Reviewerly/exHarmony huggingface dataset:
Building Educational/Academic Resilience through Digital Literacy

The ongoing digitization of the education systems has introduced a new pedagogical approach to teaching and learning – Education 4.0 that aligns the fourth industrial revolution. Education 4.0 has improved the effectiveness and efficiency of learning globally. However, the issues and challenges associated with Education 4.0 or technology –enabled technology teaching and learning emerged when the global education system was mandated to technology-enabled learning during the pandemic. Both the educators and the learners were found to lack relevant digital skills to successfully facilitate the learning process. This study introduces the concept of digital literacy, a digital literacy framework and a digital literacy tool that can be used to effectively champion digital literacy skills to both the educators and the learners. The digital literacy tool has been successfully used in the South Pacific to evaluate digital literacy competencies and further improve the digital literacy competencies of the selected sample. As such, the digital literacy tool can be used to improve digital literacy of individuals in the developed and developing economies hence promoting educational resilience for all in the technology-enabled environment.
ICT-Based Conceptual Model for Local Culture Communities using Soft System Methodology

The problems that exist in the field of art and culture preservation experienced by the arts and culture community side are the limitations on physical facilities for disseminating works, exchanging information between arts and culture communities, as well as the need to increase professionalism so that they can be independent in order to help improve the economy, where the role of regulation in this regard also affects this condition. Based on the facts above, this community requires an interaction model that is integrated with the use of information technology. This study uses a Soft System Methodology approach to create a model that is suitable for the arts and cultural community. The result is an ICI-based conceptual model for a local culture community. (Abstract)
Topological Texture-Fractal Processing of Signals and Fields in Radio Physics, Radio Engineering and Radiolocation: Developed Methods and Technologies (1979 – 2022) – Fractal Engineering

The report discusses the main directions of the introduction of textures, fractals, fractional operators, dynamic chaos and methods of nonlinear dynamics to create new information (breakthrough) technologies. The research is carried out in the fundamental scientific direction "Fractal radio physics and fractal radio electronics: design of fractal radio systems", initiated and developed by the author in V. A. Kotel’nikov IREE RAS from 1979 to the present. The relevance of these studies is related to the need for a more accurate description of all real processes occurring in radio physical and radio engineering systems: taking into account the hereditarity (memory), nonGaussianity and scaling of physical signals and fields. The use of fractal systems, sensors and nodes is a fundamentally new solution that significantly changes the principles of building intelligent radio engineering systems and devices. The performed studies are priority ones in the world and serve as a basis for further development and justification of the practical application of fractal-scaling and texture methods in the synthesis of fundamentally new topological texture-fractal methods for detecting signals in the space-time channel of waves propagation with scattering (a new type of radar). The concepts of fractal engineering are introduced for the first time.
IDENTIFICATION OF FACTORS AFFECTING THE LEARNERS' MOTIVATION IN AN E-LEARNING SCENARIO FOR INDUSTRIAL DESIGN EDUCATION

Industrial design has certain "niche" aspects illustrating precision and level of detail which are introduced to the students to fulfill real-world design expectations.As reported in literature, encouraging students to undergo an online industrial design course while simultaneously maintaining their motivation is a difficult task.Today, technological advancements in e-learning are being employed to make industrial design education and learning more engaging for students.The learners are rapidly changing their e-learning practices and motivations in response to new innovations.Research suggests that the technological advancements in e-learning are appealing primarily because they are novel and different.Later, when learners get used to them, they may lose their interest leading to loss of motivation.Motivational factors of an e-learning platform can be an aid to understand learners' motivation and knowledge gaining methods.However, there has been a dearth of literature featuring methods for assessing motivation of an online industrial design learning.Furthermore, complexity of learners' motivation, drives and variety of motivational theories has made the assessment more complicated particularly in the context of online industrial design education.Therefore, this study tries to investigate the relation between effectiveness of an online industrial design learning platform and learners' motivation.The study followed a mixed-method approach and used survey questionnaires and semi-structured interviews with 200 participants who underwent an online industrial design education.The study identifies various facets of industrial design learning and its relation with students' attitudes and motivations, which in turn may improve their performance in industrial design applications.
A Multisite Survey of E-learning Readiness and Academic Performance among Nursing Students in Saudi Arabia

Introduction: Determining students’ readiness for e-learning is critical to implementing more effective education and performance. Nursing education in Saudi Arabia continues to struggle with a shortage of technical tools to enable teaching and acquiring critical nursing skills. Aim: To assess the level of e-learning readiness and academic performance and their association in nursing students and to determine the factors associated with e-learning readiness and academic performance. Materials and Methods: A cross-sectional, multisite study was conducted on a convenience sample of nursing students (N = 139) attending nursing programs from multiple public and private universities in Saudi Arabia. A structured questionnaire was used for data collection from May 2020 to August 2020. Multivariate analyses were run using Statistical Package for Social Sciences (SPSS). Results: The mean age of the study participants was 27.3±6.34 years. The total average of e-learning readiness was 4.16 (SD=0.54). Bivariate analyses showed that age was associated with both e-learning readiness (r=0.175, p=0.03) and academic performance (r=0.341, p&lt;0.001). Employment status, level of education, and previous experience with e-learning displayed significant association with e-learning readiness (p&lt;0.05). In multivariate analyses, only previous experience with e-learning had positively influence on students e-learning readiness (b=0.188, p&lt;0.05). Technology access subscale (b=0.206, p&lt;0.05) and level of education (b=0.323, p&lt;0.05) had both positive impacts on students’ academic performance. Conclusion: The e-learning readiness score was high among the nursing students, especially if they were employed or had previous experience with it. Access to technology improved students’ academic performance. The current e-learning mechanism may need some developments in order to meet students’ different needs.
Examining Distance in UAV Gesture Perception

Unmanned aerial vehicles (UAVs) are becoming more common, presenting the need for effective human-robot communication strategies that address the unique nature of unmanned aerial flight. Visual communication via drone flight paths, also called gestures, may prove to be an ideal method. However, the effectiveness of visual communication techniques is dependent on several factors including an observer's position relative to a UAV. Previous work has studied the maximum line-of-sight at which observers can identify a small UAV [1]. However, this work did not consider how changes in distance may affect an observer's ability to perceive the shape of a UAV's motion. In this study, we conduct a series of online surveys to evaluate how changes in line-of-sight distance and gesture size affect observers' ability to identify and distinguish between UAV gestures. We first examine observers' ability to accurately identify gestures when adjusting a gesture's size relative to the size of a UAV. We then measure how observers' ability to identify gestures changes with respect to varying line-of-sight distances. Lastly, we consider how altering the size of a UAV gesture may improve an observer's ability to identify drone gestures from varying distances. Our results show that increasing the gesture size across varying UAV to gesture ratios did not have a significant effect on participant response accuracy. We found that between 17 m and 75 m from the observer, their ability to accurately identify a drone gesture was inversely proportional to the distance between the observer and the drone. Finally, we found that maintaining a gesture's apparent size improves participant response accuracy over changing line-of-sight distances.
Stress Prediction in Working Employees using Artificial Intelligence of Things

Stress issues are a common issue among today's working IT professionals.As people's lifestyles and workplace cultures change, employees are more prone to encounter stress.In this project, we will use IoT and machine learning approach like supervised learning to examine stress in working employees.After proper data cleaning and preprocessing, we used a variety of Machine Learning approaches like KNN, Decision Tree and Naïve Bayes algorithm to train our model.The accuracy of the above-mentioned models was determined and compared.Among the models used, KNN Algorithm had the best accuracy.Significant factors that affect stress were found using KNN, Decision Tree, and Naive Bayes algorithms.With these findings, organizations can set their sights on reducing stress and providing a much more comfortable working environment for their employees.
Research on the model of network public opinion pre-warning system for emergencies under the background of big data

At present, emergencies occur frequently. How to better adapt to the changes brought by big data in the analysis of network public opinion for emergencies is an important issue of emergency management in the era of big data. In this paper, combining the actual needs and the shortcomings of the existing public opinion pre-warning system, data mining technology, emotion computing technology and artificial intelligence methods are introduced into network public opinion pre-warning system, build a network public opinion pre-warning system model based on Hadoop big data architecture, which includes four layers: public opinion data collection layer, data preprocessing layer, public opinion analysis layer, public opinion pre-warning and decision support layer. The model integrates the important functions of the whole process of network public opinion analysis and pre-warning, in order to provide accurate pre-warning information and scientific auxiliary decision-making scheme for emergency management.
Crime Prediction and Analysis

The systematic technique used to detect and analyze crime patterns and trends is called crime analysis and prevention.The proposed model will be able to predict regions that have a high chance of crime occurrence.Crime data specialists can assist police officers in stopping crime more quickly.Our goal is to develop methods that can forecast homicide activities based on demographic and economic data from a specific area.In this project, we use a classification and clustering algorithm to build an effective crime prediction model.Support vector networks, multivariate time-series data, and artificial neural networks are some of the prediction approaches that can be used to test the efficiency of prediction models.We use multiple algorithms for classification and clustering and then we compare with accuracy.To measure the out-oftraining effectiveness of classifiers, we use a ten-fold cross-validation approach.
An Effective Approach for Object Detection Using Deep Convolutional Networks

Computer vision and artificial intelligence has replaced many of the manual inspection systems in today’s world. In recent times, artificial intelligence is applied in developing of smart city services like object detection and tracking in surveillance system. However, these systems are impacted and yield poor performance due to two major reasons. The first reason is lower performance due to illumination changes or during poor illumination and the second problem is slow speed of operation. In this paper, the second problem of speed segmentation is addressed and the work focuses on developing a customized deep network that accelerate the process of object detection and evaluate the performance of the network with multiple video sequences. The approach is tested on standard datasets and compared against the state of art methods and observed to be more accurate and precise
An Intelligent Caregiver Elderly Robot Based on Raspberry Pi

In order to cope with the deepening trend of population aging, this paper proposes a multi-functional smart elderly robot based on Raspberry Pi, which combines fall detection, voice interaction, face recognition, simultaneous localization and mapping (SLAM) indoor positioning, Android applet and other technologies to achieve full range fall detection, environmental monitoring and movable intelligent living companionship. It not only combines the physiological and psychological characteristics of the elderly to optimize the elderly care model, but also reduces the burden of social elderly care through the precise matching of elderly care needs and services.
An algorithm for matching original experimental records based on improved CDC

Aiming at the problems such as long time and occasional errors in the generation process of the current laboratory test report, we present an automatic capture technology of general original experimental records based on fence factor. First, the read files of the day are accurately filtered by calculating the overall Hash value of file. Then, we use the improved content-defined chunking (CDC) algorithm for chunking. The improvement of CDC algorithm includes setting the unit of the sliding window as the spacing of between two lines and setting the range of the byte size in the sliding window. When the text block is completed, a string matching algorithm based on pattern string is used to complete the matching process. The string matching algorithm constructs the mapping relationship between the pattern string and data block in data block index table, and then quickly matches the pattern string <italic>P<sub>n</sub></italic> to corresponding data block through the data block index table. The original experimental record files of customs laboratory are used for testing. The algorithm occupies the least memory and has the largest chunking throughput.
Constraints and Evaluations on Signature Transmission Interval for Aggregate Signatures with Interactive Tracing Functionality

Fault-tolerant aggregate signature (FT-AS) is a special type of aggregate signature that is equipped with the functionality for tracing signers who generated invalid signatures in the case an aggregate signature is detected as invalid. In existing FT-AS schemes (whose tracing functionality requires multi-rounds), a verifier needs to send a feedback to an aggregator for efficiently tracing the invalid signer(s). However, in practice, if this feedback is not responded to the aggregator in a sufficiently fast and timely manner, the tracing process will fail. Therefore, it is important to estimate whether this feedback can be responded and received in time on a real system. In this work, we measure the total processing time required for the feedback by implementing an existing FT-AS scheme, and evaluate whether the scheme works without problems in real systems. Our experimental results show that the time required for the feedback is 605.3 ms for a typical parameter setting, which indicates that if the acceptable feedback time is significantly larger than a few hundred ms, the existing FT-AS scheme would effectively work in such systems. However, there are situations where such feedback time is not acceptable, in which case the existing FT-AS scheme cannot be used. Therefore, we further propose a novel FT-AS scheme that does not require any feedback. We also implement our new scheme and show that a feedback in this scheme is completely eliminated but the size of its aggregate signature (affecting the communication cost from the aggregator to the verifier) is 144.9 times larger than that of the existing FT-AS scheme (with feedbacks) for a typical parameter setting, and thus has a trade-off between the feedback waiting time and the communication cost from the verifier to the aggregator with the existing FT-AS scheme.
TranSlider: User-Controlled Augmented Content Presence Visualization in AR Collaboration

We present a user interface method called TranSlider, designed for an AR collaboration with the concept of customized visualization of the user-selected augmented content. In AR, virtual content is augmented on top of the real world, so collaborating users are inevitably exposed to the diverse-overlapped information layers. Because the human recognition system has limited capacity, this overloaded information may burden the user to understand fully, and it is also challenging to manipulate among occluded objects. To solve this issue, we designed the TranSlider that allows users to control the presence of augmented content being transparent or highlighted based on the user interaction. The implemented prototype includes UI components, a manipulation function, and a network-connected collaboration module, and its applicability was verified through a preliminary interview. We expect that it would effectively support general AR collaborations and those with specific purposes, such as designing complex, hierarchical, or made up of many partial assets.
Penerapan Metode Waterfall Pada Aplikasi Tracer Study Berbasis Website

Tracer study is a method used by several Vocational High Schools (SMK) to collect alumni data and monitor alumni progress. The problem is in monitoring the alumni who do not only stay in Cilacap but outside of the city as well. Moreover, the data collection method is still simple by only using a questionnaire form filled out when they take their diplomas. This condition is less effective because many alumni are still looking for a job, therefore it is difficult to check up on them all the time. This study aims to create a tracer study application using the waterfall method. The testing phase uses the Blackbox testing method. Based on the test results, the application can make it easier for BKK administrators to collect alumni data and help monitor the alumni progress by checking the percentage of alumni status and making it easier for alumni to fill out questionnaires. The results of the overall usability test conducted on 30 respondents resulted in the predicate "Very Good" with an average of 85.51%.
E-function for Fuzzy Clustering in Complex Networks

In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to evaluate such clustering. The function that assesses the quality of a performed clustering is called the cluster quality function. One of the latest proposed quality functions is the E-function. The E-function is based on a comparison of the internal structure of a cluster, i.e., the connection between nodes within a cluster and the connection of its nodes with the nodes of other clusters. Due to its exponential nature, the E-function is sensitive to small changes in the membership degrees to which the nodes belong to clusters. As such, it has shown good results in evaluating clustering on known data sets. In this paper, the experimental results that the modified E-function achieves in the case of overlapping clusters are presented. Also, some possibilities for fuzzy clustering by optimizing the E-function are displayed.
A Survey of Scalable Reinforcement Learning

Reinforcement Learning (RL) has been around for some time now, and various issues have been associated with the provision.In the last two years, RL has realized a boost in popularity through deep learning.For instance, RL played a critical role in the DeepMind AlphaGo program that was crucial to beating a top-level Go player in 2016 [1].However, there is still work to be done despite RL's advancements before the DeepMind AlphaGo program becomes mainstream.For instance, one of the challenges of the program is its ability to multitask.[1] indicate that agents should perform various functions to achieve general AI.However, in the contemporary understanding, multitasking is one of the challenges of AI and RL scalability; for instance, it should not take over 1000 hrs. of different tasks to learn 1000 various tasks.Instead, AI agents must build up a library of general knowledge and learn general skills that are common and applicable across a variety of tasks.However, this ability is currently non-existent in such programs as the Deep Q-Network (DQN).While the DQN has previously been shown to have the ability to play various games, including Atari games, there is often no learning across the tasks.Each of the games is learned from scratch, which is often not scalable.
An Intelligent Robot Motion Planning Method and Application via LPPO in Unknown Environment

Aiming at the problem of motion planning in complex dynamic environment, traditional algorithm which relies on prior map information is unavailable due to the change of environment and suffer from high computational complexity. Proximal policy optimization algorithm suffers from the problem of training failure due to a bad strategy learned in a certain round. In this paper, an improved end-to-end motion planning algorithm with proximal policy optimization is proposed to address this problem by using logarithmic decay strategy dynamically selecting the clipping range and applying the algorithm to robot motion planning. It ensures a larger exploration space for the agent at the beginning of training, and limits the sudden change of the agent's strategy by attenuating the clipping range at the later stage, which improves the stability of the algorithm. We compare our proposed algorithm with several other improved algorithms in different Mujoco environments and our built environment. The results show that our proposed improved algorithm obtains higher reward values and the robot has smoother motion control while navigating obstacle avoidance.
A Study of "Poetic" in Robert Schumann’s Frauenliebe und Leben, Op. 42

und Leben, Op. 42 is a representative art song work of early Romanticism.The "poetic" expression of this work is the result of the interaction between German early Romanticism poetry and music.Therefore, by exploring the logical significance of the two aspects of unity of opposites of poetry and music in this work, the key to the fusion of acoustic material and poetry, the "meaningful formal content" of the work is understood and used as a perceptual guide for the practice of singing in order to externalize the 'poetic' of the work.