Reviewerly/exHarmony huggingface dataset:
Hardware Efficient Direct Policy Imitation Learning for Robotic Navigation in Resource-Constrained Settings

Direct policy learning (DPL) is a widely used approach in imitation learning for time-efficient and effective convergence when training mobile robots. However, using DPL in real-world applications is not sufficiently explored due to the inherent challenges of mobilizing direct human expertise and the difficulty of measuring comparative performance. Furthermore, autonomous systems are often resource-constrained, thereby limiting the potential application and implementation of highly effective deep learning models. In this work, we present a lightweight DPL-based approach to train mobile robots in navigational tasks. We integrated a safety policy alongside the navigational policy to safeguard the robot and the environment. The approach was evaluated in simulations and real-world settings and compared with recent work in this space. The results of these experiments and the efficient transfer from simulations to real-world settings demonstrate that our approach has improved performance compared to its hardware-intensive counterparts. We show that using the proposed methodology, the training agent achieves closer performance to the expert within the first 15 training iterations in simulation and real-world settings.
Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naïve Bayes at Institut Teknologi Sumatera

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Emotion Classification of Indonesian Tweets using BERT Embedding

Twitter is one of the social media that has the largest users in the world. Indonesia is one of the countries that has the 5th largest number of Twitter users in the world which causes a high possibility of conflict between Indonesian Twitter users due to emotional tension in tweets. In this paper, we will compare the BERT embedding method with CNN and LSTM. The results of this experiment are BERT-CNN has the best performance results which has an accuracy of 61% compared to BERT-LSTM. In the experiment several stages of data preprocessing, data cleaning, data spiting and data training were carried out and the results were evaluated using confusion metrics.
Geohash-Based Maize Plant Monitoring System Ulitizing Drones

Corn is one of the important food crops in the world. To ensure optimal results, farmers usually monitor crop conditions manually. Unfortunately, manual monitoring can take time and effort due to the large area of maize fields (approx.: 1 ha). In addition, corn plants are also susceptible to diseases and pests which often result in corn farmers experiencing losses due to crop failure. This can be supported by several cases of corn crop failure in Lampung caused by pests and water shortages, such as in Bumidaya Village, South Lampung. Therefore, this research will develop a corn crop monitoring system using geohash and drones. The primary objective of this research is to develop a comprehensive design for a corn crop monitoring system, leveraging the capabilities of machine learning for corn plant recognition. The application of geohash is expected to assist farmers in handling and early detection of plants that experience a decrease in health quality before it spreads to all other maize crops. The results of the model training carried out with the R-CNN are that the detection model is able to detect with an accuracy of 88.9% with a low distance of the drone in taking pictures or close to plants.
Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.
Phase-aided Online Self-correction Method for High-accuracy Three-dimensional Measurement

The binocular structured light 3D measurement system is widely used in situ industrial inspection and shape measurement, where the system structure is generally unstable due to mechanical loosening or environmental disturbance. Timely corrections to the changing structural parameters thus is an essential task for online high-accuracy measurement, which is difficult for traditional unidirectional fringe projection methods to self-correct the structural change. To this end, we propose an online self-correction method based on the investigation that orthogonal fringe projection can intrinsically relax the constraint on the epipolar geometry relationship and provide bidirectional phases for accurate corresponding point searching. Since orthogonal fringe projection may sacrifice the measurement efficiency, we further design a searching strategy by locally unwrapping one directional phase to reduce the number of projection patterns. Experimental results demonstrate that the proposed method is effective for online self-correction of unstable system structure to achieve high-accuracy 3D measurement under complex measurement environments.
PHASL-NMF: Hierarchical ALS Based Power Non-Negative Matrix Factorization

The non-negative matrix factorization (NMF) has been found an effective clustering algorithm and it outperforms the classical k-means algorithm. Existing researches mainly focus on the problem of reducing the decomposition error between two decomposition matrices and the original matrix. In this paper, inspired by the power k-means algorithm and the hierarchical alternating least square NMF, we propose a novel NMF algorithm called power NMF (PHALS-NMF), which introduces the power mean to reduce decomposition error. Massive experiments on several datasets show the feasibility and effectiveness of PHALS-NMF.
THE INTENTIONS TO ADOPT E-LEARNING USING UTAUT-3 MODEL: A POST COVID-19 PERSPECTIVE

The COVID-19 pandemic has led to significant changes to e-learning systems, highlighting the importance of understanding the factors affecting their adoption. This study highlights the importance of e-learning by blending UTAUT (Unified Theory of Acceptance and Use of Technology) and UTAUT-3 theories to examine post-COVID adoption in e-learning programs. The purpose of this study is to investigate the influence of effort expectancy, performance expectancy, social influence, and facilitating conditions on the use of behavioral intentions in e-learning and the relationship between behavioral intentions and e-learning adoption behavior. The study sample consisted of 303 university students who continued to use e-learning systems after the COVID-19 pandemic, collected using a convenience sampling technique. Partial least squares structural equation modeling (PLS-SEM) through SmartPLS was used to analyze the collected data. The results revealed that effort expectancy, performance expectancy, and social influence significantly influence the behavioral intention to use e-learning. In contrast, the relationship between behavioral intention to use e-learning and acceptance of e-learning use is not significant. This study proved that significant changes in e-learning systems caused by the COVID-19 pandemic, and these changes are likely to continue after the pandemic. The study’s findings can help learners, learning providers, and policymakers plan and execute their online strategies.
Borrowable Fractional Ownership Types for Verification

Automated verification of functional correctness of imperative programs with references (a.k.a. pointers) is challenging because of reference aliasing. Ownership types have recently been applied to address this issue, but the existing approaches were limited in that they are effective only for a class of programs whose reference usage follows a certain style. To relax the limitation, we combine the approaches of ConSORT (based on fractional ownership) and RustHorn (based on borrowable ownership), two recent approaches to automated program verification based on ownership types, and propose the notion of borrowable fractional ownership types. We formalize a new type system based on the borrowable fractional ownership types and show how we can use it to automatically reduce the program verification problem for imperative programs with references to that for functional programs without references. We also show the soundness of our type system and the translation, and conduct experiments to confirm the effectiveness of our approach.
Vaste combinaties van werkwoorden met voorzetsels in het Nederlands en het Tsjechisch

This paper describes the results of a pilot contrastive study that focused on the differences and similarities between Dutch combinations of verbs with fixed prepositions and their Czech equivalents. Since the use of the correct prepositions in the combinations with verbs belongs to the typical learning problem of Czech students of Dutch as a foreign language, the results of the research can help clarify the causes of these difficulties. The pilot study showed that the majority of Dutch constructions have an equivalent construction with a fixed preposition in Czech. Only more than a tenth of the Czech equivalents do not contain a preposition. However, the bigger issue for students may be the fact that one Dutch preposition can correspond to several Czech prepositions and that some Dutch verbs are combined with various fixed prepositions.
A meta-analysis of the impact of AR and VR technologies on mathematics learning

Using Augment reality (AR) technologies and Virtual reality (VR) technologies in education is a new tendency. The aim of this study is to test the effect of AR and VR technologies on the development of students’ mathematics learning. Articles that met the inclusion criteria from a systematic database like Web of Science were included in the meta-analysis. A total of 18 research articles published from 2010 to 2023 were included in this study. The overall result showed that AR and VR technologies had a large positive impact (SMD=0.882, p&lt;0.001) on students’ mathematics learning. Sub-group results showed that the effect were significantly moderated by outcome, school level, content area, but were not significantly moderated by technology and experimental period. According to the results, some suggestions were given for the future studies.
Research on Refrigerator Design Based on Computer Vision: Designed for Visually Impaired People

With the continuous progress of technology, artificial intelligence (AI) technology, especially computer vision, has provided new possibilities for obstacle free design. This article focuses on how to use computer vision technology to create more user-friendly refrigerator designs for visually impaired individuals. Firstly, we introduced the basic concepts of computer vision and its applications in various fields. Through user research on visually impaired individuals, we understand their needs and challenges when using refrigerators. On this basis, we propose a series of design strategies for obstacle free refrigerators based on computer vision and propose some possible design solutions. However, there are also some challenges when implementing these design solutions, such as cost, device robustness, and privacy issues. Overall, this study provides a new perspective on how to use computer vision to improve the quality of life for visually impaired individuals.
Video Super-Resolution Reconstruction Based on Group Spatio-Temporal Projection Network

With the booming development of short video era, super-resolution reconstruction has gradually developed into a pivotal research direction in computer vision. Although existing video super-resolution has achieved good results, many methods do not consider the correlation of spatio-temporal information. To address this problems, we constructs a one-stage spatio-temporal video super-resolution reconstruction model, GSPN. The model completes the extraction of inter-frame information through feature extraction and feature interpolation, the feature grouping module realizes the mutual complementation of different groups of information, the intra-group alignment module better handles the blurring and artifacts generated when the video motion is large, the intra-group mapping as well as the fusion module realizes the fusion of spatio-temporal information, which in turn improves the quality of the video.
Improving the Students’ Ability in Writing Text by Using Realia at Madrasah Aliyah DDI

The research endeavors to ascertain the advancement of students' compositional prowess in the domain of procedural text subsequent to pedagogical intervention employing realia at Madrasah Aliyah DDI. Employing a pre-experimental design, the study was conducted within the premises of Madrasah Aliyah DDI. The target population encompassed students enrolled in the eleventh grade during the academic term of 2022/2023. Employing purposive sampling, a sample size of 30 students from the aforementioned grade was selected. The research instrument utilized was a procedure text writing assessment. The findings of the data analysis distinctly underscored a significant disparity in the students' capacity to compose procedure texts, as evidenced by the variance between pre-test and post-test results. Specifically, the mean score for the pre-test stood at 58.73, contrasting with the post-test mean score of 72.58, reflecting a notable enhancement of 13.77 points. This discernible progress in writing proficiency indicates the salutary impact of incorporating realia. This observation was further corroborated by the outcomes of the t-test, which notably exceeded the critical t-table value (11.53 &gt; 2.045), affirming the efficacy of the intervention.
Towards Privacy-Preserving and Practical Vaccine Supply Chain based on Blockchain

Vaccines play a vital role in safeguarding people’s health and improving their well-being. With the increase in the variety and number of infectious diseases, the importance of vaccination cannot be underestimated. Despite being responsible for overseeing the entire lifecycle of vaccines, the current vaccine supply chain is encountering certain issues. There is no effective mechanism for sharing vaccine data, while the privacy of recipient data cannot be guaranteed. Also, different entities involved in the supply chain manage their data separately, leading to data barriers that hinder the effective supervision and tracking of vaccine data. This paper designs a vaccine whole process management system that employs a consortium blockchain to facilitate trustworthy collaboration among various entities involved in the vaccine supply chain. The system leverages data encryption to preserve data privacy and promote seamless data sharing, thereby overcoming data silos among participants. Additionally, smart contracts are utilized to establish uniform data operation protocols, while a regulator-friendly vaccine management mechanism is proposed to enable comprehensive supervision of the entire vaccine supply process. Experiments demonstrate the effectiveness of the proposed system.
Transformation of Conventional Archives into Digital at the Medical Faculty of the University X

Archiving is one of the office jobs that is experiencing digital transformation. Digital archives make it easy for employees to complete their work however, it does not rule out that digital archives have weaknesses that can reduce employee performance. The purpose of this study was to analyze the digital filing system used by the X University Faculty of Medicine, find out whether the system used was effective or not, and find out the weaknesses of the digital archiving system. The research method used in this research is descriptive qualitative with a case study approach. The results of this study indicate that digital archives help employees complete work more quickly and make it easier for employees to carry out administrative activities in agencies. However, behind that it is necessary to repair, improve and upgrade the system so that problems do not occur and can be used more optimally.
Development of semi-automatic image annotation using object recognition

The modern field of machine learning has made remarkable progress, and the demand for training data required by machine learning systems is constantly increasing. However, the annotation work required to create this training data requires a great deal of effort. Therefore, we considered semi-automating the labeling process by taking advantage of false positives that occur when object detection is performed by YOLOv5, thereby eliminating the need for region selection and reducing the cost of annotation work. In a comparison experiment with labelImg, a common annotation tool, we found that semi-automating the annotation process took less time than manually annotating the objects.
Rotational weight update in full-connection layers exceeds dropout in image recognition tasks

Various methods have been proposed to improve image recognition performance with deep learning. In this study, we propose a method named “Rotational-update” that updates the weights of neurons in rotation. The method divides the neurons into $\sqrt{N}$ groups of equal size, where N is the number of neurons in a full-connection layer. The method updates the neuron weights of only one group in turn for each mini-batch. One important feature is that this method can be combined with other methods such as bach-normalization and dropout.We verified the effectiveness of the proposed method through the image recognition task of a dataset CIFARIO with a neural network model VGG-16. The results showed that combining the proposed method and bach-normalization was the most accurate and better than the combination of dropout and bach-normalization. We conclude that using the proposed method instead of dropout improves the performance of image recognition tasks.The proposed method is provided as a library on pip (pip install rotational-update).
Enhanced Multi-Object Detection via the Integration of PSO, Kalman Filtering, and CNN Compressive Sensing

Many inventive techniques have been created in the field of machine vision to solve the challenging challenge of detecting and tracking one or more objects in the face of challenging conditions, such as obstacles, object motion, changes in light, shaking, and rotations. This research article provides a novel method that combines Convolutional Neural Networks (CNNs), Compressive Sensing, Kalman Filtering, and Particle Swarm Optimization (PSO) to address the challenges of multi-object tracking under dynamic conditions. Initially, a CNN-based object classification and identification system is demonstrated, which efficiently locates objects in video frames. Subsequently, in order to produce precise representations of object appearances, utilizing compressive sensing techniques. The Kalman Filter ensures adaptability to irregular observations, eliminates erroneous data, and reduces uncertainty. PSO enhances tracking efficiency by optimizing forecast precision. When combined, these techniques provide robust tracking even in the presence of complex movement patterns, occlusions, and visual disparities. The efficiency of this strategy is demonstrated by an empirical investigation that produces a remarkable tracking accuracy of 98%, which is 3.15% greater than other methods across a range of challenging settings. This technique has been compared to various existing approaches, including the Clustering Method, YOLOV4 DNN Model, and YOLOV3 Model, and its deployment is made easier with Python software. This hybrid technique, which addresses the limitations of separate approaches and offers a holistic approach to multi-object monitoring, has potential applications in surveillance, robotics, and autonomous systems.
Information Security Management in Small Public Sector Organizations: Requirements and Design of a Procedural Approach

The increasing digitalization of enterprises and public authorities has resulted in the growing importance of information technology in everyday operations. In this context, an information security management system (ISMS) has become an essential aspect for most organizations. The dependency on technology for almost every single process in an organization has put ISMS at the top of the corporate agenda of public sector organizations. For public organizations in particular, the NIS 2 Directive describes abstract requirements for the development of an ISMS. On the other hand, only a few public administrations operate an ISMS. In this context, this article analyses the requirements of the NIS-2 Directive and complements them with the obstacles and reasons for success in the introduction of ISMS in small public sector organizations (SPSO). At the same time, minimum requirements should be defined that help municipal administration set up an ISMS quickly and easily. This article summarizes the different requirements and generates a foundation for a rough procedural model, for implementing the upcoming requirements of the NIS 2 Directive in local governments. The article also presents the conceptual design of the procedural model.