Reviewerly/exHarmony huggingface dataset:
Public Hospital Review on Map Service with Part of Speech Tagging and Biterm Topic Modeling

Health is a basic human need.Increasing standard of life influences the quality of health.It demands health service providers improve quality better service and provide satisfaction for consumers as health care users.Hospital as one of the means of health services is required to improve the quality of services.The study found that customers tend to choose private hospitals over public hospitals because of service factors.Public hospitals are gradually being abandoned.This is challenging for public hospitals to improve services.One of the scientific techniques to know the level of public satisfaction with health services, especially hospitals, is to examine the reviews from users.In this research, we use public mapping review as a data source.From the review, it can be known what topics are mostly discussed specifically for various ratings.A suitable model is required to find out the topics in the review that have a low rating so that it can be used as a suggestion for improving health services.Topic modeling is best achieved through the text mining method.The study proposed the use biterm topic model suitable for short text in the reviews of the map platform.Short text reviews are characterized by sparse data, the small number of words that appear as topic builders, and rare word contexts related to the topic.The result shows that biterm topic model can produce topics with an acceptable combination of accuracy and performance.The model with the addition of part-of-speech tagging on noun-only is unable to increase the accuracy of the model, compared to the one without the addition of part-of-speech tag.However, the addition of part-of-speech tagging on noun-only can improve the performance of the model.
Model of Watershed Segmentation in Deep Learning Method to Improve Identification of Cervical Cancer at Overlay Cells

Cervical cancer is a disease that is very scary for women because it is the cause of death among women. To be aware of this disease is to do an early examination through the Pap Smear (PS) test. In terms identifying overlapping cancer cells, it still has low accuracy. Therefore, this research was carried out with the aim of getting the level of cell separation with high accuracy. This study uses a model to develop the Watershed segmentation technique in the Deep Learning Method. The data tested in this study comes from the RepomedUNM dataset. The amount of data tested is 420 overlapping images with the formulation of 1,260 test images. The results of this study can very well separate each overlapping cell with an average Intersection over Union (IoU) score of 0.9061. Each result can be divided fully by the whole of its area, so the final results of overlapping cells were successfully separated with an average score of 0.945. Therefore, this research can be used as a reference in identifying cervical cancer cells.
From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization

Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available.
Approximate Kalman filtering by both M-robustified dynamic stochastic approximation and statistical linearization methods

Abstract The problem of designing a robustified Kalman filtering technique, insensitive to spiky observations, or outliers, contaminating the Gaussian observations has been presented in the paper. Firstly, a class of M-robustified dynamic stochastic approximation algorithms is derived by minimizing at each stage a specific time-varying M-robust performance index, that is, general for a family of algorithms to be considered. The gain matrix of a particular algorithm is calculated at each stage by minimizing an additional criterion of the approximate minimum variance type, with the aid of the statistical linearization method. By combining the proposed M-robust estimator with the one-stage optimal prediction, in the minimum mean-square error sense, a new statistically linearized M-robustified Kalman filtering technique has been derived. Two simple practical versions of the proposed M-robustified state estimator are derived by approximating the mean-square optimal statistical linearization coefficient with the fixed and the time-varying factors. The feasibility of the approaches has been analysed by the simulations, using a manoeuvring target radar tracking example, and the real data, related to an object video tracking using short-wave infrared camera.
The Effect Of The Use Of Crossword Puzzle Media On Students' Vocabulary Mastery

The kids' vocabulary prevented them from fully mastering the four pillars of English language proficiency. As a result, it was decided to investigate "The Effect of Crossword Media on Students' Vocabulary Acquisition and Oral Communication." To improve their English, students can look into ways to expand their vocabulary and practice public speaking. After giving some thought to a number of potential approaches to the classroom, the author settled on using crossword puzzles as a means of fostering student growth in these areas. The objectives of this study were to: (1) give a general overview of how crossword puzzles can aid in vocabulary development and retention; (2) offer suggestions for future research; and (3) describe how crossword puzzle-containing media influences students' capacity to learn new words. After information was gathered via a questionnaire, quantitative data analysis was done. A representative sample of SMPIT Bait Et-Tauhied students participated in the study. We sent a questionnaire with 20 distinct sets of statements to 30 students in order to conduct the study. The results demonstrate that a crossword puzzle significantly affects students' understanding and use of va because the validity test of the test with the calculated R value is greater than the R table, demonstrating the validity of the 20 questions completed by the respondents. Additionally, the reliability test using Croanbach alpha demonstrates very reliability, and the case processing summary demonstrates that the number of respondents when filling out the questionnaire is not lost or broken.
How have music emotions been described in Google books? Historical trends and corpus differences

Abstract Human records can assist us in understanding real descriptions and expected ideals of music. The present work examined how have music emotions been described in millions of Google books. In general, positive adjectives were more regularly used to describe music than negative adjectives, demonstrating a positivity bias in music. The emotional depiction of music has shifted over time, including a decrease in the frequency of emotional adjectives used in English books over the past two centuries, and a sudden surge in the usage of positive adjectives in simplified Chinese books during China’s Cultural Revolution. Negative adjectives were substantially less employed to describe music in simplified Chinese books than in English books, reflecting cultural differences. Finally, a comparison of different corpora showed that emotion-related adjectives were more frequently used to describe music in fictional literature.
Using Deep Learning Neural Networks to Enhance HDR Images

In recent days, photography becomesmain in our daily life style. Everyonelike to upload imageson facebook, instagram,or any social media site, or maybe want to photograph some personal images or for memory.In other hand, the development of technology, especiallyin cell phones, let these mobilesequipped with cameras with a specific resolution. We define cameras with two parameters. First oneis the width and the highof the image which can define the number of pixels inside the image, the bigger these pixels can be the accuracy and true scene can get. The second in the resolution of the pixel which means how many bits are used to define the colors of the image.All natural images havea wide dynamic range because of containinga large range ofintensity values. This high range cannotbe collected using non-HDR cameras. So, mobile cameras might give very bad resolutions.This search aims to use conventional neural networks to createan HDR images using a programmed manner so we can solve the problem of poor cameras problems, firstof all we will createa map for an image using adataset of static images hasa different light intensityfor the same scene (using differently exposed LDR images), and in the next task we will use the trained model to get the optimal tuningfor the image to result the right color space. Here we could select a group of main parameters which are the number of learning tasks, the learning strategies, sensor data used, number of input exposures
Investigating Mother Tongue Effect In The Acquisition Of English Prepositions By Indonesia-Speaking Learners

The process of learning prepositions gets complicated most notably for learners of English as foreign language (EFL) because they encounter challenges in grasping the manifold meanings and dissimilar contextual applications. This study is aimed at identifying the sources of the difficulty that Indonesia learners of English undergo in learning preposition. To this end, a multiple-choice test was designed to gather data regarding the problems of acquiring and learning English prepositions by UNIRA students of English. The test comprised of fifteen questions. Each of the students was supposed to choice the best possible answer out of the choices from a list with the correct English preposition. In this study, method research design used is qualitative. The results suggest that the students meet considerable difficulties in using English prepositions. The students highly depend on their mother tongue, i.e. Indonesia language, which poses a source of command that enables the students in producing inappropriateness of preposition use.
A trust, privacy and security model for e-commerce in Nigeria

Today, digital technologies has permeated every sector of human life from communication to medicine to education to transportation and most recently shopping.. The mode of buying and selling by Nigerians have moved from traditional to digital, whereby consumers buy and sell goods online. The online presence of commerce is known as Electronic commerce or E-commerce and it is an initiative amongst other emerging digital sectors in Nigeria that has witnessed substantive growth recently. However, the issues of security, privacy of data and the trust endeared by users on the initiative remains a challenging one. Presently in Nigeria, the solutions to combat security concerns of E-commerce are technically inclined and therefore do not sufficiently capture the solution paradigm. Due to a rise in these E-commerce‘s security-related concerns in Nigeria as discovered by taking random samples of the population used for the survey, this research proposes a Security, Trust and Privacy model for the survival of E-commerce in Nigeria.
A Framework for the Synthesis of X-Ray Security Inspection Images Based on Generative Adversarial Networks

Object detection of prohibited items in X-ray security inspection is challenging because of serious overlap, disorderly background, and high throughput.In the past few years, a variety of deep learning algorithms have been proposed and achieved satisfactory results.However, the performance of these algorithms relies heavily on the specified datasets.Moreover, establishing a large-scale X-ray image dataset by manually collecting and labeling images is prohibitively expensive and time consuming.In this paper, we propose a text-driven framework for synthesizing X-ray security inspection images based on Generative Adversarial Networks (GAN).First, a conditional GAN is developed to generate natural images of prohibited items from class labels.Second, an improved model based on a pix-to-pix GAN is implemented to convert natural images into X-ray images.Third, another HD pix-to-pixel GAN is responsible for producing high-resolution benign background images, which are subsequently fused with the generated images of prohibited items to create X-ray inspection images.Finally, the proposed method is evaluated using SOTA object detection algorithms, such as YOLO-v5, and achieving 4.6% promotion for mAP 0.5 and 15.9% promotion for mAP 0.5-0.95 .The experimental results demonstrate that our image synthesis framework can effectively augment the datasets of prohibited items and improve the detection performance of deep learning algorithms during X-ray security screening.
Implementing multi-session learning studies out of the lab: Tips and tricks using OpenSesame

Here, we provide tips and tricks for running multisession experiments out of the lab using OpenSesame, a user-friendly experimental tool that is open source and runs on Windows, Ma-cOS, and Linux.We focus on learning experiments that involve the measurement of reaction times.We show how such experiments can be run with traditional desktop-based experiment software on participants' own notebooks (i.e., out-of-the-lab, but not in a browser).Learning experiments pose specific challenges: accessing individual identifying numbers, accessing session numbers, and counterbalancing conditions across participants.This article includes helpful code and provides hands-on implementation tips that will be useful also beyond the presented use case.The aim of this article is to illustrate how to create multisession learning experiments even with little technical expertise.We conclude that, if done right, out-of-the-lab experiments are a valid alternative to traditional lab testing.
Cyber Threat Intelligence: Current Trends and Future Perspectives

Many organizations are continually put in danger by sophisticated and malevolent cyber attacks in today's rapidly developing threat landscape, which is evolving at a rapid rate. Cybercriminals, who are more skilled, more organized, and have more financial resources than in the past. Cyber Threat Intelligence, abbreviated as CTI, has emerged as a popular topic and is now being considered by many organizations as a potential solution to the growing number of cyber attacks. The purpose of this work is to conduct a literature review on the previous research that has been done on CTI. The most fundamental question about what CTI is investigated through the process of doing a literature study. This is done by contrasting several definitions in order to identify areas of agreement and disagreement. It has been discovered that neither the organization nor the suppliers have a comprehensive grasp of the information that is deemed to be CTI; hence, further study is required in order to define CTI. This article also listed existing CTI products and services, including as threat intelligence data feeds, threat intelligence standards, and tools that are being utilized in CTI. This study outlines four research issues in cyber threat intelligence and assesses contemporary work carried out in each of those areas. These difficulties were determined based on an assessment of the CTI definition, standards, and technologies. When an organization is inundated with a massive amount of threat data, the need for trained threat data analysts who are able to fully utilize CTI and transform the data into actionable information becomes more vital than it has ever been before. The problem of poor data quality is not a recent development; nonetheless, because to the increasing use of CTI, further study in this field is required.
Reducing Inequalities through Higher Education and Economic Growth. Gender Analysis by Educational Degrees: Bachelor's, Master's and Doctoral Degrees

Inequality is an economic and social problem, and its reduction is a concern in the recent decades. In this context, we decided to analyse the contribution of higher education, according to gender on its three levels (Bachelor`s degree, Master`s degree and Doctoral degree - or equivalent), and economic growth. Higher education and economic growth are factors of competitiveness, important in reducing inequalities in the EU. We applied the panel methodology to some indicators taken from the World Bank website for the period 2013-2018. Data validity narrowed the analysis to 19 European countries divided into two groups according to which we developed two models. Model 1 includes Austria, the Czech Republic, Poland, France, Greece, Slovakia and Slovenia, and Model 2 includes Belgium, Cyprus, Denmark, Finland, Germany, Hungary, Latvia, Lithuania, Malta, the Netherlands, Portugal and Sweden. The two groups differ not only in the number of countries but also in the period of analysis. For the first group, the period analysed is 2014-2017, and the applied model is Fixed Effects. For the second one, the analysed period is 2013-2018, and the applied model is Random Effect. The results show the importance of national characteristics in terms of the effect of higher education on reducing inequalities, despite efforts to standardise European practices. The increase in the number of higher education graduates highlight European inequalities, especially in the case of those with Bachelor's and Doctoral degrees. The results regarding the effect of the numerical increase of graduates with a Master's degree on inequalities are ambiguous. Equal access to education for women and men reduces inequalities, but the analysis captures the maintenance of women in a relatively vulnerable position in the European society and economy. Growth does not guarantee the absorption of inequalities because its transposition in the economy and society has antagonistic effects. There is a possibility to reduce inequalities due to higher growth rates, but this is also a possibility to increase them and to emphasize the differences between the rich and poor people and between the developed and developing countries.
Stochastic Qubit Resource Allocation for Quantum Cloud Computing

Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs.
Quantum Full-Duplex Communication

Integrating the full-duplex capability with quantum communication potentially equips emerging wireless networks with a quantum layer of security for the stringent communication efficiency and security requirements. This paper proposes two new full-duplex quantum communication protocols to exchange classical or quantum information between two remote parties simultaneously without transferring a physical particle over the quantum channel. The first protocol, called quantum duplex coding, enables the exchange of a classical bit using a preshared maximally entangled pair of qubits by means of counterfactual disentanglement. The second protocol, called quantum telexchanging, enables the exchange of an arbitrary unknown qubit without using preshared entanglement by means of counterfactual entanglement and disentanglement. We demonstrate that quantum duplex coding and quantum telexchanging can be achieved by exploiting counterfactual electron-photon interaction gates. It is shown that these tasks can be viewed as full-duplex transmission of bits and qubits via binary erasure channels and quantum erasure channels, respectively.
Federated Learning with Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. Firstly, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.