Reviewerly/exHarmony huggingface dataset:
Process genre-based approach in improving the essay writing skills of grade 3 learners in a selected public elementary school towards a proposed PGB compendium

This study aimed to assess the effectiveness of the Process Genre-Based (PGB) Approach in improving the essay writing skills of Grade 3 learners in Cainta Elementary School using a quasi-experimental method that utilized the pre-test-post-test design.Two groups composed of 35 learners each served as experimental and control groups.The PGB approach was implemented on the experimental group using the proposed PGB compendium.A pre-test was conducted at the start of the experiment.It was given as a post-test at the end of the investigation.In addition, the researcher-developed instrument underwent validation.The test assessed the learners' writing in the content, organization, grammar, vocabulary, and mechanics using a 5-point rubric.Results of the study indicate that the experimental and control groups had comparable pre-test performance in the aspects of content, organization, grammar, vocabulary, and mechanics.Post-test results generally showed that the experimental group exhibited better performance levels than the control group in all aspects of writing.Statistical analysis, however, revealed that the experimental group performed significantly better in the areas of vocabulary and content.These results suggest that PGB can improve the pupils' writing skills, and its extensive use with appropriate teacher training is strongly recommended.
Analysis of Object-Centric Visual Preference in 360-Degree Videos

To provide information for 360-degree visual space exploration, we design experiments to measure and analyze object-centric visual preference. After defining the static and dynamic properties of the objects of interest, we collect real-shot 360-degree videos and synthesize computer-generated 360-degree videos so that the objects have different combinations of static and dynamic properties. From head movement trajectories of subjects wearing head-mounted displays and watching 360-degree videos, we compare visual preference between objects with different static and dynamic properties. The experimental results indicate that subjects have visual preference for certain static and dynamic properties of objects over others; with this knowledge we can construct visually salient viewports by detecting and comparing static and dynamic properties of objects in a 360-degree video.
Image quality radiographic testing on the making of trabeculae genu phantom by using ingredients surfactant

Abstract Exposure to radiation can be dangerous for the body. Hence why we need an effort to minimalize the effects of radiation received, especially for students and lectures during practicum that uses source of radiation. One of the efforts that can be done is to use phantom radiology so that there is no need to expose living things. Previous research has done to make phantom radiology by using gypsum instead of human bones and acrylic as a substitute for soft tissue (human flesh). However, this research still has a shortage which is trabeculae cannot be formed in the phantom produced. The purpose of this study was to determine whether the surfactant solution could be used to form trabeculae in phantom genu by using in the concentration of surfactants with a ratio of 2: 1. The research was conducted in three stages, namely designing the genu phantom and trabecular phantom , printing genu phantoms and trabeculae, and conducting radiographic quality tests of genu phantom . Trabeculae in phantom genu have been successfully made with a water and surfactant ratio of 2:1. Radiographic quality tests showed good detail, sharpness and contrast. The presence of trabeculae and soft tissue is apparent. Surfactants with a ratio of 2:1 can be used to form trabeculae on phantom genu with good quality in radiographic test results.
A Gesture Elicitation Study to Generate Interaction Design Insights for Self-reporting of Mental and Body States Using a Portable Device

Self-report of mental or body states through Ecological Momentary Assessment is recommended over asynchronous methods, however, in contexts where users lack time for self-reporting, as with factory operators, this is a challenge. Furthermore, interaction designers lack references and guidelines to design these interactions. We designed a speculative prototype of a portable device that could recognise gestures corresponding to certain mental and body states and we conducted a gesture elicitation study with the prototype with eight participants. The gesture data were analysed and represented graphically to guide and inspire interaction designers. These visualisations were discussed and iterated on through two focus groups with a group of three interaction and communication designers. The study allowed to elicit visual gesture representations which may be useful to inspire and inform interaction designers and suggests that, if representations are aesthetically appealing to designers, they will be encouraged to spend time understanding complex data.
Formal self duality

We study the notion of formal self duality in finite abelian groups. Formal duality in finite abelian groups has been proposed by Cohn, Kumar, Reiher and Sch\"urmann. In this paper we give a precise definition of formally self dual sets and discuss results from the literature in this perspective. Also, we discuss the connection to formally dual codes. We prove that formally self dual sets can be reduced to primitive formally self dual sets similar to a previously known result on general formally dual sets. Furthermore, we describe several properties of formally self dual sets. Also, some new examples of formally self dual sets are presented within this paper. Lastly, we study formally self dual sets of the form $\{(x,F(x)) \ : \ x\in\mathbb{F}_{2^n}\}$ where $F$ is a vectorial Boolean function mapping $\mathbb{F}_{2^n}$ to $\mathbb{F}_{2^n}$.
Improving Search Relevance with Word Embedding Based Clusters

The main purpose of web search engines is to provide the user with most relevant results based on the searched keywords. Finding content that meets the expectations of the user and the relevance of the content to these needs are very important for the success of the search engine. In this study; with the keywords written to the search engine, it is aimed to reach not only the results which contains these terms, but also the semantically related results. Words and phrases in all documents to be searched will be vectorized with Word2Vec model, then phrases will be clustered based on their similarity values. Finally, these outputs will be integrated into a Lucene based NoSQL solution at index time. The study will be used for Kariyer.net’s job search engine. This study includes research and applications in word embeddings, machine learning, unsupervised learning.
HOW TO PERFORM ORACLE DATABASE 11G VERSION UPDATE TO ORACLE DATABASE 19C

Due to the discontinuity of support for one of the database versions most used by organizations, Oracle Database 11g, it is important that companies that still use this version, pay attention to the next upgrade from Oracle. This work was elaborated by means of exploratory research, using as methods and research techniques the documentary and bibliographic analysis, with the purpose of providing steps with techniques and methods of how to proceed with two oracle projects, one of migration from the Linux operating system Red Hat 4.4.7-3 for Oracle Linux 7.7, and a version upgrade from Oracle Database 11g to Oracle Database 19c, presenting strategies using tools and following Oracle's recommendations. This study was built based on real demands that companies have been facing with this great dilemma, the discontinuity of support for the Oracle database 11g version. The observed results were, official support from Oracle, architectural change in order to be prepared for a supposed intervention with updating and migration of services to the cloud.
Assessment of University Intellectual Potential

High competition in the educational environment requires the search for factors and the development of methods to improve the quality of education. The article deals with the issues related to the logic of development and the structure of the intellectual potential of the organization based on the principles of a systematic approach. The definition of the "intellectual potential of the university" concept has been proposed. Based on the structure of intellectual potential, a set of indicators has been proposed to assess the level of intellectual potential of the university. The weighting coefficients of the significance of the indicators have been calculated using the paired comparison method. The level of intellectual potential of the university has been considered as one of the main objective prerequisites for ensuring the competitiveness of the university and the quality of the educational services provided. It has been found that the main factors determining the level of intellectual potential of the university are highly qualified teaching staff, their scientific research, and their work.
Facial Detection in Uncontrolled Environments: Systematic Literature Review

Facial detection is an extremely important topic in biometrics. In particular, its application in uncontrolled environments presents a great challenge for the various existing techniques. This work aims to present and evaluate recent studies on facial detection applied in uncontrolled environments through a systematic review of the literature that addresses what are the main techniques and bases of faces used, as well as what are the main uncontrolled scenarios explored in them. Were formulated three research questions and 85 papers were selected for analysis. Based on the analysis of these works, it can be concluded that the techniques based on convolutional neural networks are the most explored techniques in the literature and that the FDDB face database is the most used in the experiments and specific scenarios present in uncontrolled environments, as occlusion and variation of illumination, are still little investigated.
The Limited-FCMSPCNN algorithm for image defogging

Abstract In order to address the problem of defogging images of foggy scenes, this paper proposes a parameter-tunable image defogging algorithm based on the FC-MSPCNN model. Firstly, we simplify the traditional PCNN model and also propose the Limited-FCMSPCNN (LFC-MSPCNN) model. Secondly, we give the five key parameters in the model. There are five key parameters in the model: α , β , V , G and a modulation parameter q , and how they are set. Thirdly, we verified that the algorithm in this paper has good defogging effect on foggy sky images by comparing with the Dark Channel Prior algorithm, the Retinex algorithm and the FC-MSPCNN algorithm, showing that the LFC-MSPCNN method proposed in this paper has robustness.
Mitigating Sparsity and Cold Start Problem in Collaborative Filtering using Cross-domain Similarity

Collaborative filtering (CF) has proven to be the most prominent and preferable approach in the recommendation systems due to its simplicity and effectiveness. The fundamental philosophy of CF-based recommendation is that similar users have the same rating patterns and similar items obtain similar ratings. However, the accuracy of CF significantly degrades when the dataset becomes highly sparse due to the unavailability of sufficient rating information of a user. The existing similarity measures have not been able to cope up this problem that very well, and this has been the motivation of this paper, in which we aim to improve the performance of collaborative filtering by utilizing the rating information of common users who belong to multiple domains. This approach can albeit both the sparsity (inadequate rating information) and the cold start (no rating information at all) problems. The effectiveness of the proposed approach is examined with the Amazon dataset and the experimental results show that the proposed approach attains high accuracy than the existing CF-based recommendation approaches.
Statistical estimation and nonlinear filtering in environmental pollution

This paper studies a nonlinear filtering problem over an infinite time interval. The signal to be estimated is driven by a stochastic partial differential equation involves unknown parameters. Based on discrete observation, strongly consistent estimators of the parameters are derived at first. With the optimal filter given by Bayes formula, the uniqueness of invariant measure for the signal-filter pair has been verified. The paper then establishes approximation to the optimal filter, showing that the pathwise average distance, per unit time, of the computed approximating filter from the optimal filter converges to zero in probability. Simulation results are presented at last.
Indian Sign Language Recognition

The paper focuses on designing and developing an user interface to help out the dumb community in making a better use of their gestures. It mainly deals with making services of the hand gestures to be easily accessible and understandable to by the people using sign languages. This system makes use of data collected from standards of Indian Sign Languages (ISL). The system is built using machine learning tools, TensorFlow library. The result of this project is to translate the gestures into the texts and return it to the web interface. The sole reason of this project is to help the individual with speech and hearing impairment.
Multi-criteria recommender system with Hybrid Deep Tensor Decomposition

Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings. To alleviate this problem, we propose a generic architecture of hybrid deep tensor decomposition (HDTD) by integrating deep representation learning and tensor decomposition, where the side information is incorporated as a compensation for tensor sparsity. Experimental results on three real-world datasets demonstrate that our HDTD schemes outperform state-of-the-art methods on multi-criteria rating predictions.
Suitability of Voice Recognition Within the IoT Environment

In this chapter, the usability of voice biometric authentication is investigated. In doing so, we first studied the current status of Internet of Things [IoT] authentication technologies. We subsequently determined that existing authentication systems suffer from a number of deficiencies. Some systems need higher computing and energy resources to perform their work, which exacerbates the resource-constrained nature of IoT technology. Others suffer from security deficiencies. Our implementation of the voice biometric shows that it can be used remotely to authenticate the user on their IoT devices.
Pseudo-Label Generation Method Based on Wind Turbine SCADA Data

During the daily operation of the wind turbine, the SCADA system continuously records the various operating parameters of the wind turbine, and has stored a large amount of data for many years. The amount of these data is huge, but most of them are unlabeled data. If you want to use deep learning to study the state of the wind turbine, these data cannot be directly used for analysis. In response to the above problems, this paper proposes a method of pseudo-label generation based on deep neural networks. This method first builds a deep neural network model, secondly uses part of the labeled data to train the built deep neural network model, and finally uses the trained deep neural network model to pseudo-label the unlabeled data, so as to obtain a large number of labeled data, label data set.
Improving the generalisation of genetic programming models with evaluation time and asynchronous parallel computing

In genetic programming (GP), controlling complexity often means reducing the size of evolved expressions. However, previous studies show that size reduction may not avoid model overfitting. Therefore, in this study, we use the evaluation time --- the computational time required to evaluate a GP model on data --- as the estimate of model complexity. The evaluation time depends not only on the size of evolved expressions but also their composition, thus acting as a more nuanced measure of model complexity than size alone. To constrain complexity using this measure of complexity, we employed an explicit control technique and a method that creates a race condition. We used a hybridisation of GP and multiple linear regression (MLRGP) that discovers useful features to boost training performance in our experiments. The improved training increases the chances of overfitting and facilitates a study of how managing complexity with evaluation time can address overfitting. Also, MLRGP allows us to observe the relationship between evaluation time and the number of features in a model. The results show that constraining evaluation time of MLRGP leads to better generalisation than both plain MLRGP and with an effective bloat-control.
Metric for Evaluating Non-Geologic CO2 Utilization Options

Carbon Dioxide Utilization (CDU) has been proposed as a means of converting carbon dioxide that would otherwise be emitted to the atmosphere into a useable or storable product. This work proposes a rapid, standardized methodology for assessing the performance, status, and potential impact of a wide range of CDU technologies. These components are used to develop a multi-dimensional metric for evaluating CDU options.The intended use of the metric is for rapid evaluation of CDU technologies to understand their performance and potential impact if deployed at scale. To support the assessment, a companion spreadsheet is made available for performing energetic and lifecycle analysis of different CDU processes based on major inputs and outputs. This spreadsheet is populated with common reactants and products of carbon utilization and can be modified by the user to include additional reactants or products.Carbon dioxide is a stable molecule that requires energy to be converted into alternate forms. This conversion energy takes the form of thermal, electrical, chemical, or solar energy. Each of these energetic inputs and their associated greenhouse gas emissions are compared to the energetic and greenhouse gas emissions of the product(s) displaced by the CDU synthesis route. The overall impact of the technology is defined as the net difference between the current method of production and the proposed production route via carbon conversion.
An Authentication Mechanism for IoT Devices Based on Traceable and Revocable Identity-Based Encryption

To solve low computing efficiency and high computational energy consumption in the traditional Public-key Infrastructure cryptosystem in the authentication of IoT devices, an authentication scheme for IoT devices based on the Identity-based Encryption is proposed in this paper, which is realized by introducing traceable and revocable mechanisms. The effective management of the secret key of the suspicious devices by the authentication center and the reasonable accountability of the dishonest private key generator (PKG) are realized. Finally, through the security proof, it is verified that the Identity-based Encryption designed in this paper can meet the security of the Chosen Ciphertext Attack (CCA), indicating that the scheme has high practicability and security.
Pad Vending Machine with Cashless Payment

A vending machine aims to provide required product or the service to the customer with certain ease, wherein not much effort is required. This research work aims to design a pad vending machine with an option of payment using QR code which is implemented using blockchain to make the system much more efficient and reliable than the existing systems present in the Indian market. The system is divided into two parts, first being the working of the machine and second being the mode of payment which is implemented using a blockchain. It is noticed many times that due to unpredictable menstrual flow women tend to face a lot problems. To overcome this problem, a pad vending machine is proposed with certain advancements through which women can help themselves in the stated circumstances.
Advanced web methodology for flexible web development

Web development is one of the ways to contribute to the digitization of organizations. However, being a relevant area, it has the contribution of countless companies and people worldwide. This high number of contributions necessarily leads to several trends and forms of development. This article explores one of these trends, namely the use of react on the frontend and nodejs on the backend. In terms of the data flow between the frontend and the backend, the GraphQL specification was used. The database is supported by Postgresql. The architecture and organization of development is crucial to find generic baseline sequences that support cross-cutting development between different information systems. This was the focus of the development in order to easily change the data structure and reflect this change in the frontend, without the need for maintenance in the code. This is a key step to allow the next stage, namely the development supported by advanced techniques based on artificial intelligence (this stage is under development and will be described in a next article by the authors).
On the Diversity of Self-tracking Purposes: Systematizing the Objectives in Tracking Oneself

With the ongoing spread of smart devices, self-tracking increasingly finds its way into everyday use. Even a community has formed around it, called the “Quantified Selfers”. A pivotal question to further develop appropriate tools tailored to people’s needs in the future is: For what purposes do people try to systematically track their data digitally? This question is fundamental as different goals of tracking might lead to different needs and requirements, e.g. in terms of the collected data or the provided data analyses. Since knowledge regarding the purposes of self-tracking is scattered among various works, we provide a consolidated overview through a systematic literature review. In contrast to frequently mentioned health-related purposes, there are a lot less apparent purposes that are connected, for example, to a community, awareness, or life experiences. We present our systematization of the purposes as frame for consideration in future developments and research.
Gender Identification from Bangla Name Using Machine Learning and Deep Learning Algorithms

The names of people have a large significance in various types of computing applications. In general, people’s names usually have a potential distinction between genders. Detecting genders from names with higher accuracy could be very challenging for Bangla and English character-based Bangla names. In this article, we showed the machine learning and deep learning-based characterization system which can recognize sexual orientations from the Bangladeshi people’s names. The Bangla character-based name placed with an exceptionally higher exactness of 91% and for the English name, it was 89%. We likewise consolidated diverse machine learning and deep learning classifiers techniques like random forest, SVM, Naive Bayes, impact learning, CNN, LSTM, etc., to break down which calculations give better outcomes. Besides, a Python pre-trained model on gender identification by Bangla’s name has been revealed.