as an approach to promote the personal health of elderly and cognitively impaired people, with spatial AR being a promising, wearable-less solution to enable. an augmented living space (ALiS) that immersively provides and communicates individual, needs-oriented functionalities in the areas of perception, mobility, organization, and medicine. To address the knowledge gap of missing knowledge concerning the implementation of such assistance systems that support autonomy in everyday life, we derived a reference architecture (RA) based on an existing design theory. Our RA contains UML diagrams for components and sequence flows, accompanying text descriptions, and a user interface design. We successfully implemented a prototype to show the RAs feasibility and conducted an expert survey for its general usefulness with positive results. Our contributions add to the prescriptive knowledge base of the community as the results may be adapted by researchers and practitioners.
Human-Computer Negotiations: A Systematic Evaluation of the Effects of Timespan, Tactic, and Search Mechanism

Artificial Intelligence and Computer Agents are having a substantial impact on our everyday lives. The current paper focuses on the prospects of humans negotiating with computer agents in e-commerce settings. We conducted experiments where the subjects negotiated the purchase of mobile plans with computer agents acting as sellers. Three time-based negotiation tactics and two search mechanisms were employed in synchronous vs. asynchronous sessions. The results suggest that computer agents’ negotiation tactics and search mechanisms have significant effects on both the subjective and objective outcomes of the negotiations, while timespan has marginal effects on the agreement rate of the negotiation.
Leveraging the Potential of Conversational Agents: Quality Criteria for the Continuous Evaluation and Improvement

Contemporary organizations are increasingly adopting conversational agents (CAs) as intelligent and natural language-based solutions for providing services and information. CAs promote new forms of personalization, speed, cost-effectiveness, and automation. However, despite their hype in research and practice, organizations fail to sustain CAs in operations. They struggle to leverage CAs’ potential because they lack knowledge on how to evaluate and improve the quality of CAs throughout their lifecycle. We build on this research gap by conducting a design science research (DSR) project, aggregating insights from the literature and practice to derive a validated set of quality criteria for CAs. Our study contributes to CA research and guides practitioners by providing a blueprint to structure the evaluation of CAs to discover areas for systematic improvement.
Managing Organizational Cyber Security – The Distinct Role of Internalized Responsibility

Desirable user behavior is key to cyber security in organizations. However, a comprehensive overview on how to manage user behavior effectively, in order to support organizational cyber security, is missing. Building on extant research to identify central components of organizational cyber security management and on a qualitative analysis based on 20 semi-structured interviews with users and IT-Managers of a European university, we present an integrated model on this issue. We contribute to understanding the interrelations of namely user awareness, user IT-capabilities, organizational IT, user behavior, and especially internalized responsibility and relation to organizational cyber security.
CONCEPTUAL DESIGN OF AUTONOMOUS CART FOLLOWER FOR WHEELCHAIR USER

This paper focuses on kinematics of the cart follower and the system identification of propulsion system by using brushed DC motor. The cart follower uses Ackermann configuration as the steering system. The modeling of kinematics equation takes into account the instantaneous center of rotation (ICR), velocity of each tire, heading angle, and simple movement of the cart. The cart is propelled by transaxle brushed DC motor. It is important to approximate an accurate transfer function to represent the motor as the plant module is unavailable. The motor is simulated by using Arduino hardware package in MATLAB®. Rotary encoder is used to record the angular velocity of the shaft. MATLAB® code is created in order to calculate the linear velocity and tabulate the datasets. System Identification Toolbox determines the transfer function of the motor and its performance. The variables measured in experiment to identify the transfer function of the DC motor system are output angular velocity and input voltage. The parameters taken from the DC motor’s mathematical model are derived based on existing literatures. The graph of output velocity against time is plotted and the transfer function is estimated by using System Identification Toolbox in MATLAB®. From the results, it is demonstrated that the motor exhibits second order system.&#x0D; Keywords: Cart follower, wheel mobile robot, kinematics, system identification
Implementation of MBKM Teaching Assistance Program in Improving Teacher Digital Literacy in Aceh Besar District

The Teaching Assistance Program is a program initiated by the government through Kemendikbudristekdikti. This program aims to provide opportunities for students to sharpen competencies in the 21st century. The formulation of the problem in this research is, what is the level of digital literacy of teachers in schools implementing the MBKM Teaching Assistance Program in Aceh Besar District? This research uses descriptive analysis method. The population of this study were 15 tutors spread across 14 schools in Aceh Besar. Data collection was carried out by distributing questionnaires to respondents, then the data were analyzed using simple statistical formulas. The results of the research on the level of digital literacy of teachers in schools implementing the MBKM Teaching Assistance Program in Aceh Besar District, namely very high category 6.66%, high category 20%, medium category 60%, low category 6.66%, very low category 6.66% . In general, the level of digital literacy of teachers in schools implementing the MBKM Teaching Assistance Program in Aceh Besar is in the moderate category.
BK-SAD: A Large Scale Dataset for Student Activity Recognition

Skeleton-based human action recognition has emerged as a prominent research topic in the field of artificial intelligence due to its broad applicability in a wide range of domains, including but not limited to healthcare, security and surveillance, entertainment, and intelligent environments. In this paper, we propose a novel data collection methodology and present BK-Student Activity Dataset (BK-SAD), a new 2D dataset for student activity recognition in smart classrooms that outperforms the existing NTU RGB+D 120 dataset, SBU Kinect Interaction dataset. Our dataset contains three classes: hand raising, doze-off, and normal activities. The dataset was collected using cameras placed in real classroom environments and consisted of video data from multiple viewpoints. The dataset contains over 2700 videos of students raising their hands, over 1700 videos of students dozing off during class, and over 8500 videos of normal activities. In addition, to evaluate the effectiveness of the proposed dataset, we give some baseline performance figures for neural network architectures trained and tested for student activity recognition on BK-SAD dataset. These ConvNet architectures demonstrate significant performance improvement on the proposed dataset. The effectiveness of the proposed novel data collection methodology and BK-SAD dataset in this paper will enable further research and development of activity recognition models for classroom environments, with potential applications in smart education and intelligent classroom management systems.
Student Attendance Monitoring System using Facial Recognition

In this age of rapidly evolving technologies and trends, performing routine tasks like Attendance Recording should no longer follow the manual or semi-manual tiresome methods. Taking students’ attendance records in the classroom during a lecture period is now a common practice in the University systems especially in most private institutions in Nigeria; a case study employed is Babcock University, Ilisan Remo, Ogun State, Nigeria. This helps to account for where a student is per time, increases student’s learning focus and helps the institution to make the right decision for instance whether to allow a student to take the final exam or not. Attendance marking using conventional methods such as calling students’ names one by one or having them to write it could be quite tedious and time wasting. It becomes more difficult to manage when the class size is large. Another challenge includes the possibility of capturing a proxy attendance such as students writing for their absent colleagues. This study proposes to solve this problem using a biometric information processing known as the facial recognition system. Its application is easier with working range larger than others such as fingerprint, iris scanning and signature. Many algorithms and techniques have been developed to improve facial recognition performance, but the proposed model employs Cascade Classifier which breaks the problem of detecting faces into multiple stages. For each stage, the algorithm performs a very rough and quick test, and if this current stage passes, it does a slightly more detailed test. It eventually detects a face if all stages are passed. The implementation tools include Python, HTML, MySQL, PyCharm, XAMP server as the local host server and a web browser to register and display results. As a result, this development makes attendance monitoring simple, efficient, and time saving. Keywords: Attendance, Monitoring, Biometric, Facial Recognition, Cascade Classifier.
EFEKTIVITAS METODE MENDONGENG UNTUK MENUMBUHKAN MINAT BACA ANAK USIA DINI

The purpose of this study was to find out how effective the storytelling method was to increase early childhood interest in reading. This research is a library research. The description of the data in this study is in the form of a record of all findings regarding the effectiveness of storytelling activities in any research discussion obtained in the literature and sources, also the latest findings regarding increasing early childhood reading interest. The results of this study indicate that the effective storytelling method for increasing interest in reading in early childhood because it is based on the results of the analysis of language development theories and theories of children's brains. Storytelling is a fun activity that can develop children's imagination curiosity. So when children have a high curiosity. Then the child will seek knowledge one of them through reading.