Reviewerly/exHarmony huggingface dataset:
A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion

This paper adopts the method of multisource big data fusion to conduct an in-depth study and analysis of precision poverty alleviation and uses big data statistical analysis model to explore and analyze it. Combining the characteristics of big data itself and the development of precision poverty alleviation, it focuses on the exploration of big data and introduces the background, development status, and achieved results of poverty alleviation with typical cases, followed by the analysis of the problems in the process of big data precision poverty alleviation and the study of the improvement path of big data technology precision poverty alleviation. Through the comparative analysis of the simulation accuracy of three models, the results show that the random forest model has the lowest error rate, after which the importance degree of indicators is derived using the model. In addition, the empirical analysis of the preprocessed sample data for multidimensional identification of poor households yields the contribution rate of each dimensional indicator that leads to multidimensional poverty of farm households, establishing scientific judging criteria to accurately judge whether farm households are poor on the one hand and selecting accurate identification methods to achieve accurate identification of poor households on the other hand. The tenfold crossover method is used to verify the errors in the test sample set. When the number of classification trees is greater than 100, it will gradually increase. Therefore, it is most appropriate to select the number of trees as 100. The multidimensional accurate identification model of farm household poverty constructed in this paper has an accuracy rate of 90.26% for the identification of poor households. By analyzing the accuracy rate of model identification and the contribution rate of multidimensional indicators leading to the poverty of farm households at the same time, the poverty degree of farm households under each dimensional indicator is derived, to accurately identify the poor households and their poverty status. The results show that the multidimensional accurate identification model of farm household poverty has the accurate identification ability and application value in the identification problem of poor households, and through the implementation of the model algorithm, a good application environment of accurate identification of poverty is created, which provides technical support to help poverty alleviation work and improve the accuracy of identification of poor households.
Comparison of a palm-based biometric solution with a name-based identification system in rural Bangladesh

Unique identifiers are not universal in low- and middle-income countries. Biometric solutions have the potential to augment existing name-based searches used for identification in these settings. This paper describes a comparison of the searching accuracy of a palm-based biometric solution with a name-based database.To compare the identification of individuals between a palm-based biometric solution to a name-based District Health Information Software 2 (DHIS2) Android application, in a low-resource setting.The study was conducted in Chandpur district, Bangladesh. Trained data collectors enrolled 150 women of reproductive age into two android applications - i) a name-based DHIS2 application, and ii) a palm-based biometric solution - both run on tablets. One week after enrollment, a different research team member attempted to re-identify each enrolled woman using both systems. A single image or text-based name was used for searching at the time of re-identification. We interviewed data collectors at the end of the study.Significantly more women were successfully identified on the first attempt with a palm-based biometric application (84%) compared with the name-based DHIS2 application (61%). The proportion of identifications that required three or more attempts was similar between name-based (7%, CI 3.7-12.3) and palm-based biometric system (5%, CI: 1.9-9.4). However, the total number of attempts needed was significantly lower with the palm-based solution (mean 1.2 vs. 1.5, p < 0.001). In a group discussion, data collectors reported that the palm-based biometric identification system was both accurate and easy to use.A palm-based biometric identification system on mobile devices was found to be an easy-to-use and accurate technology for the unique identification of individuals compared to an existing name-based application. Our findings imply that palm-based biometrics on mobile devices may be the next step in establishing unique identifiers in remote and rural settings where they are currently absent.
Logics of Statements in Context-Category Independent Basics

Based on a formalization of open formulas as statements in context, the paper presents a freshly new and abstract view of logics and specification formalisms. Generalizing concepts like sets of generators in Group Theory, underlying graph of a sketch in Category Theory, sets of individual names in Description Logic and underlying graph-based structure of a software model in Software Engineering, we coin an abstract concept of context. We show how to define, in a category independent way, arbitrary first-order statements in arbitrary contexts. Examples of those statements are defining relations in Group Theory, commutative, limit and colimit diagrams in Category Theory, assertional axioms in Description Logic and constraints in Software Engineering. To validate the appropriateness of the newly proposed abstract framework, we prove that our category independent definitions and constructions give us a very broad spectrum of Institutions of Statements at hand. For any Institution of Statements, a specification (presentation) is given by a context together with a set of first-order statements in that context. Since many of our motivating examples are variants of sketches, we will simply use the term sketch for those specifications. We investigate exhaustively different kinds of arrows between sketches and their interrelations. To pave the way for a future development of category independent deduction calculi for sketches, we define arbitrary first-order sketch conditions and corresponding sketch constraints as a generalization of graph conditions and graph constraints, respectively. Sketch constraints are the crucial conceptual tool to describe and reason about the structure of sketches. We close the paper with some vital observations, insights and ideas related to future deduction calculi for sketches. Moreover, we outline that our universal method to define sketch constraints enables us to establish and to work with conceptual hierarchies of sketches.
Evaluation of the Information-based Teaching Design Abilities of Educators in Blended Teaching

Information-based teaching is a process where the information technologies are actively integrated with the curriculum system, teaching objectives and teaching content. In order to improve the information-based teaching design abilities of teachers, it is of practical significance to study the criteria and methods for evaluating their information-based teaching design abilities under the online and offline blended teaching model. Based on the visualized measurement results of the keyword co-occurrence network, this paper constructed an evaluation indicator system for the information-based teaching design abilities of teachers under the online and offline blended teaching model, and gave the detailed steps of fuzzy comprehensive evaluation. In order to identify the improvements in the information-based teaching design abilities of teachers, it established a neural network prediction model for the information-based teaching design ability set of teachers based on the given evaluation indicator sample time series. At last, the scientificity of the proposed evaluation indicator system was verified through a test, with the prediction results of the model given.
“Digital Currencies ” an unsecure and unclear economic window, need to regulate or to ban in India

In the never-ending evolving information technology era, this paper provides a review and insights of the significant contemplations that have been related with the market for digital currencies forms since their advancement as a monetary resource in 2009. Notwithstanding amazing value appreciation lately, computerized monetary forms or digital currencies have been exposed to allegations of estimating bubbles key to the trilemma that exists between regulatory oversight, the potential for illegal use through its obscurity inside a youthful immature trade framework, and infrastructural breaks affected by the development of digital guiltiness. Each impact the view of the job of advanced monetary standards as a trustworthy venture resource class and authentic of worth. In a limited ability to focus time, central banks have quickly increased their innovative work exertion on central bank digital currencies (CBDC’s). A developing group of financial examination illuminates these exercises, frequently zeroing in on the "saves for all" part of CBDC’s for retail use. In any case, CBDC’s ought to be considered in the full setting of the computerized economy and the centrality of information, which raises worries around contest, installment framework honesty and security. This paper gives a directed visit through the developing writing on CBDC’s on the microeconomic contemplations connected with functional structures, innovations and security, and the macroeconomic ramifications for the monetary framework, monetary dependability and financial arrangement.
Exploring Links between Online Activism and Real-World Events: A Case Study of the #FeesMustFall

The declaration of free education for the poor and working class by the South African government remains a key result of the two-year #FeesMustFall movement. The campaign was tainted by a few heinous incidents that sought to disrupt its objectives. This study examines the connections between online activism and real-world occurrences by undertaking a longitudinal sentiment analysis of textual conversations on the Twitter platform since no quantitative research has been documented in this regard, particularly on the opinions of social media users and their associated impacts. Between October 15, 2015, and April 10, 2017, 576,583 tweets with the hashtag campaign #FeesMustFall were collected and analyzed using the Valence Aware Dictionary Sentiment Reasoner (VADER). The change point analysis (CPA) method was used to detect various changes in the dataset, and the cumulative sum analysis (CUSUM) method was used to discover changes over time. Results revealed that this online activism sentiment reacted to and reflected real-life events. The sentiment expressed is triangulated with a perceived real-life negative event, which is the burning of the University of Johannesburg (UJ) hall and the library at the University of KwaZulu-Natal (UKZN), thus confirming how Internet activism influenced these real-world events during the #FeesMustFall campaign. The study makes a significant contribution because it is the first longitudinal examination of the #FeesMustFall campaign’s sentiment distribution and variations.
Real Estate Land Transaction System Using Blockchain

Abstract: Real Estate Management in India as well as in many parts of the world is a very inefficient and insecure process. Developing a secure system that not only accelerates the process of land registration but also makes it efficient and secure will be effective. Blockchain technology is one of the latest and secured technologies on the horizon and has evolved over the last 9-11 years. There is tremendous potential for usage of Blockchain technology in the land industry. This paper presents a blockchainpowered real estate management system that will impart transparency, efficiency, and security in Real Estate Management. The decentralized data storage application and its interactions with Ethereum Virtual Machine (EVM) are presented to point out the event of a sensible contract which will be used for blockchain smart contracts in real estate management. Further, a detailed design and interaction mechanism are highlighted for the estate owners and users as parties to a sensible contract. It will store all the transactions on a distributed blockchain which will be very secure and will not be prone to hacking. A list of functions for initiating, creating, modifying, or terminating a sensible contract is presented and this will help the user enjoy a more immersive, user-friendly, and visualized contracting process, whereas the owners and real estate agents can enjoy more business and sales. It is a practical solution to the real estate management problem in the real world. Keywords: Blockchain, Smart Contracts, Real Estate Management, Ethereum Virtual Machine, Transparent Contracting Process
Contextual Awareness Service of Internet of Things User Interaction Mode in Intelligent Environment

In order to reduce the difficulty of the Internet of Things intelligent environment system to provide users with layout services and optimize the system structure, this paper proposes to design a context-aware service system based on the user interaction mode of the Internet of Things. This article first collected a large amount of data through the literature survey method, which laid a sufficient theoretical foundation for the subsequent research; then, using smart sensors, smart interaction technology, and context awareness methods is employed to design and build a context-aware service system based on the user interaction modality of the Internet of Things; in order to test the performance and effect of the situational awareness system, take the elderly home care behavior as the research object, collect the 28-day home behavior of the elderly as sample data, and divide it into 7-day data, 14-day data, 21-day data, and 28-day data, by adopting rule pruning demand reasoning algorithm and evidence theory algorithm to reason about the service demand of the elderly. Finally, the experimental results show that the rule-pruning-based service demand reasoning algorithm combined with the evidence theory algorithm optimizes the system, which can better perceive the situation of the elderly living alone and can well reason about the elderly’s service demand, which is of great significance for solving the life and safety problems of the elderly.
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning. Our results show that Expert Q-learning is indeed useful and more resistant to the overestimation bias. The baseline Q-learning algorithm exhibits unstable and suboptimal behavior in non-deterministic settings, whereas Expert Q-learning demonstrates more robust performance with higher scores, illustrating that our algorithm is indeed suitable to integrate state values from expert examples into Q-learning.
Glass Refraction Distortion Object Detection via Abstract Features

Glass reflection and refraction lead to missing and distorted object feature data, affecting the accuracy of object detection. In order to solve the above problems, this paper proposed a glass refraction distortion object detection via abstract features. The number of parameters of the algorithm is reduced by introducing skip connections and expansion modules with different expansion rates. The abstract feature information of the object is extracted by binary cross-entropy loss. Meanwhile, the abstract feature distance between the object domain and source domain is reduced by a loss function, which improves the accuracy of object detection under glass interference. To verify the effectiveness of the algorithm in this paper, the GRI dataset is produced and made public on GitHub. The algorithm of this paper is compared with the current state-of-the-art Deep Face, VGG Face, TBE-CNN, DA-GAN, PEN-3D, LMZMPM, and the average detection accuracy of our algorithm is 92.57% at the highest, and the number of parameters is only 5.13 M.
Meeting Challenges of Modern Standard Arabic and Saudi Dialect Identification

Dialect identification is a prior requirement for learning lexical and morphological knowledge a language variation that can be beneficial for natural language processing (NLP) and potential AI downstream tasks. In this paper, we present the first work on sentence-level Modern Standard Arabic (MSA) and Saudi Dialect (SD) identification where we trained and tested three classifiers (Logistic regression, Multi-nominal Na¨ıve Bayes, and Support Vector Machine) on datasets collected from Saudi Twitter and automatically labeled as (MSA) or SD. The model for each configuration was built using two levels of language models, i.e., unigram and bi-gram, as feature sets for training the systems. The model reported high-accuracy performance using 10-fold cross- validations with average 98.98%. This model was evaluated on another unseen, manually-annotated dataset. The best performance of these classifiers was achieved by Multi-nominal Naïve Bayes, reporting 89%.
No-reference panoramic image quality assessment based on multi-region adjacent pixels correlation

The distortion measurement plays an important role in panoramic image processing. Most measurement algorithms judge the panoramic image quality by means of weighting the quality of the local areas. However, such a calculation fails to globally reflect the quality of the panoramic image. Therefore, the multi-region adjacent pixels correlation (MRAPC) is proposed as the efficient feature for no-reference panoramic images quality assessment in this paper. Specifically, from the perspective of the statistical characteristics, the differences of the adjacent pixels in panoramic image are proved to be highly related to the degree of distortion and independent of image content. Besides, the difference map has limited pixel value range, which can improve the efficiency of quality assessment. Based on these advantages, the proposed MRAPC feature collaborates with the support vector regression to globally predict the quality of panoramic images. Extensive experimental results show that the proposed no-reference panoramic image quality assessment algorithm achieves higher evaluation performance than the existing algorithms.
Cluster Analysis and Discriminant Analysis for Determining Post-Earthquake Road Recovery Patterns

The transport network in eastern Japan was severely damaged by the 2011 Tohoku earthquake. To understand the road recovery conditions after a large earthquake, a large amount of time is needed to collect information on the extent of the damage and road usage. In our previous study, we applied cluster analysis to analyze the data on driving vehicles in Fukushima prefecture to classify the road recovery conditions among municipalities within the first six months after the earthquake. However, the results of the cluster analysis and relevant factors affecting road recovery from that study were not validated. In this study, we proposed a framework for determining post-earthquake road recovery patterns and validated the cluster analysis results by using discriminant analysis and observing them on a map to identify their common characteristics. In addition, our analysis of objective data reflecting regional characteristics showed that the road recovery conditions were similar according to the topography and the importance of roads.
Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence

Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.
Hardware Implementation of Elliptic Curve Digital Signature Algorithm over GF(2409) Using SHA-3

This paper presents the hardware implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA) by considering the NIST recommended elliptic curve in binary fields GF(2 409 ) and the NIST recommended Secure Hash Algorithm-3 (SHA-3).In this case, four modules are implemented: modular arithmetic hardware, finite field arithmetic hardware, an elliptic curve cryptoprocessor over GF(2 409 ), and SHA-3 module.The implementation is described in VHDL language, synthesized on the Cyclone V 5CSEMA4U23C6N using Intel-Quartus II V 19.1, and verified using Signal Tap II Logic Analyzer.The synthesis and performance results show a good area-throughput trade-off, and it is suitable for high-performance cryptographic applications for embedded systems.