Reviewerly/exHarmony huggingface dataset:
Inducers of motion sickness in vehicles: A systematic review of experimental evidence and meta-analysis

With the development of intelligent cabins and the popularity of smart devices, motion sickness has become an increasing concern. A number of theories have been proposed to explain the cause of motion sickness in vehicles. However, because of the diversity of the dynamical characteristics, road conditions, and in-vehicle human–machine interaction designs, the influence of the inducers on motion sickness in vehicles has not yet been fully quantified. Thus, in this paper, we aim to review and summarize the influential factors of motion sickness in road vehicles through a systematic review. In total, we identified 57 studies related to influential factors of motion sickness in vehicles, of which, 27 were further included for meta-analysis. In total, we identified eight categories of motion sickness inducers in vehicles, including the type of eye view, non-driving-related task availability, existence of artificial motion cues, head dynamic movement, vehicle dynamics, internal layout of vehicle, individual differences, and others. Most inducers had consistent effects on inducing motion sickness; however, inconsistent results have also been observed in vehicle dynamics, head dynamic movement, eye view, individual difference, and artificial motion cues (i.e., cues regarding the motion states of the ego-vehicle provided through in-vehicle auditory, tactile, or visual interfaces). Additional meta-analysis was conducted for motion cues. It was found that only natural present motion cues (i.e., cues regarding relative movement between the ego-vehicle and the environment through the windshield or side windows) and non-visual artificial anticipatory motion cues (i.e., auditory or tactile artificial motion cues regarding the future motion of ego-vehicle) were effective in alleviating motion sickness. Future research directions have been pointed out. Our study can provide insights into the optimization of vehicle design to mitigate motion sickness among occupants.
Automated extraction of domain knowledge in the dairy industry

Three weeks prior to calving to three weeks after calving, the transition period poses challenges for dairy cattle and farmers. Vast changes in housing, feeding, and reproduction might result in milk drop, metabolic and reproductive diseases. Moreover, most of the metabolic processes are intricately linked as many conditions can coexist. This challenge means that dairy producers and their advisors have difficulty drawing concise conclusions because of all aspects and relationships in transition cow management. Herein, machine-learning techniques and knowledge-graph theory were explored with a view to creating a decision-support system that could provide producers and their advisors with knowledge from domain literature. Specifically, knowledge is modelled as entities and relationships in knowledge graph theory, and natural language models were developed to extract information as knowledge graphs. A dataset comprising 1152 sentences from 20 papers was created and split into 922 sentences for training and 230 sentences for testing. Sequentially, two deep learning models were trained to extract entities and relationships respectively. For training results, a Bi-directional Long-Short-Term Memory model was applied for the entity extraction task and obtained an F1 score of 80 %. As for relationship extraction, a Transformer-based model was deployed but yielded a low F1 of 23 %, thus another pre-trained Transformer model with 89 % accuracy was deployed into the system. After feeding the domain literature into the deep-learning models, a knowledge graph of 1,576 nodes and 3,456 edges was constructed and stored in the graph database Neo4j. Afterward, a semantic parsing method was used to allow users to conduct question answering through the knowledge graph in natural language. In addition, to determine the quality of answers that the knowledge built from the papers, answers were sampled and evaluated based on human judgment. On average, answers scored 7.5 out of 10 and proved informative with respect to the original literature. Although the final interactive results demonstrated a high degree of visualization and scalability, this study primarily sought to demonstrate its feasibility. For tailored commercial applications, further improvements could be implemented in knowledge graph expansion and reasoning.
Integration of Robot Arm Control System in Brachytherapy Microcapsules Preparation

HDR brachytherapy uses Ir-192 wrapped in microcapsules (seeds). Ir-192 requires high precision during the preparation process to produce good-quality seeds. The technological innovation of four DoF robot arms using LabVIEW and Arduino MEGA is equipped with an endoscope camera monitoring system to maximize preparation and minimize radiation exposure. The endoscope camera helps identify and observe seeds picked up by the vacuum gripper. The system starts by taking the seeds on the conveyor and placing them at the chuck. The robotic arm's test for accuracy and precision employs quantitative analysis. The linear motion angle test results in precision of 99.3% and accuracy of 100%; the base had 98.4% precision and 98.4% accuracy; Link 1 had 96.2% precision and 97.2% accuracy; and Link 2 had 72.4% precision and 71.5% accuracy. The average error of the inverse kinematics test was obtained: linear motion 0%, base 8.1%, Link 1 8.2%, and Link 2 9.8%. The average error of forward kinematics testing for positions a and z 0%, position x 25.1%, and position y 18.9%. The average success result for the seed collection test was 87%, the monitoring test result was 80%, and the seed placement test was 86%. The overall system test results yielded 80% for monitoring, picking, and placing seeds, with an average system accumulation time of 83.6 seconds.
The Design of Simulation Interface G2 COMBAT Pistol Cal. 9 mm with Pimp My Gun Simulator

The development of firearms has been included as an introduction and use at the University of Defense. However, using firearms that are not used freely is a barrier for students to understand the ins and outs of firearms better. Therefore, in this research, firearms are replaced with interface simulation design. One firearm that needs to be designed for interface simulation is the G2 Combat pistol. The reason for using the G2 Combat pistol in making an interface simulation design is because the Indonesian armed forces have widely used it. The interface simulation design uses Pimp My Gun software. The method of designing the G2 Combat pistol interface simulation uses the waterfall method. The waterfall method is considered easy and systematic. After the interface simulation design is carried out, the results are compared with the original item or image, photo, and others. Assessment of the G2 Combat pistol interface simulation design with the original pistol by comparing and finding the level of similarity. The assessment results are 87.61% at imgonline.com and 86.14% at blue2digital.com. With two assessment results close to 100%, the G2 Combat pistol interface simulation design is successful. However, there still needs to be improvements so that the interface simulation design is even better in the future.
DStore: Blockchain-Powered Decentralized Cloud Mesh

Data is a critical asset for any company, as well as for any individual as well, but it is also vulnerable to attack. In the last few years, we have seen an alarming increase in data breaches that have compromised millions of accounts and resulted in billions of dollars lost. But how do you protect something so sensitive? In response to this, we propose our Project. This project focuses on developing a Decentralized Cloud Storage to store and secure data. You don't access data simply specifying 'where it is' in Decentralised Cloud Storage. Instead, you define 'what it is'. Because data is distributed throughout a global network rather than being stored in a specific location, the concept of location is rendered obsolete in decentralised cloud storage.
Intelligent Destination Recommender and Community Builder

Recommendation engines make use of machine learning techniques and generally deal with ranking and rating of products/users. With the help of this system we aim to suggest different destinations to users based on their interest and previous visits. Along with recommendations we also aspire to enable users to build travel communities for people sharing similar interests .This shall help travelers with planning ,meeting like-minded people,safety and enthralling experience.&#x0D; As per the analysis done on pre-existing systems we discerned that enabling users to build a community of travelers visiting the same destination is an eccentric attribute proposed.&#x0D; This distinctive attribute of building communities shall be implemented using the basics of clustering algorithms in Machine learning.
Multi-task learning for X-vector based speaker recognition

Abstract In this paper, we propose a speaker recognition system that leverages multi-task learning and features integration (MTFI), to improve the performance of x-vector based speaker recognition models. It is important to integrate complementary information from different features such as MFCC, Fbank, spectrogram and LPCC, as often a single feature usually cannot cover all information about a speaker and generalization is insufficient. Since the x-vector model outputs affine transformation values with the penultimate hidden layer in the trained model, the parameter distribution of this layer should be stable and should not be affected by tasks that are not current branches when switching tasks. Therefore, we propose a shared unit (SU) in multi-task learning, which is useful for sharing common representations and other auxiliary tasks. Then, an attention mechanism is designed to calculate the frame weight in the statistical pooling layer, so as to enhance the key frame information. The proposed system had an EER of 0.98% in voxceleb1 and the average score fusion obtained the EER of 0.65%.
Deep and adaptive feature extraction attention network for single image super‐resolution

Abstract Single image super‐resolution (SISR) has been revolutionized by convolutional neural networks (CNN). However, existing SISR algorithms have feature extraction and adaptive adjustment limitations, leading to information duplication and unsatisfactory image reconstruction. In this paper, we propose a deep and adaptive feature extraction attention network (DAAN), which first fully extracts shallow features and then adaptively captures precise and fine‐scale features by a deep feature extraction block (DFEB). It includes multi‐dimensional feature extraction blocks (MFEBs) that combine large kernel and dynamic convolution layers to improve large‐scale information utilization effectively. Finally, an enhanced spatial attention block (ESAB) to further selectively reinforce the transmission of details. A large number of experimental results show that our proposed model reconstruction performance is superior to existing classical methods.
A robot path planning method using improved Harris Hawks optimization algorithm

The traditional Harris Hawks optimization algorithm is prone to the local shortest path, slow search speed and poor path accuracy in indoor mobile robot path planning. For the above problems, a multi-strategy improvement of the Harris Hawks optimization algorithm (MIHHO) is proposed. In this study, a Chebyshev chaotic mapping strategy is used to increase the diversity of the Harris Hawk population, improve the global search performance of the Harris Hawk algorithm, and prevent being trapped in the locally optimal path. A fusion exploration mechanism is proposed to fuse the discovery mechanism of the sparrow algorithm with the exploration mechanism of the HHO. Then the influence factor E is improved to improve the algorithm’s search accuracy and search efficiency, and finally, in the design of a dynamic Lévy flight strategy, which accelerates the convergence speed of the algorithm and improves the robot planning speed. Simulation results show that the proposed MIHHO method exhibits better search performance in path planning, improved planning efficiency, and superior quality of planned paths.
Digital competence of employees: modern realities and the requests of employers

The article is devoted to the analysis of the digital transformation of the labor market through the prism of digital competencies. The first and second sections provide an overview of digital competencies in the modern world. In the conditions of the digital economy, new professions are emerging, the education system is being revised with an emphasis on the continuity of the educational process and learning throughout life. This is especially relevant in the context of involving able-bodied older people in the digital environment. High-speed digitalization processes are becoming a common challenge for the education system and the state, business and employees themselves. At a certain stage, the question arises about how employers perceive the changes taking place and what are the requirements of modern Russian employers for digital competencies of employees. Based on the mentioned theoretical ideas, the authors conducted a sociological study of the attitude of employers to digital competencies of employees, the results of which are presented in the second section. The authors came to the conclusion that the importance of digital capital of employees is increasing, but the "digital capital" itself is undergoing constant changes. This is expressed in an ever-expanding list of "basic" digital skills.
Lyric poetry in the information society

Poetry seems to have become a kind of a foreign body in the modern society because it does not contain what we call information. But while information conveys actual truth, poetry can articulate the other one: while the permanent flood of information puts people into constant tension and emotional unrest, poetry can provide counterbalance. With the information society spreading worldwide, new forms of culture have emerged alongside with new forms of work, and rap is one of them. Just as one recognizes both opportunities and dangers in the information society, this new form of poetry is also judged aesthetically, ethically and sociologically in very different ways. This is not surprising, since there are very ordinary as well as artistic forms of rap.
A Study on the Application of Machine Learning for Predicting Concrete Compressive Strength

The compressive strength of concrete is one of the main factors affecting the quality of construction, and it is also a key parameter for concrete design, so it is necessary to predict it. However, most of the early empirical formulas for predicting the compressive strength of concrete are for specific concretes, which can not be universally promoted, while other prediction methods such as physical testing method, finite element analysis method, etc. are cumbersome in terms of steps, and can not be formed into a generalized prediction model under the harsh conditions of applicability. In recent years, the concrete compressive strength prediction model based on machine learning has become a hot research topic at home and abroad. In this paper, firstly, by comparing the application of various machine learning methods, including regression model, support vector regression machine model, tree model, neural network and integrated learning in concrete compressive strength, it is concluded that the support vector regression machine model and integrated learning have higher effectiveness in the prediction of compressive strength. Finally, the article discusses the shortcomings of the widely used concrete compressive strength prediction methods and gives the future research direction of machine learning based concrete compressive strength prediction model.
Joint frame rate adaptation and object recognition model selection for stabilized unmanned aerial vehicle surveillance

Abstract We propose an adaptive unmanned aerial vehicle (UAV)‐assisted object recognition algorithm for urban surveillance scenarios. For UAV‐assisted surveillance, UAVs are equipped with learning‐based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self‐adaptive control strategy to maximize the time‐averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real‐world data demonstrate that the proposed algorithm achieves the desired performance improvements.
Return to Face-to-Face Classrooms in Higher Education: Students Experiences in Chile, Venezuela, and Ecuador

Emergency remote teaching (ERE) triggered by the COVID-19 pandemic posed significant challenges to higher education institutions worldwide. Although it had several negative consequences, it also enabled advances in the integration of technology into the teaching and learning process. The purpose of this study was to examine and characterize the key elements of the process of returning to face-to-face courses. The study was based on a mixed quantitative and qualitative design. 631 students from Chile, Venezuela, and Colombia were surveyed about their experiences returning to face-to-face classes (in terms of virtual tools, their feelings, and their expectations for academic success). The survey also included questions about the lockdown caused by the pandemic, specifically regarding instructional and didactic design, participation in online sessions, and the role of faculty during the pandemic. Descriptive and correlative analyses were performed to examine relationships among variables. Qualitative data were analyzed within a hermeneutically-oriented framework. Our analyses show that virtual classrooms or learning management systems (LMS) continued to be used after the pandemic. However, their use is strongly associated with a more traditional approach to teaching; LMSs and virtual classrooms are primarily used as filing locations and for receiving and storing homework assignments and assessments. In terms of interaction, qualitative analyses have shown that relationships with faculty and between students are better in face-to-face classes and that there is not much difference in perceptions of collaborative work in online learning compared to face-to-face classes. Finally, students' expectations of academic success are low in the short term, while they are higher in the long term. Students also indicated that it is important to them that teachers take care about the didactic design of the course, including strategies that encourage interaction and participation in both synchronous and asynchronous sessions. In addition, students mentioned that teachers should incorporate strategies to promote academic motivation, self-regulation, and a safe environment. An important finding of this study is that students prefer a blended learning format when comparing online and face-to-face instruction. This article also provides recommendations for the new post-pandemic education scenario. Doi: 10.28991/ESJ-2023-SIED2-017 Full Text: PDF
The importance of digital evidence strategies

Abstract As the complexity of digital forensic work continues to grow, and the demands and pressures placed on practitioners to complete their investigatory commitments remain, methods for conducting effective and efficient work are of paramount importance. To combat examination challenges any investigating team requires two fundamental and linked components; those conducting DF examinations should develop (1) a digital evidence strategy (DES) that outlines an effective investigative approach, and, (2) deploy it using appropriate tools and techniques. While these should be considered as a pair, arguably as tools have become more comprehensive and more akin to “ suites ,” there is a real risk that tools themselves are being considered an “ examination strategy ,” bypassing the need for investigative forethought. Given this concern, through the vehicle of an example deconstructed hypothetical forensic examination process, this work discusses the relationship between DESs and digital forensic tools, and the importance of both. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation
Implementasi Metode Dynamic Time Warping Pada Aplikasi Kamus Bahasa Isyarat Indonesia Sebagai Media Komunikasi

Communication is something vital in human existence. Communication is generally divided into two, namely verbal and non-verbal. For deaf and speech impaired people, the comic used is sign language and the intermediary used is a web-based application based on sign language from SIBI, with the implementation of the Dynamic Time Warping (DTW) method to analyze its usability and use. The use of this method in sign language dictionaries is to change the time coefficient into distance, because similarities in images can produce patterns from the object that is the example. In this test there are 24 letters and for each letter 10 image models taken via camera are used, and training is carried out with 50 datasets. The average test result collected reaches 90% because the condition of the hand can be said to be like the condition of other hands. This application is used to learn SIBI language, translate and read hand movements via real-time video. And during testing, accuracy results affect certain situations such as camera capacity, model coloring in the background and hand movements, and light brightness in the training data.&#x0D; &#x0D; Keyword: Metode Dynamic Time Warping, Indonesian Sign Language System, Deaf and Speech Impaired
Aplikasi Sistem Pendukung Keputusan Menggunakan Algoritma C5 Untuk Menentukan Penerima Bantuan Sosial

Poverty is one of the development problems in various fields which is characterized by high unemployment, underdevelopment and deterioration caused by change. Most of the residents of Setia village, Pahae Jae sub-district, still have many poor people, the poverty rate is still high and they have implemented a social assistance system for the poor or underprivileged to reduce poverty. However, it turns out that the selection of social assistance recipients in the Setia Village area, Pahae Jae District is still using a manual system. the selection process is carried out by observing the residents' files from the start of the process to who can receive assistance based on the criteria that have been determined in the social section. So that the settlement process in determining the prospective recipients of social assistance does not occur systematically and sometimes is not on target, the decision tree algorithm c5.0 method was chosen by the author to speed up and facilitate the selection of eligible citizens to receive social assistance. the criteria are processed so that and obtain a value that will be compared with the training data, this research is an application of the classification of eligible and unworthy social assistance recipients. building this program or application can help make it easier for the village to determine recipients of the social assistance program for underprivile ged families.