Reviewerly/exHarmony huggingface dataset:
Analysing the Impact of Vibrations on Smart Wheelchair Systems and Users

Mechanical vibrations due to uneven terrains can significantly impact the accuracy of computer vision systems installed on any moving vehicle. In this study, we investigate the impact of mechanical vibrations induced using artificial bumps in a controlled environment on the performance of smart computer vision systems installed on an Electrical powered Wheelchair (EPW). Besides, the impact of the vibrations on the user’s health and comfort is quantified using the vertical acceleration of an Inertial Measurement Unit (IMU) sensor according to the ISO standard 2631. The proposed smart computer vision system is a semantic segmentation based on deep learning for pixels classification that provides environmental cues for visually impaired users to facilitate safe and independent navigation. In addition, it provides the EPW user with the estimated distance to objects of interest. Results show that a high level of vibrations can negatively impact the performance of the computer vision system installed on powered wheelchairs. Also, high levels of whole-body vibrations negatively impact the user’s health and comfort.
Utilizing augmented reality technology for teaching fundamentals of the human brain and EEG electrode placement

Abstract This paper demonstrates the use of augmented reality (AR) to teach the fundamental aspects of the human brain and guide proper EEG electrode placement. The proposed application consists of two main parts: (1) the proposed marker-based AR system uses the Vuforia technique to determine the dimension of the head to create the virtual brain and virtual EEG electrodes; and (2) user interaction and implementation. We performed two experiments using a phantom head to verify the size and workspace area of the marker and validated the position of the virtual electrode with ground truth data. The results showed that the proposed method can be employed for electrode placement guidance in the recommended range. We aim to use the proposed system for beginners. We will further test the system with human heads to evaluate the usability and determine key areas for application improvement.
Real-time fall detection system based on deep learning and infrared array sensors

In this paper, a novel fall detection algorithm based infrared image is proposed. Firstly, the RetinexNet algorithm is adopted for the infrared image pre-processing and enhancement, then the YOLOv3 algorithm is improved by adding three bounding boxes to achieve the task of falling posture detection and recognition, finally a fall data set collected by ourselves is utilized to train and test the algorithm. The experimental results shows that our proposed algorithm achieves excellent fall detection accuracy result and outperforms the traditional YOLOv3 algorithm, the average accuracy of our proposed algorithm is more than 90.86%, which meets the requirements of the fall detection task quite well.
Modernization and Innovative Development in Society 5.0

Modernization of society goes hand in hand with innovations made in technology. Society 5.0 is a smart society that thrives on human-centered applications of innovative technologies like IoT, blockchain, and AI. It is proposed to be a future that builds upon Industry 4.0. There is a need to ensure a proper definition of the consequences and transformations when it comes to implementing various models that help in the betterment of the daily resources accessed by the people. Society 5.0 is potentially promising as it proposes to deepen the relationship between people and technology and assures a positive impact on all aspects of society. This chapter briefly describes the concepts involving the upcoming changes in society. How modernization and innovation work parallelly with Society 5.0. Finally, the chapter elaborates on the various case studies related to Smart contracts, waste management systems, and Artificial Intelligence scenarios of Society 5.0.
Smart Surveillance System for Anomaly Recognition

Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and authority without any human interference. It is a very critical issue in a smart surveillance system to instantly detect an anomalous behaviour in video surveillance system. In this project, a unified framework based on deep neural network framework is proposed to detect anomalous activities. This neural network framework consists of (a) an object detection module, (b) an object discriminator and tracking module, (c) an anomalous activity detection module based on recurrent neural network. The system is a web application where user can apply for three different security services namely motion detection, fall detection and anomaly detection which is applicable for monitoring different environment like homes, roads, offices, schools, shops, etc. On detection of anomalous activity the system will notify the user and responsible authority regarding the anomaly through mail with an anomaly detected frame attachment.
Multimedia Planning for Junior High School Gamelan Learning

Gamelan music is a traditional music which is a product of original Indonesian art and culture that needs to be preserved.In junior high school cultural art learning materials, learning plays traditional music ensemble, due to this pandemic student are required to study at home, therefore, in this study, we will design a basic gamelan learning application for junior high school students.This application will use the Power point.The results of the study make it easy for students to learn and understand music gamelan at home.The research method used is descriptive research with an approach qualitative.
A Blind Digital Signature Protocol over NTRU

Actually, the NIST post-quantum cryptosystem standardization competition reached its third round with seven finalist candidates. And NIST invites the cryptographic community to analyzing the selected candidates. In this context, we contribute by creating a new blind digital signature protocol over our release of NTRU post-quantum cryptosystem. Our protocol can be a variant of FALCON digital signature scheme, which is among of those finalist candidates. Because of our NTRU release is additively homomorphic, we successfully blind and unblind the digital signature by adding a random message. We obtained good results; the speed performance of our protocol outperforms FALCON by a factor up to 27, with a stronger security level, and perfect correctness.
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG

Large pretrained models enable transfer learning to low-resource domains for language generation tasks.However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning to different extents.To exploit these varying potentials for transfer learning, we propose a new hierarchical approach for few-shot and zero-shot generation.Our approach consists of a three-moduled jointly trained architecture: the first module independently lexicalises the distinct units of information in the input as sentence sub-units (e.g.phrases), the second module recurrently aggregates these sub-units to generate a unified intermediate output, while the third module subsequently post-edits it to generate a coherent and fluent final text.We perform extensive empirical analysis and ablation studies on fewshot and zero-shot settings across 4 datasets.Automatic and human evaluation shows that the proposed hierarchical approach is consistently capable of achieving state-of-the-art results when compared to previous work. 1
Logical Choice in Hades Does Home Improvement and Its Bahasa Indonesia Translation

Clause complex is a relation between clause and further clause to create sequence or flow of information.It is part of logical metafunction.Its relations are divided into conjunctive relations, taxis, and logico-semantic relation.Translating clause complex often poses a problem for translators because it relates to mode of meaning.This study, thus, aims to find 1) the pattern of logical choices, including conjunctive relations, taxis, and logico-semantic relations and 2) the translation shifts in mode of meaning in Hades Does Home Improvement by using a qualitative method.From the findings, firstly, 132 complex clauses are found in the ST and 133 are found in the TT.The ST is realized with 129 external conjunctions and 3 clauses with continuative.The TT is realized with 125 external conjunction, 3 clauses with continuative, and 5 clauses changing from complex to simplex.In both the ST and the TT, the preferred taxis system is parataxis and the most employed logico-semantic type is enhancement.Secondly, three translation shifts are found: 1) from textual to logical metafunction (complex into simplex), 2) from logical to textual metafunction (explicit into implicit and vice versa), and 3) between logical metafunction, including taxis shift and logico-semantic realization shift.The similarities of the clause complex relation in ST and TT are stimulated by the similar system of logical choice in the ST and the TT, and because the translator expects the translation to be similar to the original rather than considering the reader perspectives.
Modelling of Child Seat Dependencies for Digital Information Services using Semantic Technologies

With the increasing importance of knowledge management and the growing amount of data in industry, a field of application for semantic technologies has emerged with the representation of knowledge in the area of technical communication. Semantic networks and ontologies offer a high degree of intelligence and can make complex relationships interpretable for humans and machines alike. This aspect can lead to an improvement in the classification, connection, and delivery possibilities of content. Technical documentation is one of many areas where semantic technologies and knowledge graphs can add value to industry. This paper should give an outlook on how semantic technologies can be used in different use cases by companies in the domain of technical communication in the future. Based on a use case of a German company of the child seat industry, the use of semantic technologies and their various use cases in technical communication is shown. The company has started to use semantic technologies to simplify processes and to provide their digital information products in a user- and context-oriented way. This paper focuses on the possibilities that the use of semantic technologies, especially ontologies, provides in a company. It also highlights the difficulties that can arise in the transition and transformation of data.
Understanding the Agile Approach: A Grounded Theory of How Empower Team Members and Stakeholders Case Study: Petroleum Corporate in Tunisia

Purpose: The Important principles of the Agile Manifesto can be identified directly or indirectly from the characteristics of a team Agile development.It works by iterative and incremental cycles of creating value for the user, it is self-organized, in constant collaboration with his client, and tries to continuously improve by wearing a critical look at his activity and his way of function.Faced with this wide variety of choices, an inevitable question arises for companies wishing to deploy Agile: How does empowerment occur through an interactional change in Agile team developments?To address this research question, this article follows a grounded theory approach and draws on 20 semi-structured in-depth interviews with team members and project managers working with the Agile approach.From analyzing the teams and their interactions, the creation of empowerment through continuous interactions with their stakeholders in the organizational context of petroleum industries.Design/Method/Approach: In order to understand the principles of the Agile method and the empowerment of the team in the context of Agile, we conducted an exploratory case study in a petroleum company.This study is based on twenty semi-structured interviews with fifteen project managers and team members using the Agile method.Each interview took approximately 45 -120 min.These face-to-face interviews aided to record the verbal information and capture the interviewee's expressions and tone.All these interviews were transcribed for analysis.The NVivo software analysis was used to gather the data for the research.This data helped in developing more knowledge about tasks allocated to Agile teams.We applied open coding, the Strauss, and Corbin GT's procedure of data analysis to participants' transcribed interview responses.In addition to Interviews, we accompanied the teams of developers How to cite this paper:
COMPARATIVE ANALYSIS OF DIFFERENT CALCULATION METHODS OF THE GEOLOGICAL STRENGTH INDEX (GSI) BASED ON QUALITATIVE AND QUANTITATIVE APPROACHES

In this research, the dispersion of the Geological Strength Index (GSI) values obtained with quantitative and qualitative approaches has been evaluated in four rock outcrops of different quality. The subjective component associated with qualitative or visual methods has been studied by conducting a virtual survey in a group of forty participants constituted by civil engineers, geological engineers, and mining engineers from Peru, Spain, and Chile, who were given a data sheet with a photograph and a basic description of each rock mass. The results showed that the GSI values fit a normal distribution characterized by a mean value and a standard deviation, which in some cases could present moderate to high coefficients of variation (COVs). This paper also includes the study of the dispersion of the GSI values obtained with quantitative formulations that have been evaluated and incorporated into regional databases to assess trends, mainly in the GSI-RMR’ relationships. The results indicate that the average GSI values reported with both approaches are similar; however, with the quantitative methodologies, COV values were classified as low to moderate, which is better adjusted to the suggested COV values for the GSI. Despite this, quantitative methodologies must be used with caution, taking into account the characteristics of the rock masses on which the relationships have been defined.
Construction Course Ideology and Politics Knowledge Chains in Inorganic Element Chemistry

Inorganic element chemistry is a compulsory course for students majored in chemistry or materials.This paper constructs the chain of inorganic element chemical ideological and political knowledge from the aspects of the development history of chemical element periodic table, the discovery history of chemical elements, the history of ancient Chinese science, the example of Chinese chemists, the double blade sword of chemical elements, and expanding the time and space of course ideology and politics education.The chemistry education with better knowledge teaching, ability training and value shaping has been well defined.
The Software Implementation of a Metagraph Processing System Based on the Big Data Approach

The paper discusses the approach to solving the problem of processing metagraphs using Big Data technology. The formal definition of the metagraph data model and the metagraph agent model are given. The metagraph representation using the flat graph model is discussed. The flat graph and metagraph Big Data processing are described. The architecture of the system for processing data in metagraph is discussed. The metagraph processing using metagraph agents based on Big Data technology is discussed. The experiments result for parallel metagraph processing are given.
Evaluating Acceleration Techniques for Genetic Neural Architecture Search

The increase in the available data and computational power has led to the rapid evolution of the field of deep learning over the last few years. However, the success of deep learning methods relies on making appropriate neural architecture choices, which is not a straightforward task and usually requires a time-consuming trial-and-error procedure. Neural architecture search is the process of automating the design of neural network architectures capable of performing well on specific tasks. It is a field that has emerged in order to address the problem of designing efficient neural architectures and is gaining popularity due to the rapid evolution of deep learning, which has led to an increasing need for the discovery of high-performing neural architectures. This paper focuses on evolutionary neural architecture search, which is an efficient but also time-consuming and computationally expensive neural architecture search approach, and aims to pave the way for speeding up such algorithms by assessing the effect of acceleration methods on the overall performance of the neural architecture search procedure as well as on the produced architectures.
GENERAL SCIENTIFIC ALGORITHM OF COGNITION AND FORMS OF SCIENTIFIC RESEARCH

Aim.At the epistemological level, fixing the results of scientific research, to identify the forms of the process of searching for truth, which are categories, laws, principles, concepts and scientific theories.Methodology.The work was carried out on the basis of a systematic approach, taking into account the interdisciplinary significance of the subject under study.Results.It is revealed that epistemological forms acquire scientific status only if they reflect the essence of certain fragments of being, have their own specific ontological basis.Concepts (categories) reflect the essence of the elements of the subject area of a particular science; laws are the essence of their connections; principles -the essence of the ways (sequences) of links between elements of a particular subject field; concepts -the essence of the impact of classes of environmental phenomena on the phenomena studied by a particular science; theories -the essence of the influences of the environment on them as a kind of holistic formation.Research implications.The results of the study can be used to improve methodological competencies at the stage of modernization of the education system.
Design and Evaluation: Virtual Reality Haptic Interface to Enhance User Experience

Haptic interface is a platform that convey haptic stimuli to represent and transmit information through touch. This paper systematically focus the achievements of haptic interface and multisensory interaction in virtual reality environment on perspectives of human-computer interaction research: immersive experience and emotional expression, and proposes an evaluation system based on the Quality of Experience (QoE) model aimed to quantitatively analyze and enhance human-computer interaction user experience in haptic interfaces with its statistical significance, which assists researchers and engineers to research, develop, and utilize haptic interaction technologies in virtual reality environments.
Unsupervised Domain Adaptation using Maximum Mean Covariance Discrepancy and Variational Autoencoder

Face Recognition has progressed tremendously from its initial use of holistic learning models to using handcrafted, shallow, and deep learning models.DeepFace, a ninelayer Deep Convolutional Neural Network (DCNN), reached nearhuman performance on unconstrained face recognition for the Labeled Faces in the Wild (LFW) dataset.These models performed very well on the benchmark datasets, but their performance sometimes deteriorated for real-world applications.The problem arose when there was a domain shift due to different distribution spaces of the training and testing models.Few researchers looked at Unsupervised Domain Adaptation (UDA) to find the domaininvariant feature spaces.They tried to minimize the domain discrepancy using a static loss of maximum mean discrepancy (MMD).From MMD, the researchers delved into the higher-order statistics of maximum covariance discrepancy (MCD).MMD and MCD were combined to get maximum mean and covariance discrepancy (MMCD), which captured more information than MMD alone.We use a Variational Autoencoder (VAE) with joint mean and covariance discrepancy to offer a solution for domain adaptation.The proposed MMCD-VAE model uses VAE to measure the discrepancy in the spread of variance around the mean value and uses MMCD to measure the directional discrepancy in the variance.Analysis was done using the TinyFace benchmark dataset and the Bollywood Celebrities dataset.Three objective image quality parameters, namely SSIM, pieAPP, and SIFT feature matching, demonstrate the superiority of MMCD-VAE over the conventional KL-VAE model.MMCD-VAE shows an 18 % improvement in SSIM and a remarkable improvement in the perceptual quality of the image over the conventional KL-VAE model.
Finding Sub-task Structure with Natural Language Instruction

When mapping a natural language instruction to a sequence of actions, it is often useful to identify sub-tasks in the instruction.Such subtask segmentation, however, is not necessarily provided in the training data.We present the A2LCTC (Action-to-Language Connectionist Temporal Classification) algorithm to automatically discover a sub-task segmentation of an action sequence.A2LCTC does not require annotations of correct sub-task segments and learns to find them from pairs of instruction and action sequence in a weakly-supervised manner.We experiment with the ALFRED dataset and show that A2LCTC accurately finds the sub-task structures.With the discovered subtasks segments, we also train agents that work on the downstream task and empirically show that our algorithm improves the performance.