Reviewerly/exHarmony huggingface dataset:
Study on the composition of glass artifacts based on PSO particle swarm neural network

The analysis of the composition of glass artifacts has always been the focus of research in related fields. By analyzing and identifying the different compositions of glass artifacts from different eras and fields can support the understanding of the history of glass artifacts, their achievements and thus their exit from human civilization. In this paper, we use multiple correlation analysis, kernel principal component analysis, BP neural network model optimized by PSO particle swarm algorithm to study the composition of ancient glass artifacts.To address the shortcomings of the conventional BP neural network model, this paper adopts the BP neural network model under the optimization of PSO particle swarm algorithm to identify and study the unknown artifact types. And different subjective assignments are made to glass artifacts with different weathering degrees to discover the level of model sensitivity.Then the S-W normal distribution test was used and it was found that most of the chemical components did not obey normal distribution. Therefore, nuclear principal component analysis was chosen to find out the correlation between different types of components. On the basis of the identified two correlation coefficients and the paired data were normally distributed, by paired sample t-test, it was found that the magnitude of the differences between the chemical components of different categories was large. This was supplemented by correlation analysis and kernel principal component analysis. Finally, the composition identification study of glass artifacts was realized, which provided a new methodological path for the composition study of glass artifacts.
Realization of quantum mean filters with different sized on NEQR quantum images by QFT based operations

For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.
Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining

In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets.
Multilayer feature fusion and saliency-attention object tracking

Most of the existing Siamese network-based trackers achieve the similarity matching problem through the cross-correlation of convolutional features between the template branch and the search branch. However, the method of using the overall feature of the target as the convolution kernel to cross-correlate with the search area contains a lot of background information, which will adversely affect the tracking. To solve this problem, we propose a feature fusion and saliency-attention Siamese network (MSSiamCAR) for object tracking, adopting a part-based tracking strategy to track discriminative local salient regions in objects. This algorithm uses the ResNet-50 network to extract the target features. To solve the loss of the shallow part of the target due to the deepening of the network, a multilayer feature is proposed. The fusion module enhances the feature extraction ability of the network for the target. Second, a saliency capture module is proposed to obtain the local saliency of the target. These saliency are robust to interference factors. The saliency interaction module is designed, and the graph attention mechanism is used to establish an effective connection between the saliency, and the target feature information is propagated to the search feature so that the tracker has more discriminative ability to the target feature. Finally, the region proposal network is used to perform operations, such as classification, regression, and centrality calculation. Experiments on challenging benchmarks, including OTB-100, VOT2018, large-scale single object tracking (LaSOT), generic object tracking-10k (GOT-10k), and unmanned aerial vehicles 123 (UAV123), demonstrate that our proposed tracker performs favorably against state-of-the-art trackers.
GEO-SPATIAL MAPPING OF IRON (FE) AND MANGANESE (MN) PRESENT IN GROUNDWATER OF CLARK, PHILIPPINES

The study covers a comprehensive analysis of Fe and Mn to characterize their distribution and occurrence within the Clark Freeport Zone. 3-year groundwater samples taken from 39 wells/boreholes scattered throughout the study area. Fe was most frequently detected with concentrations near the limits of Philippine National Standards for Drinking Water (PNSDW). 0.42% of the 958 studied samples for Fe had concentration greater than the 1mg/L limit. As for Mn, none of the 909 samples for Mn had concentration greater than the PNSDW. Of those wells that have shown presence higher than the minimum detection level (MDL), 5 have been installed with individual filtration system (DMI-65) to capture Fe and Mn. No correlation has been found between the two parameters. As for seasonality, there is an obvious spike in Fe for all wells during the months of July, September, and October. Mn on the other hand manifested a spike in July for the shallow wells. The QGIS3 software was also used to develop a spatial map and report the distribution presence. From the generated maps, Fe and Mn gradually increase in concentration going north to northeastern directions of CFZ. The produced map can be key in raising awareness of the public and fellow water concessionaires with regards to the possible extent of underground Fe and Mn presence. Information generated here will be valuable as resources, and engineering controls can be applied to give priority to the low probability affected areas.
Using a Smartphone Application as a Tool for English Learning Among Medical Staff and Students in Japan

To report on the effects of a smartphone application, among the medical staff and medical students, for learning English as a foreign language.We conducted an exploratory quasi-experimental study among eight medical staff and 10 medical students in Japan. The participants used an application called ABC Talking (created by ABC Talking Laboratories Inc, currently unavailable due to application renewal), loaded onto their smartphones, to talk with native English speakers from overseas. The participants used the application for five minutes twice a day over five consecutive days as per their convenience. The study collected quantitative and qualitative data using assessments on the participants' listening and speaking skills and questionnaire. The assessment scores of the first five sessions were compared to those of the last five. Average self-assessment scores and teacher assessment scores were compared using a t-test. A paired t-test was performed on quantitative data of the questionnaire, and content analysis was performed on qualitative data.More than 80% of the calls were made from home and 70% occurred between 9PM and 1AM. The participants' self-assessment scores on their listening and speaking skills increased significantly from the first five sessions to the last five sessions (14.8-26.1%). However, there was no significant change in the assessments by the teachers (-4.5-2.1%). The self-assessment scores of those with low English proficiency were lower than the teachers' assessment scores. Improvement of communicative self-confidence and communicative competence, two factors that affect willingness to communicate, were seen from the questionnaire.Using smartphone applications allows on-demand English training, which may be especially useful to medical staff and students who have unpredictable work schedules. Teachers should be aware that learners tend to assess themselves lower than their actual ability so that they can give appropriate feedback to the learners.
Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review

The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach.
Design of a Smart Contract for Medical Records Based on Blockchain

In today's medical field, an ever-increasing number of different medical institutions begin to share patients' cases. Although this provides convenience for patients to seek medical advice, it will also lead to the problem of data abuse, and the patient's medical records are also at risk of being leaked or tampered with. The decentralization, non-tampering, openness, transparency, and traceability of blockchain technology can effectively solve these problems. This paper mainly studies a kind of medical records electronic contract which can guarantee the authenticity, security, and security of medical data. Firstly, patients own their medical records, and their medical data can be stored on the blockchain. The certificates of doctors and patients will also be stored on the blockchain. Secondly, the identities of doctors and patients need to be double verified in the storage process to ensure the authenticity and reliability of the data on the chain. Finally, the performance of a smart contract is evaluated by experiments. The smart contract studied in this paper can assure that large-scale medical data can be effectively stored and ensure the security of electronic medical records. And it can realize the sharing and reading of medical information between different hospitals.
Multi Perspectives Steganography Algorithm for Color Images on Multiple-Formats

The Internet and Big Data expansion have motivated the requirement for more generous stockpiling to hold and share information. Against the current era of information, guaranteeing protection and security to individuals sending data to each other is of utmost importance. The only file type that is instantly and widely used is the image. Therefore, to secure transmission, it is necessary to develop a mechanism to safeguard user data transmission. Considering this thought, it is necessary to analyze the best file type of image for essential criteria of image steganography, such as Payload, Robustness, Imperceptibility, etc., to challenge the weakness of the current algorithms. The widely used image formats are PNG, TIFF, JPEG, BMP, and GIF, which is the cause of existing methods. However, in this case, the critical softness is the credibility of the steganography, which plays a vital role in these format images to ensure the end users communicate. In this paper, a single algorithm provides several advantages for various types of images used as cover objects. However, after the critical and comparative analysis of different perspectives and some assessment metrics, the experimental results prove the importance, significance, and promising limits for these image formats by accomplishing a 4.4450% normal higher score for PSNR correlation than the next best existing methodology. Besides, in PSNR with a variable measure of code implanted in similar pictures of similar aspects, the proposed approach accomplished a 6.33% better score. Encrypting similar code sizes in pictures of various dimensions brought about a 4.23% better score. Embedding the same message size into the same dimension of different images resulted in a 3.222% better score.
A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.
Secure Medical Data Transmission In Mobile Health Care System Using Medical Image Watermarking Techniques

Medical information is maintained in a digital format, like scanned images along with patient information. In the mobile health care system, digitized medical information is transmitted to remote specialists for diagnosis purposes. The remote specialists verify the patient medical information using mobile or other devices and suggest the treatment. During medical data transmission, through unsecured media, there is a chance to modify the medical data by the attacker. It leads to the wrong diagnosis and affects the patient's entire life. So there is a need for secure medical data transmission in mobile healthcare to protect medical information from unauthorized users or intruders. The medical image watermarking technique is required to protect medical information in mobile healthcare. To withstand various medical image watermarking attacks, this chapter discusses two different types of robust medical image watermarking techniques in mobile healthcare. First, an intelligent-based medical image watermarking technique is discussed to protect the medical data in a secured manner during the electronic patient information embedding part. After embedding the patient information in the medical image, the generated watermarked medical image looks like an original medical image. So the attacker knows the visual existence of the medical data during its transmission. To avoid this, the second technique, i.e., the visual medical image encryption technique, is discussed. The mobile healthcare system uses the intelligent medical image watermarking technique and visual medical image encryption for the secure transmission of medical information.&amp;nbsp;&lt;br&gt;
MILI: Multi-person inference from a low-resolution image

Existing multi-person reconstruction methods require the human bodies in the input image to occupy a considerable portion of the picture. However, low-resolution human objects are ubiquitous due to trade-off between the field of view and target distance given a limited camera resolution. In this paper, we propose an end-to-end multi-task framework for multi-person inference from a low-resolution image (MILI). To perceive more information from a low-resolution image, we use pair-wise images at high resolution and low resolution for training, and design a restoration network with a simple loss for better feature extraction from the low-resolution image. To address the occlusion problem in multi-person scenes, we propose an occlusion-aware mask prediction network to estimate the mask of each person during 3D mesh regression. Experimental results on both small-scale scenes and large-scale scenes demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/MILI.
Adaptive Perona–Malik model based on dynamical threshold for image multi–noise removal with details preservation

In this paper, we propose a new dynamical threshold based Perona–Malik (DTPM) model for image denoising. As one of the most famous anisotropic diffusion equations, the PM model has been widely used in noise removal, image segmentation, edge detection and image enhancement. However, the major disadvantages of the traditional PM model are the high difficulty of choosing the best threshold and the tendency of impairing details, so that the denoising is either excessive or insufficient in the whole process. These defects are more obviously when dealing with the image that polluted by noise from multiple distributions. By designing a dynamical threshold function in the edge indicator, we establish a new PM model that can change the diffusion mode and strength adaptively according to the image features. The existence and uniqueness of the weak solution are proven. After theoretical analysis, we give an experimental approach to show the efficiency of this kind of model, especially to the image containing multi–noise and multi–details.
Optimized reverse converters with multibit soft error correction support at 7nm technology

Residue number system (RNS) speeds up digital signal processing systems involving dominant addition and multiplication. Addition and multiplication are accelerated further and performed with a balanced performance on one-hot coded (OHC) residue digits. However, the high complexity of the RNS reverse converter (RC) may kill the performance gain. This paper proposes a high-speed and scalable RNS-RC for both regular and one-hot RNS. For redundant RNS (RRNS), an RRNS-RC is proposed, which based on majority-voting between OHC residue digits, corrects multibit soft errors occurring in a single residue channel. With pass-transistor logic and low-power FinFETs, the proposed RCs are optimized. The simulated RRNS-RC corrected 98.5% of soft errors, while consuming 7, 6.2 and 0.4% of the system's area, leakage power and dynamic power, respectively. As compared to the leading lookup table RC in the literature, RNS-RC exhibited 10.3X, 4.4X and 1.8X savings in area, average power, and delay, respectively.
Strong semantic biases make demonstrative pronouns act like personal pronouns

Anaphoric demonstrative pronouns (d-pronouns), which are found in some languages in addition to the more common personal pronouns (p-pronouns), provide an interesting test case for theories of pronoun resolution. With regard to structural factors (e.g., syntactic function, linear position, topichood), d-pronouns have been found to stand in complementary relation to p-pronouns. Whereas the latter prefer structurally prominent antecedents, the former prefer structurally non-prominent antecedents. In contrast to structural factors, semantic factors (e.g., implicit causality and consequentiality) have been intensively explored for p-pronouns but not for d-pronouns. In particular, it is an open question whether semantic biases can override structural biases in the case of d-pronouns in the same way as it has been shown for p-pronouns. We have addressed this question in three sentence completion experiments and one acceptability experiment that investigated German p- and d-pronouns. Semantic bias was manipulated by including causal and consequential discourse markers in the interpretation experiments. The production experiments used questions to elicit continuations expressing causes and consequences. The results show that the semantic factor of coherence influences p- and d-pronouns in similar ways. Based on our experimental results, we show that the Bayesian Theory of Pronoun Resolution (Kehler et al. 2008), which captures the relationship between pronoun interpretation and pronoun production in a quantitative way, provides a good fit of observed and predicted values for both p- and d-pronouns.
Machine Learning Applications in Volcanology and Seismology

The purpose of this chapter is to show some applications in volcanology and seismology of Machine Learning methods as reported in literature.