Reviewerly/exHarmony huggingface dataset:
Image Analysis of Spatial Differentiation Characteristics of Rural Areas Based on GIS Statistical Analysis

In rural geographic studies, the topic of multi-functions of rural regions has been gaining growing interest. Geographic areas with a complicated arrangement of activities of society and nature and the regional landscape noticeably articulate spatial differentiations. The image analysis and classification study of the spatial differentiation characteristics and patterns of rural regions are the basis of efficient governance and arrangements of village space, which play leading roles in rural revitalization and new-type urbanization policy. With rapid urban–rural transformation, rural development faces challenges under the progressive drive of accurate urban–rural integration development. Therefore, this paper proposes a spatial differentiation model based on a sociophysical information system and geographic information system, which is used to study rural development planning and land classification. The data are taken from the dataset of ucsd for analyzing the rural geographical data. The gis is a computer-aided system for analyzing, acquiring, displaying, and storing rural geographic information. This article discusses several noteworthy features of rural settlement distribution using a gis-based information processing approach and image analysis.
Mineralization Regularities of the Bainiuchang Ag Polymetallic Deposit in Yunnan Province, China

The Bainiuchang Ag polymetallic deposit is located at the junction between the Cathaysia, Yangtze, China and Indosinian blocks. It has experienced many geological events, and records excellent conditions for multiple mineralization. In this paper, elemental correlation analysis, cluster analysis, factor analysis, a semivariogram of Zn/Pb values, mineralization distribution and trend surface analysis have been carried out based on the prospecting database and ore body model. Our results show that Ag–Pb–Zn were mineralized at moderate temperatures. Tin was mineralized at high temperatures, and Sn and Zn/Pb values are well correlated. The Zn/Pb values can be used for tracing the ore-forming fluid. The semivariogram revealed that the Zn/Pb values are moderately spatially dependent, with good mineralization continuity in the 100° and 10° directions. The spatial pattern of the elemental grade correlates with mineralization enrichment. The trend surface analysis shows that the Ag, Pb, Zn, and Cu mineralization is weak in the south and strong in the north of the deposit, and the Sn grades and Zn/Pb values are high in the south and low in the north. High-temperature Sn, medium-temperature Cu, and medium-temperature Ag–Pb–Zn mineralization have occurred in a south-to-north trend. Therefore, the source of the ore-forming fluid was in the southern part of the mining area. During the migration of the ore-forming fluid from south to north, different minerals were precipitated due to changes in the physicochemical environment. The spatial patterns of mineralization may provide a basis for studying the formation of the ore deposit, and can guide ore exploration and mining in the mine area and similar ore deposits elsewhere.
Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance

Abstract Fitness landscape analysis devotes to characterizing different properties of optimization problems, such as evolvability, sharpness, and neutrality. Although several landscape features have been proposed, only a few of them can be used in practice as predictors of algorithm performance. In this study, the keenness ( $$\textrm{KEE}_{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>KEE</mml:mtext> <mml:mi>s</mml:mi> </mml:msub> </mml:math> ) is proposed to characterize the sharpness of the fitness landscape for continuous optimization problems and predict the performance of the differential evolution algorithm. Specifically, a mirror simple random walk algorithm is designed to construct the relevance between the front and back search points in the sampling. The fitness value of each point is replaced by the specific integer. The values in the set of integers with the same circumstance are computed as the feature scalar using the cumulative calculation mechanism. The results of experimental studies in various functions demonstrate the superiority of $$\textrm{KEE}_{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>KEE</mml:mtext> <mml:mi>s</mml:mi> </mml:msub> </mml:math> in terms of accuracy, reliability, and coverage of samples. Moreover, $$\textrm{KEE}_{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>KEE</mml:mtext> <mml:mi>s</mml:mi> </mml:msub> </mml:math> has shown excellent practicability in the application of differential evolution algorithm performance prediction for continuous optimization problems. Thus, $$\textrm{KEE}_{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>KEE</mml:mtext> <mml:mi>s</mml:mi> </mml:msub> </mml:math> is a new landscape feature for fitness landscape analysis of continuous optimization problems and algorithm performance prediction within limited prior knowledge of the unknown problem.
Region selection in Markov random fields: Gaussian case

We consider the problem of model selection in Gaussian Markov fields in the sample deficient scenario. The benchmark information-theoretic results in the case of d-regular graphs require the number of samples to be at least proportional to the logarithm of the number of vertices to allow consistent graph recovery. When the number of samples is less than this amount, reliable recovery of all edges is impossible. In many applications, it is more important to learn the distribution of the edge (coupling) parameters over the network than the specific locations of the edges. Assuming that the entire graph can be partitioned into a number of spatial regions with similar edge parameters and reasonably regular boundaries, we develop new information-theoretic sample complexity bounds and show that a bounded number of samples can be sufficient to consistently recover these regions. Finally, we introduce and analyze an efficient region growing algorithm capable of recovering the regions with high accuracy. We show that it is consistent and demonstrate its performance benefits in synthetic simulations.
Quantum Binary Field Multiplication with Optimized Toffoli Depth and Extension to Quantum Inversion

The Shor's algorithm can find solutions to the discrete logarithm problem on binary elliptic curves in polynomial time. A major challenge in implementing Shor's algorithm is the overhead of representing and performing arithmetic on binary elliptic curves using quantum circuits. Multiplication of binary fields is one of the critical operations in the context of elliptic curve arithmetic, and it is especially costly in the quantum setting. Our goal in this paper is to optimize quantum multiplication in the binary field. In the past, efforts to optimize quantum multiplication have centred on reducing the Toffoli gate count or qubits required. However, despite the fact that circuit depth is an important metric for indicating the performance of a quantum circuit, previous studies have lacked sufficient consideration for reducing circuit depth. Our approach to optimizing quantum multiplication differs from previous work in that we aim at reducing the Toffoli depth and full depth. To optimize quantum multiplication, we adopt the Karatsuba multiplication method which is based on the divide-and-conquer approach. In summary, we present an optimized quantum multiplication that has a Toffoli depth of one. Additionally, the full depth of the quantum circuit is also reduced thanks to our Toffoli depth optimization strategy. To demonstrate the effectiveness of our proposed method, we evaluate its performance using various metrics such as the qubit count, quantum gates, and circuit depth, as well as the qubits-depth product. These metrics provide insight into the resource requirements and complexity of the method. Our work achieves the lowest Toffoli depth, full depth, and the best trade-off performance for quantum multiplication. Further, our multiplication is more effective when not used in stand-alone cases. We show this effectiveness by using our multiplication to the Itoh-Tsujii algorithm-based inversion of F(x8+x4+x3+x+1).
A Morpho-Pragmatic Study of Roman Alphabetic Initialisms (RAI s) on Chinese and Japanese Social Media

Abstract The use of Roman alphabetic initialisms ( RAI s) is common on both Chinese and Japanese social media. However, to date, few studies have examined the phenomenon at the morpho-pragmatic interface in adequate detail or depth; nor has any researcher addressed their formational and motivational similarities and differences between Chinese and Japanese RAI s. To fill these gaps, this study conducts a comparative analysis of the RAI s collected from Weibo (for Chinese data) and Instagram and Twitter (for Japanese data). The results show that while the RAI s on the Chinese and Japanese social media share some ways of formation and converge on some motivations, differences in both formation and motivations also exist between Chinese and Japanese RAI s. It is argued that the formational differences stem from language characteristics and the functional divergences may be attributed to the different societal-cultural backgrounds of Chinese and Japanese netizens. The study may not only expand the scope of initialism research in general, but also enrich our understanding of the effect of societal-cultural differences on their formation and function of use in Computer- Mediated Communication ( CMC ) in particular.
Uncovering the development of linguistic knowledge in lesser studied languages

There has recently been an increased interest in studying the language development of non-western languages. This is not new - it began in 1960's and continued into the 1980's and 1990's. The current renewed interest is much welcomed, and will benefit from many of the experimental methods and theoretical insights developed over the past decades.
Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism

Solar photovoltaic power generation has the characteristics of intermittence and randomness, which makes it a challenge to accurately predict solar power generation power, and it is difficult to achieve the desired effect. Therefore, by fully considering the relationship between power generation data and climate factors, a new prediction method is proposed based on sliced bidirectional long short term memory and the attention mechanism. The prediction results show that the presented model has higher accuracy than the common prediction models multi-layer perceptron, convolution neural network, long short term memory and bidirectional long short term memory. The presented sliced bidirectional cyclic network has high prediction accuracy by low root mean square error and mean absolute error of 1.999 and 1.159 respectively. The time cost is only 24.32% of that of long short term memory network and 13.76% of that of bidirectional long short term memory network.
Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

Alzheimer’s disease (AD) is a type of brain disease that affects a person’s ability to perform daily tasks. Modern neuroimaging techniques have made it possible to detect structural and functional changes in the brain that are linked to AD, and machine learning (ML)-based methods have been extensively developed to help physicians achieve fast and accurate imaging-based AD detection. One critical issue when deploying ML methods in clinical applications is the domain shift that exists between the training and test data, which may significantly attenuate a model’s performance. To resolve this issue, domain adaptation (DA) is needed to narrow the performance gap between data from domains with different distributions. The purpose of this review is to offer insight into the state of ML and DA research in the field of neuroimaging-based AD detection. The limitations of existing studies, as well as opportunities for future studies, are discussed with the hope that more investigations will be conducted in the future to optimize the clinical workflow for AD diagnosis and treatment.
Image retrieval effectiveness of Bing Images, Google Images and Yahoo Image Search in the scientific field of tourism and COVID-19

The year 2020 brought a big concern for the global community because of COVID-19, which affected every sector of society, and tourism is no exception. Researchers across the globe are publishing their studies related to different dimensions of tourism in the context of COVID-19, and images have formed an essential component of their research. In tourism, images related to COVID-19 can open new dimensions for scholars. The main aim of the research is to measure the retrieval effectiveness of three image search engines (ISEs), that is, Bing Images, Google Images and Yahoo Image Search, concerning images related to COVID-19 and tourism. The study attempts to identify the capability of the ISEs to retrieve the desired and actual images related to COVID-19 and tourism. The PubMed Central (PMC) Database was consulted to retrieve the desired images and develop a testbed. The advanced search feature of PMC Database was explored by typing the search terms ‘COVID-19’ and ‘Tourism’ using ‘AND’ operator to make the search more comprehensive. Both the terms were searched against the ‘Figure/Table’ caption to retrieve papers carrying images related to COVID-19 and tourism. Queries were executed across the select ISEs, that is, Bing Images, Google Images and Yahoo Image Search. Retrieved images were individually analysed against the original image from the articles to determine the Precision, Relative Recall, F-Measure and Fallout Ratio. The format of the images in JPG/JPEG, besides checking the original image rank in the retrieved lot, was also ascertained. Bing Images scores more in terms of Mean Precision, followed by Google Images and Yahoo Image Search. For the Relative Recall measure, Google Images scores high, followed by Bing Images and Yahoo Image Search, respectively. Regarding F-Measure and Fallout Ratio, Bing Images outperforms Google Images and Yahoo Image Search. In retrieving the sought format of JPG/JPEG, Google Images performs best, followed by Yahoo Image Search and Bing Images. Google Images produces the original image at the first rank on more than one occasion. In contrast, Bing Images retrieves the original image at the first rank in two instances. Yahoo Images performs poorly over this metric as it does not retrieve any original image at the first rank on any other instance. The study cannot be generalised as the scope is only limited to the images indexed by PMC. Furthermore, the retrieval effectiveness of only three ISEs is measured. The study is the first to measure the retrieval effectiveness of ISEs in retrieving images related to the COVID-19 pandemic and tourism. The study can be extended across other image-indexing databases pertinent to tourism studies, and the retrieval effectiveness of other ISEs can also be considered.
Organization of suspending the successful completement of homework for educators with special educational needs

Education performs the two most important civilizational functions of personality development: its spiritual, moral, artistic, cultural development and socialization, as well as an economic function - reproduction of qualified labor resources for public production. Intensification of the process of acquiring knowledge, requirements for quality and individualization of the process of obtaining education by different categories of students cause an urgent need to develop and implement a wide range of educational programs that allow everyone who wants to get exactly the education, at the time and in the place that seems most acceptable to him, regardless of gender, age, social background and abilities. Inclusive pedagogy is aimed at studying the role of education in this context, because one of the directions of humanization of the education system of Ukraine is the integration of children with special educational needs into the educational space.&#x0D; In addition, the result of successful mastery of the required level of knowledge largely depends on the completion of homework. The article reveals the peculiarities of the organization of support for the successful completion of homework for students with special educational needs, and provides recommendations for parents. The main areas of work related to the establishment of pedagogical interaction between assistants, teachers and parents have been revealed.&#x0D; The organization of support for the successful completion of homework consists in close interaction and cooperation of parents and teachers, in established contact and trust, involvement of parents in participation in developed individual development programs and individual educational plans, in the educational process, in particular in extracurricular activities; establishment of cooperation with specialists of psychological and pedagogical support; in interaction and exchange of experience, information with parents regarding their approaches in raising and teaching children with special educational needs.
Semantically Adaptive JND Modeling with Object-Wise Feature Characterization, Context Inhibition and Cross-Object Interaction

Performance bottlenecks in the optimization of JND modeling based on low-level manual visual feature metrics have emerged. High-level semantics bear a considerable impact on perceptual attention and subjective video quality, yet most existing JND models do not adequately account for this impact. This indicates that there is still much room and potential for performance optimization in semantic feature-based JND models. To address this status quo, this paper investigates the response of visual attention induced by heterogeneous semantic features with an eye on three aspects, i.e., object, context, and cross-object, to further improve the efficiency of JND models. On the object side, this paper first focuses on the main semantic features that affect visual attention, including semantic sensitivity, objective area and shape, and central bias. Following that, the coupling role of heterogeneous visual features with HVS perceptual properties are analyzed and quantified. Second, based on the reciprocity of objects and contexts, the contextual complexity is measured to gauge the inhibitory effect of contexts on visual attention. Third, cross-object interactions are dissected using the principle of bias competition, and a semantic attention model is constructed in conjunction with a model of attentional competition. Finally, to build an improved transform domain JND model, a weighting factor is used by fusing the semantic attention model with the basic spatial attention model. Extensive simulation results validate that the proposed JND profile is highly consistent with HVS and highly competitive among state-of-the-art models.
Trust evaluation of health websites by eliminating phishing websites and using similarity techniques

Summary Every user uses a search engine to find health information from websites. Content‐rich health websites are considered in our research as wrong information in these websites can threaten life. Search engines give a list of URLs related to their search keyword. Generally, the user follows the top websites displayed by the search engine. Newly constructed websites do not have ratings, hit counts, and reviews. The search engine does not display newly constructed websites in their top rank. In such a case, the newly constructed website with the same content as the website displayed at the top of the search engine loses the user's trust. Another problem is; the phishing website URLs are also displayed by the Google Search engine, which appear similar to the genuine websites. To solve the problem and enhance the trust of health websites which is not at the top of the search engine among users, we have proposed an approach that extracts all URLs based on the keyword. It identifies all legitimate URLs using a Machine Learning classifier. Address bar features, Domain name features, HTML, and JavaScript features were identified for the dataset of getting legitimate URLs. Three classifiers (Decision Tree, Random Forest, and Support Vector Machine) were trained and evaluated. Decision Tree has the highest training accuracy, 94.125, testing accuracy, 92.75, and precision score of 96.97. The cross‐validation score of all three models is almost 93. Therefore, Decision tree is used to identify legitimate websites. After getting the list of legitimate URLs, all the content of the legitimate website is extracted. A Semantic Similarity between top‐rank legitimate website content and legitimate websites is found using Natural language processing techniques. Then the websites are ranked based on similarity and the value of the trust is assigned from highly trustable to less trustable. We have compared and correlated our results with the Web of Trust, a reputation tool for trust analysis, and have achieved a positive correlation. Thus, our approach removes phishing websites and enhances the trust in other websites that are not at the top of the search engine.
The Effects of Different Data Visualisation Formats on News Recall and Comprehension

This study uses the cognitive Theory of Multimedia Learning as the theoretical foundation to explore the potential effects of different data visualisations on news recalling and comprehension. Using a quasi-experimental design, three data visualisations (bar chart, coloured map, and timeline) were examined on three different topics; Corona vaccines, conflict in Syria, and Ever Given ship aground. The results suggest that data visualisations can increase recall and comprehension, but this effect is inconsistent for all data visualisations. Also, recall and comprehension are not always correlated; an easy-to-remember visualisation is not necessarily easy to comprehend.
An ensemble learning model based on differentially private decision tree

Abstract Using differential privacy to provide privacy protection for classification algorithms has become a research hotspot in data mining. In this paper, we analyze the defects in the differentially private decision tree named Maxtree, and propose an improved model DPtree. DPtree can use the Fayyad theorem to process continuous features quickly, and can adjust privacy budget adaptively according to sample category distributions in leaf nodes. Moreover, to overcome the inevitable decline of classification ability of differentially private decision trees, we propose an ensemble learning model for DPtree, namely En-DPtree. In the voting process of En-DPtree, we propose a multi-population quantum genetic algorithm, and introduce immigration operators and elite groups to search the optimal weights for base classifiers. Experiments show that the performance of DPtree is better than Maxtree, and En-DPtree is always superior to other competitive algorithms.