Reviewerly/exHarmony huggingface dataset:
Space-earth coupling with relation to earths thermal capacity: A case study for Nsukka Region, Nigeria

The radiation coming from the sun to the earth is the only form of incoming radiant energy that determines the heat balance and thermal regime of the earth.Variation of solar radiation is the single most important factor affecting the earth's surface temperature.This study seeks to investigate the relationship between ambient temperature and soil temperature with a view to determine the existence of space-earth coupling.With a view to achieving this aim, the research was narrowed to the correlation between air temperature and soil temperature for the Nsukka area of Enugu State in Nigeria.The data used in this study was a two-year data of air temperature and soil temperature measurements collected by the Nigerian Environmental Climactic Observing Program (NECOP) station situated at Nsukka, which carried out simultaneous basic measurements of meteorological and climatological variables, in real time, through telemetry technology, with 5-min update cycles.In order to visualize the relationship between the two variables under study, the soil and air temperatures were co-plotted with time for each month of the year for 2011 and 2012, daily and annual range using Microsoft Excel, OriginPro 2018 64-Bit and Python Programming.From the findings of this work, the air and soil temperature variation patterns over each day, month, and year depict a pattern of correlation.The soil temperature fluctuated alongside the variations observed in air temperature with the addition of time lags (response time) due to difference in their thermal conductivities and heat capacities.This time lag was observed from this work to be up to 2 to 3 h for Nsukka region.On cooling, a delay was also observed in the air temperature, as the air temperature cools about an hour after the reduction in soil temperature was observed in a daily cycle.
CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.
Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization

Industries such as banking, telecom and airlines - often have large software systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system.
Binary Count Tree: An Efficient and Compact Structure for Mining Rare and Frequent Itemsets

The discovery of rare and frequent itemsets is done efficiently if the datasets to be processed are stored within the main memory.In recent years, various data structures have been developed to represent a large dataset in a compact form, which otherwise cannot be stored as a whole within the main memory.Binary Count Tree (BIN-Tree), a tree data structure proposed in this paper, represents the entire dataset in a compact and complete form without any information loss.Each transaction is encoded and stored as a node in the tree, in contrast to the existing algorithms that store each item as a node.The efficiency of the BIN-Tree for datasets of varying size and dimensions was evaluated against Single Scan Pattern Tree (SSP-Tree) and Weighted Count Tree (WC-Tree).The results obtained revealed BIN-Tree to be 95% and 75% more space-efficient than SSP-Tree and WC-Tree, respectively.The BIN-Tree construction and discovery of itemsets from a large dataset were found to be 93% and 22% more time-efficient than SSP-Tree and WC-Tree, respectively.BIN-Tree is equally efficient to discover rare and frequent itemsets from a small dataset in the main memory.
The Use of Artificial Intelligence in the Field of Electronic Commerce

The study aimed to identify the use of artificial intelligence applications, and their use to classify and analyze data, add a personal character to the way goods and services are presented in proportion to the users’ desires, in addition to extracting and analyzing user reactions about goods and services, carrying out the inventory and planning process, and others. The study also shed light on the advantages of using artificial intelligence applications, including the possibility of dispensing with routine work, obtaining information and results for analysis in record time, and helping to make marketing and strategic decisions.
Online Turkish Handwriting Recognition Using Synthetic Data

We present a recognition system for online Turkish handwriting trained with synthetically generated data and transfer learning.Training deep networks requires large amounts of data.However, a sufficiently large collection of Turkish handwriting samples is not available.Hence we synthesize data to do pretraining before adapting the system to target dataset by fine tuning.We generate words from isolated character collection of a large English handwriting dataset.Then, we train the system first with synthetic data and fine tune it with Turkish handwriting samples from a smaller dataset.Fine tuning increases the character recognition rate of the final system which is evaluated on 2,041 samples of isolated Turkish words from the initial value of 61% to 88%.Performance of the system on synthetic data is quite similar to that on the Turkish test data which shows that the synthetic data resembles the real data quite closely.According to these results, synthetic data generation can be a solution to the data scarcity problem of online Turkish handwriting.
Local or Global? A Comparative Study on Applications of Embedding Models for Information Retrieval

Application of embedding techniques for improving the performance of different text processing tasks, including information retrieval (IR) has been a much sought after research area for the last few years with decisive improvements over state-of-the-art. Apart from applying embedding techniques for different tasks, significant works have been conducted on setting the best practice. However, an optimal setting to apply embeddings is still an ongoing research area in the text processing community. In this study, we have compared ways of applying embeddings for downstream query expansion task for IR and have discussed some effective variations of locally selecting embedded vectors. Experimentation on TREC news and web corpora empirically validates the ascendancy of the variation over the benchmark settings.
A New Method to Estimate Peak Signal to Noise Ratio for Least Significant Bit Modification Audio Steganography

Audio steganography is implemented based on three main features: capacity, robustness, and imperceptibility, but simultaneously implementing them is still a challenge. Embedding data at the Least Significant Bit (LSB) of the audio sample is one of the most implemented audio steganography methods because the method will give high capacity and imperceptibility. However, LSB has the lowest robustness among all common methods in audio steganography. To cater to this problem, researchers increased the depth of the embedding level from fourth to sixth and eighth LSB level to improve its robustness feature. However, consequently, the imperceptibility feature, which is commonly measured by Peak Signal to Noise Ratio (PSNR), is reduced due to the trade-off between imperceptibility and robustness. Currently, the lack of study on the estimation of the PSNR for audio steganography has caused the early assessment of the imperceptibility-robustness trade-off difficult. Therefore, a method to estimate PSNR, known as PSNR Estimator (PE), is introduced to enable early evaluation of imperceptibility feature for each stego-file produced by the audio steganography, which is important for the utilisation of embedding. The proposed PE estimates the PSNR based on the pattern collected from the embedment at different levels. From the evaluation, the proposed method has 99.9% of accuracy in estimating PSNR values at different levels. In comparison with the Mazdak Method, the proposed method performs better in all situations. In conclusion, the proposed PE can be used as a reference for embedding and further reducing the calculation complexity in finding the feasible value to minimise the trade-off between robustness and imperceptibility.
A neural network for a generalized vertical complementarity problem

&lt;abstract&gt;&lt;p&gt;In this paper, an efficient artificial neural network is proposed for solving a generalized vertical complementarity problem. Based on the properties of log-exponential function, the generalized vertical complementarity problem is reformulated in terms of the unconstrained minimization problem. The existence and the convergence of the trajectory of the neural network are addressed in detail. In addition, it is also proved that if the neural network problem has an equilibrium point under some initial condition, the equilibrium point is asymptotically stable or exponentially stable under certain conditions. At the end of this paper, the simulation results for the generalized bimatrix game are illustrated to show the efficiency of the neural network.&lt;/p&gt;&lt;/abstract&gt;
On subset-resilient hash function families

Abstract In this paper, we analyze the security of subset-resilient hash function families, which is first proposed as a requirement of a hash-based signature scheme called HORS. Let $${\mathcal {H}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>H</mml:mi> </mml:math> be a family of functions mapping an element to a subset of size at most k . ( r , k )-subset resilience guarantees that given a random function H from $${\mathcal {H}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>H</mml:mi> </mml:math> , it is hard to find an $$(r+1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>r</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -tuple $$(x,x_1,\ldots ,x_r)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>r</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> such that (1) H ( x ) is covered by the union of $$H(x_i)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and (2) x is not equal to any $$x_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> . Subset resilience and its variants are related to nearly all existing stateless hash-based signature schemes, but the power of this security notion is lacking in research. We present three results on subset resilience. First, we show a generic quantum attack against subset resilience, whose time complexity is smaller than simply implementing Grover’s search. Second, we show that subset-resilient hash function families imply the existence of distributional collision-resistant hash function families. Informally, distributional collision resistance is a relaxation of collision resistance, which guarantees that it is hard to find a uniform collision for a hash function. This result implies a comparison among the power of subset resilience, collision resistance, and distributional collision resistance. Third, we prove the fully black-box separation from one-way permutations.
FPGA Implementation of 3-Bit Quantized Multi-Task CNN for Contour Detection and Disparity Estimation

Object contour detection is a task of extracting the shape created by the boundaries between objects in an image. Conventional methods limit the detection targets to specific categories, or miss-detect edges of patterns inside an object. We propose a new method to represent a contour image where the pixel value is the distance to the boundary. Contour detection becomes a regression problem that estimates this contour image. A deep convolutional network for contour estimation is combined with stereo vision to detect unspecified object contours. Furthermore, thanks to similar inference targets and common network structure, we propose a network that simultaneously estimates both contour and disparity with fully shared weights. As a result of experiments, the multi-tasking network drew a good precision-recall curve, and F-measure was about 0.833 for FlyingThings3D dataset. L1 loss of disparity estimation for the dataset was 2.571. This network reduces the amount of calculation and memory capacity by half, and accuracy drop compared to the dedicated networks is slight. Then we quantize both weights and activations of the network to 3-bit. We devise a dedicated hardware architecture for the quantized CNN and implement it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at the operating frequency of 250 MHz is 9 TOPs/s.
visxhclust: An R Shiny package for visual exploration of hierarchical clustering

visxhclust is an R Shiny (Chang et al., 2021) web app that facilitates iterative exploration of hierarchical clustering.The package assembles together the outputs of different steps of a clustering workflow in a fluid visual analytics (Keim et al., 2008) interface, enabling analysts to change selected features and hyperparameters and quickly re-evaluate results.It focuses on hierarchical clustering, a widely used method that is often chosen due to the familiarity of analysts with the tree-like structure of results presented as dendrograms and its frequent combination with visualization techniques such as heatmaps.Package reference and an illustrated tutorial are available online.
The influence of data mining on accounting information system performance: A mediating role of information technology infrastructure

The current study aimed at examining the influence of data mining (information and communication technologies (ICTs), knowledge management (KM), data warehousing (DW), and data mining (DM) on performance and outcomes of accounting information system (AIS) application through a mediating role of information technology (IT) infrastructure. Through adopting a quantitative approach, a questionnaire was distributed on 143 individuals working within food manufacturing organizations in Jordan; primary data were screened and analyzed depending on SPSS version 27. Results of the study matched what came along with Zhang (2021) and indicated that there is a positive relationship between data mining and AIS performance in terms of the fact that data mining along with its strategies (prediction, classification, collecting, and distributing) had the ability to ease the process of managing huge amount of data and transfer it to AIS application for better processing in accounting means. However, this relationship, as according to Kim (2020), was attributed to a well-built IT infrastructure that appeared to be the main and most important aspect that played a role in determining the level of performance of both data mining and AIS applications. In conclusion, the current study summed up that adopting technology means generating more data, the more data an organization gets the more it needs to improve its data organization, storage, classification, and analysis. This can only come from organizational vigilance and total awareness of technology and how it can improve organizational ability to generate well-built information that helps in decision-making.
Digital currencies: Challenges between the United States, the European Union and Asia Pacific

In June 2019, Facebook announced its own cryptocurrency “Libra” to be used through WhatsApp and Messenger, in order for its value to remain stable thanks to the support from banks and electronic commerce companies, linking to different currencies and the support of important companies. EU antitrust regulators investigated it as a “threat to monetary sovereignty”. China is already testing its own DCEP cryptocurrency tied to state banks and major companies, with notable advantages. It will probably displace cash and be used even for world trade, which can make it an alternative to the traditional international payment system, compared to the current one led by the United States with the USD. The Belt and Road Initiative (BRI) offers “the area and the route” to extend the Yen as a global digital currency. These new developments, and their rapid and high competitiveness produced as the “art of bian lian”, have generated intense concerns in the international financial system. Will the EU, Japan or the US be able to counter the virtual yuan so quickly?&#x0D; Received: 01 September 2021Accepted: 05 November 2021
Evolution of quantum coherence of qutrit-qutrit system under correlated depolarizing channels

In order to study the decoherence process of quantum system and find the ways to delay it, in this paper we investigate the decoherence process under correlated depolarizing channels by calculating the evolution of norm coherence, relative entropy coherence and basis-independent coherence of the three initial states: special initial state, maximum coherent state and isotropic state. From the analytic results, numerical results and evolution images, it can be found that 1) completely correlated channels can suppress decoherence to a greatest extent,. 2) the time point at which maximum decoherence occurs is irrelevant to the degree of correlation, but system’s decoherence evolution behavior depends on it. 3)the correlation of depolarizing channels can reinforce the collective quantum coherence between subsystems, and an inequality relation of basis-independent coherence is proved in this system.
[Health Risk Assessment of Soil Heavy Metals in a Small Watershed of a Mining Area in Yunnan].

The purpose of this study was to explore the pollution of soil heavy metals and the health risk of the contaminated soil to residents, which was affected by the copper mine in a small basin of a mining area in Yunnan Province. Soil (39 samples), sediment (six samples), water, and corresponding suspended particle (six samples) and dust (one sample) samples were collected. The contents of Cd, Pb, Hg, As, Zn, Cu, Ni, and Cr in the samples and the soil pH were determined. The spatial distribution of heavy metals was analyzed, and the source of soil heavy metals was innovatively traced by the relative proportion of heavy metals in various media. The geo-accumulation index, Nemerow comprehensive pollution index, and potential ecological risk index were used to evaluate and analyze the pollution status and the potential ecological risk of soil heavy metals in the watershed, whereas the health risk model recommended by USEPA was applied to evaluate the health risk. The results showed that the heavy metals in the soil of the upstream area might be derived from the synergistic input of irrigation, atmospheric deposition, and soil erosion. In the middle reaches and lower reaches, the irrigation and the soil erosion of sloping land mainly contributed the heavy metal input, respectively. It was also found that the pollution degree in the upstream area was higher than that in the downstream area. The farmland soil was seriously polluted by Cd, Zn, Pb, and Cu, and Cd, Zn, and Pb had high potential ecological risks. Although residents did not face the risk of non-cancer diseases, the carcinogenic risk had exceeded the acceptable level, and children were at higher risk of cancer. In addition, although the content of As in the soil was lower than that of Cd, Zn, and Pb, it had a higher carcinogenic risk.
AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.