Reviewerly/exHarmony huggingface dataset:
Harmonic Mean based Classification of Images using Weighted Nearest Neighbor for Tagging

On image sharing websites, the images are associated with the tags.These tags play a very important role in an image retrieval system.So, it is necessary to recommend accurate tags for the images.Also, it is very important to design and develop an effective classifier that classifies images into various sematic categories which is the necessary step towards tag recommendation for the images.The performance of existing tag recommendation based on k nearest neighbor methods can be affected due to the number of k neighbors, distance measures, majority voting irrespective of the class and outlier present in the k-neighbors.To increase the accuracy of the classification and to overcome the issues in existing k nearest neighbor methods, the Harmonic Mean based Weighted Nearest Neighbor (HM-WNN) classifier is proposed for the classification of images.Given an input image, the HM-WNN determines k nearest neighbors from each category for color and texture features separately over the entire training set.The weights are assigned to the closest neighbor from each category so that reliable neighbors contribute more to the accuracy of classification.Finally, the categorical harmonic means of k nearest neighbors are determined and classify an input image into the category with a minimum mean.The experimentation is done on a self-generated dataset.The result shows that the HM-WNN gives 88.01%accuracy in comparison with existing k-nearest neighbor methods.
Hybrid PSO-ANFIS for Speaker Recognition

This paper introduces an evolutionary approach for training the adaptive network-based fuzzy inference system (ANFIS). The previous works are based on gradient descendent (GD); this algorithm converges very slowly and gets stuck down at bad local minima. This study applies one of the swarm intelligent branches, named particle swarm optimization (PSO), where the premise parameters of the rules are optimized by a PSO, and the conclusion part is optimized by least-squares estimation (LSE). The hybrid PSO-ANFIS model is performed for speaker recognition on CHAINS speech dataset. The results obtained by the hybrid model showed an improvement on the accuracy compared to similar ANFIS based on gradient descendent optimization.
Where lol Is: Function and Position of lol Used as a Discourse Marker in YouTube Comments

Lol is probably one of the most popular words in computer-mediated communication.It is generally taken to be the acronym of "laughing out loud", but it is not always used to indicate a humorous response; rather, it is multifunctional.Drawing on previous studies of the different functions of lol, this paper explores a possible correlation between the position and function of non-lexicalized lol in the specific context of YouTube comments.The hypothesis is that the function of lol largely depends on its position: clause-initial lol is not used with the same functions as clause-final lol.The data for the study come from the comment threads of three popular YouTube videos posted in 2017, 2018 and 2019 on the channel Miranda Sings, a channel posting humorous videos, which has a very wide audience and 10 million subscribers.The complete comment threads total 20,287 comments and 886 distinct occurrences of non-lexicalized lol.The analysis of the occurrences is both quantitative, aimed at determining the proportions of each use and position, and qualitative.Using the tools of discourse analysis and pragmatics, the study examines the functions of lol on two levels: the level of discourse organization and the level of social interaction.
Decentralized Business Process Control using Blockchain

Many applications supported by blockchain technology are emerging in the industry. Blockchain can be used to enforce the correctness of business process execution and, consequently, increasing the trust among the stakeholders involved in complex business processes. Since few experiences in the area have been reported, more are needed to better understand the benefits and pitfalls of blockchain-based solutions for decentralized business process control. This paper is an experience report of two projects that enforce decentralized business process control using blockchain - a food supply chain and a car registration - using a Design Science Research Methodology approach. Each application follows a different business process design - DEMO and BPMN - leading to different implementations based on Hyperledger Fabric. The major lessons learned are related to the appropriateness of DEMO and BPMN as business process modelling languages in the context of blockchain applications.
Security Issues in Near Field Communications (NFC)

Near Field Communications (NFC) is a rising technology that enables two devices that are within close proximity to quickly establish wireless contactless communications.It looks intuitively secure enough and various applications like ticketing, mobile payments, access grant etc. are taking advantage of NFC and flooding into the market in recent years.However, is it worth to trust such applications at the risk of leaking the user's private information?This paper surveys NFC vulnerabilities and exploits different kinds of security attacks.Upon surveying related materials, the paper covered possible solutions that could defend against those security threats.Furthermore, attacks and countermeasures evaluation in terms of practicality and cost have been further investigated.
COMMISSIVE SPEECH ACT IN DONALD TRUMP’S SPEECH CAMPAIGN

Commissive speech acts focus to carry out the speaker toward particular action in the future. The speech of Donald Trump in campaign rally at Nevada has function to convey his vision and mission as candidate president in second time. This research purposes to find the types of commissive speech act and understand the functions of commissive speech act in Donald Trump’s speech campaign in Henderson, Nevada on 13 September 2020. The orientation in this research refer to utterance of Donald trump, The data source of this research take from video’s Fox Business in YouTube. This research used descriptive qualitative, with technique of collecting data are observation, watching and take note. The result of this research, there are 28 commissive speech acts in different functions. The result indicates 8 types commisive speech act by utterance of Donald Trump, there are promise, threaten, swear, guarantee, warrant, refuse, assure and vow.
Proposed De-noising Algorithm

minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).
Recent Progress of Blockchain Initiatives in Government

Blockchain is a decentralized and distributed ledger technology that aims to ensure transparency, data security, and integrity.There are rising interest and investment by the governments and industries in Blockchain to deliver significant cost savings and increase efficiency.Identifying Blockchain initiatives that are currently implemented in the government world-wide could improve understanding as well as set benchmarks for specific countries.However, although some review studies on Blockchain initiatives have been carried out, there are very few studies that uncover Blockchain initiatives implemented by the government in Asian countries.Hence, this study reviews Blockchain initiatives in the five-top e-government development index (EGDI) countries in Asia; South Korea, Singapore, Japan, United Arab Emirates, and Cyprus.We strategized our review methods by utilizing relevant keyword searches in existing literature, books, academic journals, conferences, and industrial reports.The results of this study will help other researchers and practitioners to recognize the current stage of Blockchain initiatives in the government of Asian countries.
The Contribution of Absolute Income Towards Poverty Reduction in Sub-Saharan Africa: The Case of Rwanda

The study portrays current worldwide empirical facts on the progress of economic growth and poverty relief in developing countries of the world. It’s well known that in most developing countries, the lives of citizens are not always very pleasant. There are usually lots of inconveniences in the ways of life for the citizen of these countries. Whenever the government of such a country responds to the inconveniences of life by formulating developmental programs to alleviate the problems of the people, such programs in most cases, do not work, making such government to be frustrated and discouraged. However, the government of Rwanda plays an important role in a number of areas including agriculture, education, and the health sector in order to reduce the mortality rate to its citizens and promote entrepreneurship. The aim of this study is to evaluate the level of absolute poverty and the appropriate methods that Rwanda has applied to curb down poverty levels, at the same time, picking key lessons and guidelines that can be of assistance in curbing down poverty in parts of sub-Saharan countries, Rwanda inclusive. In order to achieve its aim, the paper was put into four sections. Section one explains the poverty profile of Rwanda before and immediately after the genocide while the second part describes the short strategies that Rwanda took to reduce poverty. Section three elaborates the work the country puts to ensure it achieves its goals of achieving long-term goals of becoming a middle-income country by 2020. The fourth section describes the environment that the state has created to ensure that its strategies work as planned. The research work also relies on other related secondary documents from the mentioned institutions.
MyDigitalFootprint: An extensive context dataset for pervasive computing applications at the edge

The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapting behavior based on rapidly changing user context. The ability of mobile devices to process this data locally is crucial for quick adaptation. This can be achieved through a single elaboration process integrated into user applications or a middleware platform for context processing. However, the lack of public datasets considering user context complexity in the mobile environment hinders research progress. We introduce MyDigitalFootprint, a large-scale dataset comprising smartphone sensor data, physical proximity information, and Online Social Networks interactions. This dataset supports multimodal context recognition and social relationship modeling. It spans two months of measurements from 31 volunteer users in their natural environment, allowing for unrestricted behavior. Existing public datasets focus on limited context data for specific applications, while ours offers comprehensive information on the user context in the mobile environment. To demonstrate the dataset's effectiveness, we present three context-aware applications utilizing various machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) daily-life activity recognition using smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. Our dataset, with its heterogeneity of information, serves as a valuable resource to validate new research in mobile and edge computing.
Examining a Multisemiotic Approach to Measuring Challenging Content for English Learners and Others: Results from the ONPAR Elementary and Middle School Science Study

This study contributes to the empirical research base on the effectiveness of ONPAR, a promising multisemiotic test item development process. ONPAR uses a variety of multisemiotic performance techniques to present and measure challenging concepts and skills of students, including low English proficient English Learners (ELs) and non-ELs. Experimental trials were used to investigate how 648 ELs at three English proficiency levels and native English speaking non-ELs performed on randomly assigned 4th and 8th grade traditional items and equivalent ONPAR items of challenging science content. General linear modeling using a covariate variable of classroom performance and bi- and multi-nomial regressions found differential boost across both grades. That is, findings showed that lower English proficient ELs perform better on ONPAR vs traditional forms in both grades, with p &lt; .05 in favor of ONPAR in grade 8, whereas there were no significant differences between the two forms for non-ELs. The results also underscore the viability of the assessment methodology where students often demonstrate their response by showing their knowledge and skills. Item level results indicate that the ONPAR approach is useful at mitigating the effect of group.
Aplikasi Augmented Reality (AR) dengan Metode Marker Based sebagai Media Pengenalan Hewan Darat pada Anak Usia Dini menggunakan Algoritma Fast Corner Detection (FCD)

Education in children is not only learning from books, with the development of educational technology it can be done using technological equipment such as computers, smartphones, or gadgets. The use of the Augmented reality application can be used as an example in learning in early childhood by introducing land animals in 3D. This study aims to provide education, especially to children, about the introduction of land animals by utilizing augmented reality technology. This research uses a marker-based tracking method and a fast corner detection algorithm. The results of the study resulted in an application that contained the introduction of land animals consisting of dogs, horses, kangaroos, cats, wolves, elephants, deer, camels, giraffes, and cows. Brief information about each animal, designed to be as attractive as possible for children. Applications can run on the Android operating system.Keywords:Augmented reality, 3D Objects, Marker Based, Fast corner detection.
Understanding and Exploiting Dependent Variables with Deep Metric Learning

Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations where the salient features in arbitrary classification problems vary over time or due to changing underlying variables. Examples of such variable features include seasonal and time-of-day variations in outdoor scenes in place recognition tasks for autonomous navigation and age/gender variations in human/animal subjects in classification tasks for medical/ethological studies. Through the use of visualisation tools for observing the distribution of DML representations per each query variable for which prior information is available, the influence of each variable on the classification task may be better understood. Based on these relationships, prior information on these salient background variables may be exploited at the inference stage of the DML approach by using a clustering algorithm to improve classification performance. This research proposes such a methodology establishing the saliency of query background variables and formulating clustering algorithms for better separating latent-space representations at run-time. The paper also discusses online management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings in the DML approach. We also discuss latent works towards understanding the relevance of underlying/multiple variables with DML.
Research on weather classification pattern recognition based on support vector machine

weather is the most important factor affecting the photovoltaic power generation.In this paper, the irradiance data of a photovoltaic power station in crodora in 2020 are collected, and the daily out of ground irradiance and the measured irradiance curve of that day are compared and observed, then the weather of that year is classified by human work, and then the daily irradiance data records are counted for the relevant indicators, with the maximum third order Based on the attributes of difference value, discrete difference and normalized variance, it is unified with the classified weather type.Then, the SVM prediction model of weather category is established based on radial basis function, and the optimal model parameters are determined by cross validation, so that a large number of historical date weather categories can be classified and predicted.This is obviously different from the traditional prediction method based on linear statistical theory, and the results show that it has a good effect.
Generator pyramid for high-resolution image inpainting

Abstract Inpainting high-resolution images with large holes challenges existing deep learning-based image inpainting methods. We present a novel framework—PyramidFill for high-resolution image inpainting, which explicitly disentangles the task into two sub-tasks: content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolution image, and synthesize the textures of unknown regions in a higher-resolution image, progressively. Thus, our model consists of a pyramid of fully convolutional GANs, wherein the content GAN is responsible for completing contents in the lowest-resolution masked image, and each texture GAN is responsible for synthesizing textures in a higher-resolution image. Since completing contents and synthesizing textures demand different abilities from generators, we customize different architectures for the content GAN and texture GAN. Experiments on multiple datasets including CelebA-HQ, Places2 and a new natural scenery dataset (NSHQ) with different resolutions demonstrate that PyramidFill generates higher-quality inpainting results than the state-of-the-art methods.
Threshold benchmarking for feature ranking techniques

In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
How Does Explanatory Virtue Determine Probability Estimation?—Empirical Discussion on Effect of Instruction

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.
Measurement-device-independent quantum key distribution under K-distributed strong atmospheric turbulence

Free-space quantum key distribution (QKD) allows two distant parties to share secret keys with information-theoretic security, which can pave the way for satellite-ground quantum communication to set up a global network for sharing secret message. However, free-space channels in the presence of atmospheric turbulence are affected by losses and fluctuating transmissivity which further affect the quantum bit error rate and the secure key rate. To implement free-space QKD, it is indispensable to study the effect of atmospheric turbulence. Different models have been used to describe the probability distribution for channel transmission coefficient under atmospheric turbulence, including the log-normal distribution and K distribution. In this paper, we focus on free space measurement-device-independent quantum key distribution (MDI-QKD) under K-distributed strong atmospheric turbulence. The MDI-QKD can close all loopholes on detection and achieve a similar performance to QKD, relying on time-reversed version of entanglement-based QKD protocol. Threshold post-selection method is adopted to restrain detrimental effects of the atmospheric turbulence, which is based on the selection of the intervals with higher channel transmissivity. By combining the general MDI-QKD system model with this method, we present a framework for the optimal choice of threshold. Our simulation result shows that the optimal threshold is dependent on the turbulence intensity and expected channel loss. Furthermore, compared with the original MDI-QKD protocols, the proposed protocol with threshold post-selection method can acquire a considerable better performance in key rate, especially in regions of high turbulence and high loss. What is more, this is instructive to the building of a practical free-space MDI-QKD system with better performance.