Reviewerly/exHarmony huggingface dataset:
An Empirical Study on the Antecedents and Consequences of Thriving at Work: Connectivity, Job Autonomy, and Working-life Satisfaction

Thriving at work is a joint psychological sense of vitality and learning at work, which communicates a feeling of progress or forward movement in one’s self development. Since the construct and conceptual model of thriving were first raised many years ago, considerable researches have been made to investigate the antecedents and consequences of thriving at work. The primary goal of this study is also to examine the antecedent and consequential factors of thriving at work, but which were rarely inquired up to now. We considered connectivity and job autonomy as the antecedents of thriving at work, and examined it’s consequential effect on employees’ working-life satisfaction. And we explored these relationships not only in the level of the construct of thriving, but also in the level of two components of the construct, that is vitality and learning. For this research, we conducted a survey on employees working in Korean National Park Service(KNPS) on two separate points in time, with a lag of three weeks. The results of analysis indicated that connectivity and job autonomy of employees were positively related to their thriving at work. And thriving at work had positive impact on their working-life satisfaction. Moreover, as expected in the hypothesis, the mediating role of thriving at work was observed in the relationships between the antecedent and outcome variables. Somewhat different mediating paths, however, were observed depending on the two components of thriving, vitality and learning. The managerial implications of these findings and future research directions were then discussed.
Image Encryption Based on Multi-Level Keys on RC5 Algorithm

In recent years, the need to develop encryption algorithms has led to an increase in the working and efficiency of algorithms to protect the transmission and reception of information from any security breach. The RC5 type encryption algorithm is the most common and closest to perfection and symmetry algorithms, knowing that it faces many problems in which data collection was limited because it occurs only twice by working on its also, the algorithm is used for only 1 function (XOR) through the encryption process. A research report on digital image development by developing the RC5 algorithm makes that algorithm more secure by adding a new security level (using two keys) and thus increasing the key space. The encryption and decryption process can be done by substituting the XOR operation applied to Sixteen rounds of the algorithm with the new operation (#) based on the use of 2 keys, each of it consisting of 4 states (0,1,2,3) instead of using the traditional key that uses two states (0,1). This development of the RC5 algorithm increases the security and robustness of the hacking methods.
Design of Portable Wireless Fall Monitoring and Warning System

Aiming at preventing the old man falling down in the field of study, this article studies the status quo, focusing on sitting down research and analyze the behavior characteristic, this paper proposes a portable wireless fall monitoring and alarm system scheme design and manufacture, the conventional time-domain motion data multilevel threshold detection algorithm combined with combined with frequency domain characteristics of machine learning classification has realized the real-time identification of fall behavior. The time domain data used rectangular window filter to preserve the critical time series of behavior occurrence, fully considering the time characteristics of data changes, and completed the detection of lying posture and falling and the exclusion of daily low-intensity activities of the elderly. In the frequency domain, Discrete Fourier Transform (DFT) was applied to the combined acceleration data of sitting posture fall and moderate intensity activity, and the Support Vector Machine (SVM) classifier model was established by using frequency domain features. Complete the recognition and detection of sitting posture fall, and complement the detection of lying posture fall. After the decision of falling is made, the information of falling will be sent to the guardian of the elderly in time through wireless communication module, and the on-site alarm will be made at the same time, so that the elderly can get help from others in time when there are people nearby.
An automatic detection of microcalcification in mammogram images using neuro-fuzzy classifier

Breast cancer is a standout amongst the most widely recognised diseases and has a high rate of mortality around the world, significantly risking the health of the females. The presence of microcalcifications (MCs) is an imperative sign of early breast cancer. This study proposes an automatic technique for detecting the microcalcifications in mammogram images. The images are filtered using anisotropic diffusion filter, segmented using the technique based on cellular automata and finally classified into benign, malignant and normal using a neuro-fuzzy classifier. For extensive experimental analysis, mini-MIAS database is considered with sensitivity, specificity and accuracy as evaluation parameters. From qualitative and quantitative results, it is evident that the proposed classification method has achieved significant and improved performance compared to existing state-of-the-art classification technique like SVM, ANN, etc.
Swarm optimisation-based bag of visual words model for content-based X-ray scan retrieval

Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images. In this paper, we present a MedIR approach based on the bag of visual words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach.
Web Usage Mining Cluster Simulation Using K-Means and Fuzzy Approach

The Internet continues to develop at an outstanding rate in both the scale and complexity of Web sites and is properly on its way to being the principal source of information and data. Due to this growth in scale and complexity of Internet, Internet site publishers are having so much trouble in appealing and associating Web users. To layout popular and appealing Web sites, publishers need to apprehend their customers’ wishes. Consequently, inspecting users’ behavior is a crucial part of Web site layout. Web usage mining (WUM) is the utility of mining methods to proxy server log repositories so as to determine the utilization patterns which need to investigate the Web user’s surfing behavior. Clustering method is grouping a set of data points into several clusters. Clustering keeps related data points within the same cluster and keeps unrelated data points into distinct clusters. Paper presents comparative study of K-Means and Fuzzy C-Means methods. These methods offer adequate and precise investigation of proxy server log. The least distance between clusters is calculated with the aid of using the Euclidean distance equation.
Real-time object detector for the visually impaired with voice feedback using openCV

The goal of this paper is to create an object detector model that can detect objects for visually impaired people and other commercial users by detecting it at a certain distance. Existing object detection algorithms required a huge amount of training data, which took longer and was extremely complex. This is also a difficult task. As a result, it presents a computer vision paradigm for converting an object to text by importing a pre-trained CAFFEMODEL (a machine learning model created by Caffe) framework dataset model, and the texts are further converted to speech. This method allows the detection of multiple objects on the same screen. It helps in real-time object detection. This paper discusses the concept, methodology, and system architecture for the implementation of the system in combination with the obtained intermediate results and analyzes the tools used in the proposed system. This system can then be implemented in any other system. Portable gadgets that detect objects at a certain distance from visually impaired people and transmit a voice signal.
Profiling the Instructional Designer: Towards a Systematization of the Profession

In recent years, particularly after the COVID-19 pandemic, the need to rethink learning experiences has become evident. Instructional design is a field that can contribute significantly to developing new ways of learning in digital environments; however, as a profession within the European context, it has not been regulated, nor the requirements for the practitioners of
Effects of Social Media Features on Music Teaching and Learning During the COVID-19 Movement Control Order (MCO)

The COVID-19 pandemic has caused many education organisations to make full use of various online communication platform technologies to continue teaching. This study aims at gaining a deeper understanding of the usefulness of an e-learning platform for teaching and learning activities. It focuses on the trends of social media usage as well as a student’s
Leukocyte Classification Using Multimodal Architecture Enhanced by Knowledge Distillation

Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single network only; 2) We further enhance the learning capability of the independent subnetworks by distilling knowledge from high complexity independent teacher networks. With this, our proposed framework can achieve a high performance while maintaining low complexity for a multimodal dataset. Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
Geological Structural and Tectonic Features of the Chatkal-Kuramin Region According to Earth Remote Sensing Data

The article presents the results of geological research based on remote sensing of the Earth and interpretations of modern data of space surveys of the Earth's surface obtained from various satellite systems. Modern computer programs processed the data of satellite images, for define mineralized zones. The results of decoding satellite images and the characteristics of the results of the research. Remote sensing of the Earth materials in the form of aerial photographs has been widely used for a long time in regional geological surveys, structural and geological research, mineral exploration, hydrogeology, engineering geology, and many other areas of fundamental and applied geology.
KRİPTO PARA PİYASASI İÇİN ALIM SATIM BOTU ÖNERİSİ

Blockchain technology opens up new business opportunities, and the market capitalization of the industries it connects has been growing quickly in recent years. As cryptocurrencies gain popularity, trading them are growing in importance. Due to the market's great volatility, trading carries a very high risk, and many new traders face financial losses. Furthermore, seasoned traders are unable to work nonstop and thus, lose out on some trading opportunities. The aim of this study is to design a trading bot for the cryptocurrency market. there are currently a lot of trading bots in use on the crypto exchange markets. They never pass up a trading opportunity that comes around in a day. Both new and seasoned traders can use automated trading bots, depending on their needs. However, most of these bots are not free to use and their free features are very limited. This study aims to fill this gap by designing a free trading bot for the cryptocurrency market. MACD and RSI indicators will be used while designing the trading bot. As a result, a simple bot which both new and seasoned traders can easily used will be available. Keywords: cryptocurrency market, trading bot, blockchain, system development
Bridging the Digital Divide with Internet Connected, Solar Powered, Community Built Hello Hubs

Our paradigm busting solution uses the power of community and the power of connectivity to provide an inclusive, effective and equitable solution to the education gap and the digital divide that is affordable and scalable, with the potential to reach every child. In 2022 it is time we saw the internet not as a luxury, but the means to overcome inequality. We have worked hard to create a poster which is both informative about our education technology innovation as well as challenging to the PCF10 audience. This will be an opportunity for our Ugandan engineer and technology lead David Mugerwa to present our work internationally. It is called the world wide web - let's make that true. // In Uganda and Nepal, we teach communities to build their own solar-powered, outdoor Internet kiosks. Each one is loaded with state of the art education software teaching literacy, numeracy and computer dexterity amongst other subjects so that underprivileged children and adults can educate themselves, communicate with others, and have a voice in the global community. We call them Hello Hubs. And we’re challenging traditional approaches to development - we do not build Hello Hubs - the community does. We show them how to construct, maintain and repair the hub for themselves. It’s a radical, self sustaining model that gives communities full say in how our work works for them.
Effect of guided reading strategies on the motivation of the students to increase reading skill of elementary level students in the subjects of English

The study investigated the Effect of guided reading strategies on the motivation of the students to increase reading skill of elementary level students in the subjects of English. The major objectives of the study were as follows: (i) to find out effectiveness of motivation before reading.(ii) To find out effectiveness of motivation during reading. (iii) to find out effectiveness of motivation after reading. This study was experimental in nature and the posttest only equivalent groups design was used. The population of the study constituted all the elementary school students studying English in grade three. A group of sixty students of grade three with equal reading difficulties was selected as the sample of the study through purposive sampling from Govt girl’s higher secondary school Attock oil refinery (AOC). The sample students were further divided into experimental and control groups through random sampling technique. Motivation to Read Survey scale” was used for the collection of the data on motivation. Chi Square was applied to measure Motivation to Read Survey Scale. They study concluded that guided reading strategies had positive effect on students motivation to read. It is recommended that curriculum developers be include guided reading strategies while designing for primary school children curriculum. This study is significant for curriculum developer, policy makers, teacher and students.
Counter Effect Rules Mining in Knowledge Graphs

Discovering causal relationships is the goal of many experiments in science. Such a relationship indicates that a variation in an attribute, i.e., the treatment, implies a variation, i.e., has an effect, on another attribute, i.e., the outcome. Mining causal relationships have been studied in a recent approach in Knowledge Graphs, where differential causal rules are mined. Such rules express an effect of a treatment on a subset of instances described by a graph pattern named strata. However, these rules can be difficult to interpret, especially when a treatment has different effects depending on the strata it is expressed on. This paper presents counter effect rules that can be discovered from differential causal rules to facilitate their interpretation. This representation allows to point out the strata that lead to opposite effects for the same treatment. Our experiment shows that counter effect rules can be discovered on a real dataset.
Quasi-Equivalent Concept Trade-Off in Ontology Design: Initial Considerations and Analyses

Abstract The problem of concept equivalence is often addressed within ontology alignment. A similar problem is however encountered in ontology design: the decision whether to express multiple semantically close informal concepts as one or more formal classes, for which we coin the term concept quasi-equivalence trade-off . We outline its formal framework as well as an initial set of decision-making criteria. We also tried to collect traces of the trade-off from two sources: the LOV vocabulary catalog and ontology design experts addressed through a questionnaire. Finally, we discuss possible modalities of a software support.
libharpia: a New Cryptographic Library for Brazilian Elections

The Research and Development Center for Communication Security (CEPESC) has a long partnership history with the Brazilian Superior Electoral Court to improve the security of the Brazilian election system. Among all the contributions from CEPESC, probably the most important is a cryptographic library used in some critical moments during the election. In an effort to improve transparency and auditability of the solution, we present the new cryptographic library developed at CEPESC, named libharpia. Its main design goal is to allow transparency and readability while substantially increasing security. One of the main advances is the use of post-quantum cryptography, implemented through secure hybrid protocols that mix current cryptographic standards (specifically elliptic curves) with new cryptographic primitives based on Lattices, believed to be secure against quantum computers.
Acceleration method of infrared image detail enhancement on FPGA

Among the many cockpit human-computer interaction systems of airborne, vehicle-mounted, and ship-borne platforms, the image detail enhancement technology can improve the ability of personnel to interpret infrared images, which has very important application requirements. The enhanced algorithm running on the embedded computing platform needs to have a higher processing speed and smaller time delay in meeting the needs of real-time interaction. The current infrared video sensor usually has a lower resolution, and the image detail enhancement algorithm still can achieve real-time processing performance on a homogeneous multi-core CPU processing platform. However, as the resolution of platform sensors continues to increase, image processing speed and time delay are difficult to meet application requirements. We propose a method for enhancing and accelerating infrared image details for FPGA platforms. By considering the FPGA's specific domain software and hardware computing architecture, the traditional bilateral filtering image details algorithm is accelerated using local cache and search table, so that real-time processing performance can still be achieved under the input image of the 4k resolution, while almost no additional memory access delay is added. While ensuring the processing effect, the algorithm greatly improves the processing and delay indicators to meet the application requirements of embedded equipment in specific fields such as the cockpit human-computer interaction system.
Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange

The particle swarm optimization (PSO) algorithm is easy to implement and can obtain high-quality solutions to optimization problems. It is widely applied to nonlinear and difficult problems such as aerodynamic optimization. However, to solve multi-modal problems, it easily falls into locally optimal values, showing that its robustness is poor. In order to improve the robustness of the PSO algorithm, an enhanced particle swarm optimization algorithm based on parallel exchange (EPSOBPE) is proposed. The algorithm enhances the optimization capability and its robustness through the parallel evolution of the cuckoo search algorithm (CSA), PSO population, hierarchical exchange operation and reinforcement learning strategy. Therefore, the algorithm has both the global search capability of the CSA and the local capability of the PSO algorithm, thus making the EPSOBPE very robust. Functional test results show that the EPSOBPE has stronger robustness and adaptability to different problems than other intelligent optimization algorithms. Moreover, the EPSOBPE is applied to the aerodynamic optimization design of the RAE2822 airfoil and the M6 wing. Compared with other algorithms, the EPSOBPE is more robust, and its optimization capability is better.
Online Feature Selection Using Sparse Gradient

Feature Selection (FS) is an important preprocessing step in data analytics. It is used to select a subset of the original feature set such that the selected subset does not affect the classification performance significantly. Its objective is to remove irrelevant and redundant features from the original dataset. FS can be done either in offline mode or in online mode. The basic assumption in the former mode is that the entire dataset has been available for the FS algorithm; and the FS algorithm takes multiple epochs to select optimal feature subset that gives good accuracy. In contrast, the FS algorithms in online mode take input data one instance at a time and accumulate knowledge by learning each one of them. In online mode each instance of the original dataset is considered as training and testing sample as well. The offline FS algorithms require long time periods, if the data to be processed is large such as Big data. Whereas online FS algorithms will take only one epoch to learn the entire data and can produce the results swiftly which is highly desirable in the case of Big data. This paper deals with the online FS problem and provides a novel Feature Selection algorithm which uses the Sparse Gradient method to build a sparse classifier. In this proposed method, an online classifier is built and maintained throughout the learning process and feature weights, which are limited to a particular boundary limit, are reduced in a step by step decrement process. This method creates sparsity in the classifier. Effectively, the built classifier is used to select optimal feature subset from the incoming data. As this method reduces the weights in the classifier in step by step manner, only those important features which have value higher than the boundary survive from this repeated decrement process. The resultant optimal feature subset is formed using these non-zero weighted features. Most significantly, this particular method can be used with any learning algorithm. To show its applicability with different learning algorithms, various online feature selection models have been built using Learning Vector Quantization, Radial Basis Function Networks and Adaptive Resonance Theory MAP. In all these models, the proposed Sparse Gradient method is used. The encouraging results shows the effectiveness of the proposed method with different learning algorithms in medium and large sized benchmark datasets.