Reviewerly/exHarmony huggingface dataset:
OFFLINE FILING AND TRANSFER OF ELECTRONIC RECORDS AT ABC AGENCY

The use of electronic records increases in the industrial revolution 4.0 and the implementation of WFH (Work from Home). Records management needs to be carried out on all media and types of records. Electronic records management needs to be carried out on filing and transferring records to maintain the authenticity, integrity, security, and safety of records. ABC agency already uses the Records Information System (SRIKANDI), but not all types of records can be managed using that information system. The information system cannot manage electronic records of types of photos, videos, and sound recordings. The records must be managed at ABC agency, so it is necessary to determine the filing and transferring the records offline. This research is qualitative research using data collection techniques: observations, interviews, and documents search. Interviews were conducted with three experts in records management, both manual and electronic. The analysis is carried out based on the stages contained in the five regulatory documents. The analysis results show that the filing stages consist of preparation, inspection, index determination, file code determination, registration/input of records information, inserting records into folders, cross-pointing (if any), labelling, and compiling a list of current records. Moreover, the transfer stage consists of preparation, selection of records, arranging semi-current records, creating a list of semi-current records to be moved, and creating a report of records transfer.
Competence Assessment and Development for Managing Human Capital in the IT Sector

With ever-increasing demand for software professionals, time has come for the information technology (IT) industry to unearth competencies required for software professionals. The majority of IT companies have been employing outsourcing software projects to inexpensive hubs in developing nations. Therefore expectations from highly skilled software professionals are rising. The purpose of the study is to recognize the technical competence levels associated with software professionals in a range of profiles which are found in India's major IT organizations. There are three basic objectives of the present study. The first objective is to conduct a literature survey of description of the software engineers in IT domain. The second objective is to quantify the competency levels of the software engineers in IT sector. Finally, the third objective is to conduct a survey-based empirical study on software engineers. The professional occupation is set up through seven successive profiles.
The algebraic solvability of the novel lamplighter puzzle

In this study, the group of finite cyclic lamplighter states is reinterpreted as the novel lamplighter puzzle. The rules of the puzzle are outlined and related back to properties of the lamplighter group with specific interest placed upon the discussion of which puzzle instances are solvable. The paper shows that, through the use of algebra, many puzzle instances can be identified as solvable without the use of an exhaustive search algorithm. Solvability depends upon the creation of irregular generating sets for subgroups of the finite cyclic lamplighter group and the cosets formed by these subgroups. Further possible generalizations of the lamplighter puzzle are also discussed in closing.
Simultaneous estimation of projector and camera poses for multiple oneshot scan using pixel-wise correspondences estimated by U-Nets and GCN

Dense and accurate 3D shape acquisition of objects by active-stereo technique has been an important research topic and intensively researched. One of the promising fields for active-stereo techniques is medical applications, such as 3D endoscope systems. In such systems, since a sensor is dynamically moved during the operation, single-frame shape reconstruction, a.k.a. oneshot scan, is necessary. For oneshot scan, there are several open problems, such as low resolution because of spatial coding, and unstable correspondence estimation between the detected patterns and the projected pattern because of irregular reflection. In this paper, we propose a solution for those problems. To increase the resolution, an accurate and stable interpolation method based on deep neural networks (DNNs) is proposed. Since most patterns used for oneshot scan are periodic, pixel-wise phase estimation can be achieved by detecting repetition in the pattern. A graph convolutional network (GCN), which is a deep neural network for graphs, is used for the correspondence problem. In the experiment, pixel-wise shape reconstruction results, as well as robust correspondence estimation using DNNs and a GCN, are shown. In addition, the effectiveness of the techniques is confirmed by comparing the proposed method with existing methods.
Feature Selection Using Elephant Herding Optimization Hybridized with Gray Wolf Optimization for Anomaly Detection in Wafer Manufacturing

Machine learning algorithms are commonly used in many applications, and their usage in manufacturing industries is very crucial. Anomaly detection in manufacturing has to consider lot of factors, and it leads to high-dimensional data. In general, either feature selection or dimensionality reduction technique will be used before classifying high-dimensional data. In this research work, feature selection is proposed using hybrid version of elephant herding optimization and gray wolf optimization. Here, elephant herding optimization is used for selecting ideal feature subset, and gray wolf optimization is used to find the optimized control parameters for elephant herding optimization. The suggested feature selection method is examined for anomaly detection in industrial wafer manufacturing. K-nearest neighbor, stochastic gradient descent, and decision trees are tested as classifiers in this research work for categorizing the selected features. Notably, stochastic gradient descent provides balanced accuracy of 96% when the proposed feature selection method is used, while it just provides balanced accuracy of 73% if the proposed feature selection is not used.
Diseased Crop Removal Assistance System Using Augmented Reality and Drones

Removal of diseased crops is a necessary agricultural operation on which a lot of labor and time will be spent every year. Augmented Reality (AR) can provide auxiliary information to help identify if a crop is infected or when to harvest, thus reducing labor, time consumption, and crop loss. In this study, we propose a crop tagging method by aerial photography with drones and AR applications. Aerial images with location information taken by a drone equipped with Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) are used to estimate the location of diseased crops. Based on the location data, the AR application runs on the mobile device will generate guidance information. The AR application will track mobile devices for real-time 3D registration. Experiments in outdoor environments verified its accuracy and feasibility.
A Study of Multidimensional Model for English Phonetic Acquisition with the Support of Mobile Technology

In economic globalization, college students need to constantly improve their English proficiency for higher requirements. Phonological competence, to a large extent, has a great influence on learners' evaluation of their overall English learning ability, which eventually affects their English academic achievements. Therefore, it is necessary to construct a scientific and feasible model for English phonetic learning. The rapid development of mobile technology creates a new opportunity for the realization of the model. The purpose of this article is to introduce a multidimensional model for English phonetic acquisition based on mobile applications, which allows learners to develop their English phonological competence anytime anywhere with the help of mobile technology and provides better opportunities for learners to improve their English proficiency.
Improving Traffic Safety By Using Waze User Reports

Abstract Road inspection regularity and existing types made by road maintenance crew have not been good enough to be aware what is really happening on the roads. Road users' contribution in road traffic safety is very important to ensure fast reaction on different road hazards. It is important to ensure not only the most common ways to report road hazards on state roads by phone, by email and on social media, but also expand data sources options in modern and user-friendly way. Waze navigation application already had functionality to report road hazards – to warn other application users, but no one acted to solve these road hazards until someone reported them through existing communication channels supported by Latvian State roads or Latvian road maintainer. To ensure better road traffic safety and faster reaction time on road hazards solving, Latvian road maintainer gained access to Waze report feed, and, in corporation with Riga Technical university, made a system for analysing and processing Waze data. As the result - Latvian roads maintainer can improve road safety by faster reaction to road hazards reported by Waze users. Today, up to 70 % from total reports processed by Latvian road maintainer are generated by Waze.
Extraction of Pipeline Vibration Feature Information Based on the Combination of EWT and SVD

Due to the problem of endpoint effect in the decomposition of hydraulic structure vibration data by EMD (empirical mode decomposition) method and the resultant mode confusion, a method based on EWT (empirical wavelet transform) combined with SVD (singular value decomposition) of hydraulic structure vibration signal noise reduction method. It is verified through engineering examples that the combination of EWT and SVD can effectively filter out high-frequency vibration noise and low-frequency water flow noise, and has superiority in extracting the characteristic information of hydraulic structure vibration signal.
Rainfall Data Modeling with Artificial Neural Networks Approach

Abstract Rainfall is one of the important information that widely used in various fields. Rainfall data involving location information is referred to as spatial rainfall data. Some of the model approaches to spatial rainfall data are the Vector Autoregressive (VAR) model, state-space, Markov chain stochastic model, and Geographically Weighted Regression (GWR). However, these models have not been able to produce predictions of the occurrence of no rain (zero value) or extreme values. Currently, theoretical modelling is mostly approached by artificial neural network (ANN) techniques. The purpose of this study is to model spatial rainfall data in East Java, Indonesia in 2020 with the ANN approach which is supported by several variables such as location and elevation information. The ANN used backpropagation and Rporp by combining the learning rate and layer which is then obtained the RMSE value. The results show that the best model has the smallest RMSE of 1.22 when the learning rate is 0.15 on 11 layers using Rprop algorithm.
Education in Cybernetic Illumination by Marian Mazur

The article presents the cybernetic approach and justifications of the same kind for highlighting flaws in the functioning of today’s, still unchanged, mass school, proffered by Marian Mazur, the founder of the Polish school of cybernetics. This author analyses all the important dimensions of school functioning – from the content, through methodology to its organisation – pointing to the rationality of an optimising cybernetic approach to education, which is perfectly consistent with the common sense feeling about the weakness of schools. Although Mazur’s analyses date back to the 1960s, they seem to be applicable today not only to our country, but also to the entire world.
Research on the Influence of Communication on the Trust Between Supply Chain Enterprises Based on Structural Equation Modeling

The revolution of information technology such as blockchain and the Internet of Things has significantly changed the way of communication and trust between supply chain enterprises. However, few studies have focused on this phenomenon. This study aims to examine the effect of communication on trust between supply chain enterprises in the context of emerging information technologies. Based on the literatures, the assumption is proposed by constructing the inter-enterprises trust model with the samples of 196 manufacturing enterprises in China. SPSS 22.0 and AMOS 22.0 are used for empirical analysis. Confirmatory Factor Analysis (CFA) is adopted to analyze the total reality and validity for measurements. Structural equation model (SEM) is applied to analyze the relationship among the latent variables and construct the hypothesis testing. The results show that communication has a positive effect on trust among supply chain enterprises. Especially, instrumental communication rooted in information technology is positively related to information sharing. What's more, information sharing plays a partial mediating role between instrumental communication and trust among supply chain enterprises. The findings enhance our understanding of the relationships among communication and trust between supply chain in information intelligence age, thereby guiding enterprises to implement informatization strategy in supply chain management.
TOLERANCE AS A VALUE OF A FUTURE HIGHER EDUCATION TEACHER

Approaches to the interpretation of the term "tolerance" are revealed. The preparation of the future teacher for the formation of tolerance as an important component of the system of his professional training is outlined. The organizational and pedagogical conditions of activity of the institution of higher education which provide preparation of future teachers for formation of tolerance in them are defined.
A Small Phase Shift Attack on Blind Quantum Computing

Blind quantum computation provides protocols which enable users to get access to quantum computing resources without leaking information about the algorithms to be performed. This paper suggests a possible attack on blind quantum computation, which can corrupt the quantum operations and their expected outputs.
Performance Study of Image Data Augmentation by Generative Adversarial Networks

Convolutional Neural Networks (CNN) are becoming increasingly popular among machine learning techniques for image classification problems. But these networks are highly dependent on large set of data for proper learning to avoid overfitting in the context of supervised classification. Contrary to numerical data, image data poses difficulty for data augmentation by artificial increase of training data. Recently Generative Adversarial Networks (GAN) have been developed and are increasingly used for generation of image data. It has been proved that GAN has higher potential in generating image data for training purpose compared to traditional methods of data augmentation in the area of image processing. In this work we have studied the performance of different GAN developed recently in terms of their effectiveness in classification problems compared to baseline CNN without GAN. We have also done the comparative study on the qualities of fake image generation by different GAN in terms of IS (Inception Score) and FID (Fréchet Inception Distance). In this work we have studied the performance of Lightweight GAN, Data-efficient GAN and StyleGAN-ADA, in addition to the most conventional Deep Convolutional Generative Adversarial Networks (DCGAN). Simulation experiments have been done using bench mark data set Food-101. The results show that Lightweight GAN is the most efficient for data augmentation.
Toward an Adversarial Model for Keystroke Authentication in Embedded Devices

Hegde, Niranjan Sankaran, SriramThe increase in computing and communication capabilities has enabled embedded devices to perform a wide variety of sensing and monitoring tasks in diverse domains. Keystroke authentication for embedded devices leverages the unique typing patterns of users toward creating behavioral profiles for identification. However, keystroke authentication can be bypassed by manipulating inputs thus causing machine learning models to operate on perturbed data resulting in higher error rates. In this paper, we propose to develop an adversarial model for keystroke authentication in embedded devices. The proposed model leverages Feature Importance Guided Attack (FIGA) [1] method to add perturbations in the inputs which in turn increases error rate thus allowing the attacker access to the system. Evaluation using RandomForest, XGBoost, and SVM models shows an average Error rate across all the users to be 60%, 55%, and 63%, respectively. Our analysis shows that it takes an average of 3.18 s for the attacker to generate samples per user and that the generated samples are realistic in nature. Finally, the computational complexity of our proposed model is O(n) which is comparable with existing adversarial models.
Semi-supervised Deep Learning for Secure Internet of Things

Previous chapters demonstrate the great success achieved by principally in supervised settings, by leveraging a larger volume of precisely annotated dataset. Nevertheless, annotated data instances are frequently complicated, costly, or laborious to acquire.
Optimized CNN Based Multi-scale Image Composition

Image composition aims at compositing specific candidate region into the target image. Taking advantage of this powerful technique, more and more interactive images could be generated. However, traditional composition methods couldn't retrieve candidate images accurately. Moreover, the results are not consistent, especially for the composited boundary. In order to solve the above problems, we proposed an optimized CNN(convolutional neural network) based multi-scale image composition model. On the one hand, the CNN model retrieves the candidate images more accurately for it can extract deep semantic information of images. On the other hand, we used a multi-scale processing model which could achieve high-performance results. To evaluate the models, we then built an experimental database containing a variety of images. Based on this, many groups of evaluation criteria and comparative experiments performed proved the effectiveness and robustness of our method.
Modern Way of Learning Using Augmented Reality (AR)

The main objective for developing this project is to build a platform for children that enables learning in a very efficient way. It encourages a next level of education that is free, creative and convenient. Introduction of virtual learning to the primary schools can provide a joyful learning experience to the students. The project is built using Augmented Reality technology. This technique involves 3D learning that aids the learning process and has a greater impact on the children's learning, as they are more likely to remember the facts and lessons learned. It helps children identify and organize visual information and learn skills easily. It's simply because visuals hold more appeal than plain text to those curious and intuitive young minds. Understanding and grasping concepts in their curriculum is made simple and fun with this technique of learning. The software technology used to build this application is unity3D (software) and Vuforia web portal (to create a database) as a prerequisite. Some image targets are set so that the camera can recognize the images and display a 3d model based on it. It can be done by adding some 3D assets in required fields and it is altered accordingly. After setting all these we will be able to extract an apk file from the scene created in software which can be installed in Android or iOS mobile devices or tabs. The user will be able to operate it just by scanning the specific image targets. This project is implemented at school level which can benefit in various ways. It helps to spark kids' interest in subjects. This approach promotes active learning and can encourage kids to use their creativity for their own pleasure. As they get older, the creative skills they developed in the process can be used to solve every day or even global problems. This project has a wider scope and can further be implemented in various other fields.
User-Guided Image Inpatinting with Transformer

Deep learning has brought unprecedented progress to image inpainting. Most of the image inpainting methods with guidance mainly use the line as guiding information, which is still a huge challenge for ordinary users. Thus, we propose a novel method for user-guided image inpainting, this method can achieve interactive object extraction in the guided image and add the target object to the original image reasonably. Our model splits this task into three parts: interactive mask extractor, guided feature extractor and inpainting network. The interactive mask extractor can interactively extract the target object from the guided image. Then, we adopt the guided feature extractor to extract the features of the target object, Finally, the inpainting network generates the fine-grained images by fusing the features from the original and guided image. The experimental results prove that our model has a higher inpainting quality than the existing state-of-the-art approaches.
Propositional Dynamic Logic with Quantification over Regular Computation Sequences

We extend test-free regular propositional dynamic logic with operators expressing combinations of existential and universal quantifiers quantifying over computation sequences represented by a given regular expression and states accessible via these computation sequences. This extended language is able to express that there is a computation sequence represented by a given regular expression that leads only to states where a given formula is satisfied, or that for all computation sequences represented by a given regular expression there is a state accessible via the computation sequence where a given formula is satisfied. Such quantifier combinations are essential in expressing, for instance, that a given non-deterministic finite automaton accepts all words of a given regular language or that there is a specific sequence of actions instantiating a plan expressed by a regular expression that is guaranteed to accomplish a certain goal. The existential-universal quantifier combination is modelled by neighborhood functions. We prove that a rich fragment of our logic is decidable and $$ EXPTIME $$ -complete by embedding the fragment into deterministic propositional dynamic logic.
Emerging Advancement for Augmented Reality (AR) and Virtual Reality (VR) in Dentistry

The skill of visual reality has matured, and VR and AR are increasingly being used in educational and surgical settings. The development of virtual reality technologies allows users to mix medical knowledge, medical data, and graphical data. It can provide more precise information, allowing users to increase their safety and reduce their risk. Virtual reality (VR) or augmented reality (AR) simulators that provide direct feedback and objective evaluation could be a useful tool in dental education in the future. Not only has it been applied to education, but it has also been created in therapeutic therapy. The authors believe that in the future VR and AR training and teaching will be extended and used in every aspect of dentistry, enabling students to develop their abilities on their own. In comparison to augmented reality, virtual reality offers a far more immersive experience. It would establish a trusting relationship between patients and doctors based on the experience of the dentists and the use of different hardware and software.