Reviewerly/exHarmony huggingface dataset:
Beyond Well-Intentioned: An HCI Students’ Ethical Assessment of Their Own XR Designs

Foreseeing the impact of augmented and virtual reality applications on users and society is challenging. Thus, efforts to establish an ethical mindset and include technology assessment techniques in HCI education are increasing. However, XR educational courses fostering students’ reasoning and perceived responsibility in designing ethical applications are lacking. We, therefore, developed the explorative design method Reality Composer to investigate and foster the students’ assessment of the ethical impact of and responsibilities in XR application design. We conducted a workshop with 40 international HCI master students applying this method and analyzed the resulting application concepts regarding the students’ ethical assessment. Our findings show that they critically discussed their concepts’ impact and identified potential countermeasures for negative social implications. However, they overestimated the users’ responsibility to securely use XR applications as well as a positive design intention. We contribute with our findings and developed method to understand students’ potential and derive future course design implications.
PinchLens: Applying Spatial Magnification and Adaptive Control-Display Gain for Precise Selection in Virtual Reality

We present PinchLens, a new free-hand target selection technique for acquiring small and dense targets in Virtual Reality. Traditional pinch-based selection does not allow people to precisely manipulate small and dense objects effectively due to tracking and perceptual inaccuracies. Our approach combines spatial magnification, an adaptive control-display gain, and visual feedback to improve selection accuracy. When a user starts the pinching selection process, a magnifying bubble expands the scale of nearby targets, an adaptive control-to-display ratio is applied to the user’s hand for precision, and a cursor is displayed at the estimated pinch point for enhanced visual feedback. We performed a user study to compare our technique to traditional pinch selection and several variations to isolate the impact of each of the technique’s features. The results showed that PinchLens significantly outperformed traditional pinch selection, reducing error rates from 18.9% to 1.9%. Furthermore, we found that magnification was the dominant feature to produce this improvement, while the adaptive control-display gain and visual cursor of pinch were also helpful in several conditions.
Web Service for Palindrome Detection from Algorithm Sequence

This research focuses on developing a Web Service for Detection Palindromes. The system incorporates the innovative concepts of the LPrF table and the Online Position Heap data structure to identify palindromic sequences efficiently. Palindromes are symmetric strings or sequences that read the same forward and backwards. They have implications in various fields, including genetics and computer science. The system allows users to input genome sequences and detect palindromes by clicking the “Find Genome” button. The calculation time depends on the number of characters in the input sequence. The web service is developed using JavaScript, ensuring platform independence and smooth operation over the internet. This research contributes to academia by providing an efficient and versatile solution for detecting palindromes in alphabetical order.
Insulator Self-explosion Defect Detection Based on Image Semantic Segmentation

Insulators, a special insulation control, play a role in overhead transmission lines. Insulators exposed to the natural environment for a long time are prone to self-explosion, resulting in line failure and interruption of power transmission. Therefore, insulator self-explosion defect detection is important. To detect insulator self-explosion defect in complex background images, we decompose the target task into two steps: image segmentation and object location and finally realize the automatic detection of insulator self-explosion defect on our segmentation images. For the segmentation task, we propose an insulator string segmentation model based on affinity and contour boundary modelling, which improves the segmentation performance. For the detection task, Yolov4 is used to locate insulator self-explosion defects based on our segmentation images. The experiments show that the AP50 on our segmentation images is 3.6% higher than that on the original images, and the F1-score on our segmentation images is 0.18 higher than that on the original images.
IMPLEMENTASI E-ARSIP BEBAN KERJA DOSEN UNIVERSITAS ISLAM INDRAGIRI

The development and advancement of information technology is currently running very fast, current technology has an impact on increasing effectiveness and efficiency in carrying out each job. reporting tri dharma activities carried out by lecturers, therefore the lecturer's workload must be distributed proportionally and measurably by utilizing the development of information technology. One of them is the Lecturer Workload E-Archive which can assist the Head of Study Program in finding out information on the workload borne by each lecturer. In the design of the Lecturer Workload E-Archive, Waterfall is a system development method that functions as a mechanism for identifying software, as well as UML (Unified Modeling Language) as modeling, and uses the PIECES method as a system analysis to facilitate researchers in building systems. Testing is carried out using Blackbox which plays a role in overcoming tests on the system software section and a questionnaire to determine system user satisfaction. With the Lecturer Workload E-Archive (BKD) the data is centralized so as to avoid duplication of data and make it easier to find and track information for lecturer assessment and fulfill the required accreditation documents
Random Online Hashing for Cross-Modal Retrieval

In the past decades, supervised cross-modal hashing methods have attracted considerable attentions due to their high searching efficiency on large-scale multimedia databases. Many of these methods leverage semantic correlations among heterogeneous modalities by constructing a similarity matrix or building a common semantic space with the collective matrix factorization method. However, the similarity matrix may sacrifice the scalability and cannot preserve more semantic information into hash codes in the existing methods. Meanwhile, the matrix factorization methods cannot embed the main modality-specific information into hash codes. To address these issues, we propose a novel supervised cross-modal hashing method called random online hashing (ROH) in this article. ROH proposes a linear bridging strategy to simplify the pair-wise similarities factorization problem into a linear optimization one. Specifically, a bridging matrix is introduced to establish a bidirectional linear relation between hash codes and labels, which preserves more semantic similarities into hash codes and significantly reduces the semantic distances between hash codes of samples with similar labels. Additionally, a novel maximum eigenvalue direction (MED) embedding method is proposed to identify the direction of maximum eigenvalue for the original features and preserve critical information into modality-specific hash codes. Eventually, to handle real-time data dynamically, an online structure is adopted to solve the problem of dealing with new arrival data chunks without considering pairwise constraints. Extensive experimental results on three benchmark datasets demonstrate that the proposed ROH outperforms several state-of-the-art cross-modal hashing methods.
INVys: Indoor Navigation System for Persons with Visual Impairment Using RGB-D Camera

This research presents the INVys system aiming to solve the problem of indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera. The system utilizes the depth information provided by the camera for micronavigation, which involves sensing and avoiding obstacles in the immediate environment. The INVys system proposes a novel auto-adaptive double thresholding (AADT) method to detect obstacles, calculate their distance, and provide feedback to the user to avoid them. AADT has been evaluated and compared to baseline and auto-adaptive thresholding (AAT) methods using four criteria: accuracy, precision, robustness, and execution time. The results indicate that AADT excels in accuracy, precision, and robustness, making it a suitable method for obstacle detection and avoidance in the context of indoor navigation for persons with visual impairment. In addition to micronavigation, the INVys system utilizes the color information provided by the camera for macro-navigation, which involves recognizing and following navigational markers called optical glyphs. The system uses an automatic glyph binarization method to recognize the glyphs and evaluates them using two criteria: accuracy and execution time. The results indicate that the proposed method is accurate and efficient in recognizing the optical glyphs, making it suitable for use as a navigational marker in indoor environments. Furthermore, the study also provides a correlation between the size of the glyphs, the distance of the recognized glyphs, the tilt condition of the recognized glyphs, and the accuracy of glyph recognition. These correlations define the minimum glyph size that can be practically used for indoor navigation for persons with visual impairment. Overall, this research presents a promising solution for indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera and proposing novel methods for obstacle detection and avoidance and for recognizing navigational markers.
Strategic Leadership and its Relationship to Administrative Creativity among School Principals from the Point of View of the Administrative Body in Public Education Schools in Jeddah

The study aimed to shed light on the reality of the practice of strategic leadership and administrative creativity among school principals from the point of view of the administrative body in general education schools, the descriptive approached applied. A questionnaire consisting of (37) items distributed to the study sample of (365) employees. The results showed that the practice of strategic leadership and administrative creativity in general education schools came with a high degree of approval. The study indicated that there are statistically significant differences between the responses of the study individuals on the axis of the practice of school principals in general education schools in Jeddah for the levels of administrative creativity in different years of experience, and it was shown through the mean ranks These differences are in favor of the study individuals who have more than 10 years of experience (expert), and the study recommended the necessity of involving school principals of the administrative body in formulating its strategic vision and allocating financial resources for the development of human capital and spreading the culture of ethical practices among members of the administrative body.
Edukasi Pertolongan Pertama pada Luka Ringan di SDN Wates 02

This study aims to develop an educational program on providing relief for minor injuries at SDN Wates 02. Minor injuries are common occurrences that require proper handling to prevent infections and further complications. However, a lack of knowledge and skills in administering first aid for minor injuries can lead to more serious health problems. The research method used is a developmental study with a qualitative approach. Data were collected through training sessions conducted for grade V students at SDN Wates 02 by the service team from the KSR SUB UNIT PGSD FIPP UNNES. An educational program was developed that includes information about various types of minor injuries, proper handling measures, and the importance of maintaining hygiene. The program also involves hands-on demonstrations and simulations of minor injury situations. These simple steps make it easier for students to master and understand how to perform first aid for minor injuries. The results of the training showed that this educational program was effective in improving the knowledge and skills of grade V students at SDN Wates 02 in providing first aid for minor injuries. This program successfully attracted enthusiastic participants, raising awareness about the significance of proper treatment for minor injuries and providing a solid foundation for dealing with daily injuries. Therefore, to optimize this program, it is recommended to conduct regular evaluations and updates to ensure that the information and knowledge provided are always in line with the latest developments in providing relief for minor injuries. Additionally, collaboration between schools and other parties, such as medical personnel, is also necessary to enhance the effectiveness of this educational program.
Learning Outcomes and Social Emotional Competences: A Cross-Feeding Relationship to Promote Meaningful Learning Experiences

Since the beginning of the employment of Learning Outcomes (LO), the educational field has been marked by significant changes. This approach, by being deliberately learner-oriented, influenced several knowledge fields to promote relevant shifts in different levels of educational programs offered by prominent institutions. Parallelly, the last decades have been characterized by an increasing concern with the development of Social Emotional Competences (SEC) in educational contexts. In this complex scenario, the main aim of this article is to reflect on the relevance of teachers’ social emotional competences in the construction of a healthy learning environment that equally embraces students with different social and cultural backgrounds. Thereby, the current work presents two specific aims: 1) to review the literature that discusses the connections between education and social emotional learning; and 2) to discuss the relevance of cultural identities and SEC for LO in an increasingly intercultural world. To achieve these objectives, the Qualitative Document Analysis’ methodology was employed to identify, examine and select the scientific sources to support the knowledge development which background the proposed discussions. Considering the intrinsic link between cultural identity and learning outcomes, this article defends the development of educators’ social emotional competences as a pathway to provide not only the best educational experiences for students with different cultural backgrounds but also to contribute to the construction of equitable societies by fostering the development of students’ intercultural competences.
A Comprehensive Model for Recommending Personalized Learning Resources for the Development of Linguistic Competence

With the continuous advancement of globalization and informatization, the linguistic competence of college students has become a key index for evaluating their comprehensive quality. Faced with diverse needs of students and educational environments, it is increasingly important and complex to accurately locate the linguistic competence goals of college students. Although existing research methods, such as standardized testing and teacher assessment, provide certain convenience, they rely on single data sources and have a certain degree of subjectivity, which limits their universality and accuracy. This study aimed to solve this problem by doing comprehensive research on two aspects: first, curriculum analysis based on relation extraction. A relation extraction model, such as Casrel, was used for advanced text analysis, which provided educators with more in-depth insights; second, personalized learning material recommendation based on text recommendation. Personalized learning paths were provided for students of different levels using the abstractness-based text recommendation algorithm. This study not only filled the gaps in existing research methods, but also provided a new, scientific and efficient solution, helping improve the quality of education and promote the formulation of scientific education policies.
The Relationship Between Learning Motivation and Online Learning Performance: The Mediating Role of Academic Self-Efficacy and Flow Experience

Learning motivation is one of the key factors influencing students’ engagement in online learning. This study aims to explore the relationship between learning motivation and online learning performance and to delve into the mediating roles of academic self-efficacy and flow experience in this relationship. A questionnaire survey was conducted with 427 online learners, and structural equation modeling was employed for analysis. The results indicate that learning motivation has a positive effect on online learning performance. Academic self-efficacy and flow experience play a mediating role in the relationship between learning motivation and online learning performance. When students possess higher levels of academic self-efficacy, they are more likely to actively engage in learning, thereby improving their learning performance. Simultaneously, flow experience plays a significant role during the learning process. When students experience a state of flow, learning becomes more enjoyable and efficient, consequently enhancing academic achievement. Therefore, educators and educational institutions can take measures to cultivate students’ academic self-efficacy, such as providing positive feedback and support and encouraging students to face challenges. Additionally, creating a positive learning environment that fosters flow experiences can help improve students’ academic performance.
An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive.
A Novel and Efficient Spatio-Temporal Clustering Algorithm to Discover Patterns in Indian Earthquake Data

The seismic activity in India, including the Himalayas, the North-East region, and the Andaman-Nicobar Islands, is prominently displayed on the seismic map. It is important to analyze the specific characteristics of seismic events on the Indian sub-continent. This research paper introduces a new algorithm for data analysis using a partitioning technique that mines clustering patterns. The algorithm generates clusters of spatial and spatio-temporal data by distributing the data into spatial bins or buckets, identifying neighboring bins, and minimizing distance calculations. Furthermore, the selection of centroids is based on the density of data in the spatial region, utilizing a random selection method. It requires minimal parameter settings, enhancing its efficiency and practicality for analyzing large-scale spatio-temporal datasets. We validate the algorithm's efficacy through experiments conducted on the Indian earthquake spatio-temporal dataset, demonstrating its ability to detect spatio-temporal patterns and identify earthquake-prone regions.The conducted experiments reveal a correlation between the frequency of earthquake events and the number of clusters formed, indicating that regions with a higher occurrence of earthquakes exhibit a greater clustering tendency, signifying their susceptibility to seismic activity. The results imply promising clustering quality, with Silhouette index in the range of 0.88 to 0.93.
CRITICAL DISCOURSE ANALYSIS: DONALD TRUMP SPEECH BROUGHT THE U.S. TO PANDEMIC PREVENTION FAILURE

This research focuses on Donald trump’s speech on preventing COVID-19 at White House Conference on May 18, 2020. His speech was about the suggestion on taking hydroxychloroquine which known as an anti-malaria. The method used in this research is mix method, as the data displayed using description and numbers and the analysis data of this research uses critical discourse theory by Fairclough, and meta function within systematic functional linguistic proposed as the tool to scrutinized the object of the research through transitivity analysis, modality analysis, and textual analysis. As for the result of the research shown that (1)the most process used by trump is material process; (2)mental process, in which becomes the reason of the audience has unconsciously followed the irresponsible suggestion by Trump; (3)modality analysis shows that Trump rarely used modal verb; (4) textual analysis shows that Donald Trump’s speech has been delivered coherently and transitivity analysis found two the new process forms, both processes are existential process + relational process and relational process + existential process.
New Approach to Automatic Fault Extraction by Utilizing LSTM Neural Networks

Summary This paper presents a novel method of geological fault detection utilizing LSTM (long-short term memory) neural network. LSTM networks excel in sequences classification and are widely used in natural language applications and series forecasting. The new approach was developed to consider the 3D nature of seismic data by splitting and modifying the data to comply with the LSTM input. Furthermore, a blind test was done which predicted most faults with reasonable accuracy. Finally, based on our experiments, LSTM shows a massive potential in fault extraction and possibly other geophysical applications due to the nature of seismic traces being sequential data.