Reviewerly/exHarmony huggingface dataset:
Determination of skarn garnet compositions using Raman spectroscopy

Abstract Garnet is a key and defining mineral of skarns and associated metalliferous deposits. Variation in garnet composition, commonly expressed by the proportions of different end‐members, is widely used to determine the physical–chemical features of hydrothermal fluids. Skarn garnets from the Fujiashan W‐Cu‐Mo deposit, eastern China, were investigated by Raman spectroscopy and electron probe microanalysis to assess the quantitative correlation between Raman band positions and proportions of garnet end‐members. Compositions and Raman band positions of so‐called “grandite” garnet (Adr 18–98 Grs 0–79 ), where Adr and Gr are the end‐members andradite and grossular, respectively, display a spatial zonation at Fujiashan that correlates with the distance from the contact between skarn and causative intrusion. Raman band positions determined in the ranges 234–246, 351–368, 369–375, 515–544, 814–826, and 873–882 cm −1 demonstrate moderate to very strong (R 2 up to 0.99) linear correlations with mol% andradite and grossular components. This is attributed to homovalent substitution between Fe 3+ and Al 3+ in the octahedral site, which has an indirect effect on bond lengths and angles of Si‐O vibration, resulting in linear variation of Raman band positions. The band between 515 and 544 cm −1 is the most sensitive to compositional variation, and its position enables robust estimation of end‐member proportions within 10% of results calculated from electron probe microanalysis. This research highlights the potential of Raman spectroscopy as a rapid, powerful method to assess the composition of skarn garnet, enabling accelerated construction of spatial zonation models for skarns during skarn deposit exploration.
Constrained minimum fuzzy error entropy filtering for target tracking

To improve the accuracy of target tracking results in nonlinear systems under complex noise, the constrained minimum fuzzy error entropy Kalman filter (CMFEE-KF) is proposed. In this proposed filter, the double indices-induced fuzzy membership is introduced to represent the different effects of different error samples on the estimation results, solving the problem of the same weight in ordinary error entropy. And then, the minimum fuzzy error entropy criterion (MFEEC) is constructed and used to optimize the Kalman filter. In this proposed algorithm, error information is obtained by model reconstruction. Then the objective function is constructed based on MFEEC, and finally, the posterior state estimation is achieved by the fixed-point iteration method. In addition, soft constraints can be implemented by adding a regularization term into the loss function, deriving the CMFEE-KF. Simulations show that the proposed filter has strong stability and more accuracy in target tracking.
Certificateless threshold signature from lattice

Threshold signature has been widely used in electronic wills, electronic elections, cloud computing, secure multi-party computation and other fields. Until now, certificateless threshold signature schemes are all based on traditional mathematic theory, so they cannot resist quantum computing attacks. In view of this, we combine the advantages of lattice-based cryptosystem and certificateless cryptosystem to construct a certificateless threshold signature from lattice (LCLTS) that is efficient and resistant to quantum algorithm attacks. LCLTS has the threshold characteristics and can resist the quantum computing attacks, and the analysis shows that it is unforgeable against the adaptive Chosen-Message Attacks (UF-CMA) with the difficulty of Inhomogeneous Small Integer Solution (ISIS) problem. In addition, LCLTS solves the problems of the certificate management through key escrow.
Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisions

Explainable AI (XAI) is a set of methodologies, design concepts, and procedures that assist developers and organizations in adding a layer of transparency to AI algorithms so that their predictions can be justified. AI models, their predicted impact, and any biases may all be described using XAI. Human specialists can grasp the forecasts generated by this technology and have trust in the results. Medical AI applications must be transparent in order for doctors to trust them. Explainable artificial intelligence (XAI) research has lately gotten a lot of attention. XAI is critical for medical AI solutions to be accepted and adopted into practice. Health care workers utilize AI to speed up and enhance a variety of functions, including decision-making, forecasting, risk management, and even diagnosis, by analyzing medical pictures for abnormalities and patterns that are undetected to the naked eye. Many health care practitioners already use AI, but it is frequently difficult to understand, causing irritation among clinicians and patients, especially when making high-stakes decisions. That’s why the health-care business requires explainable AI (XAI). Significant AI recommendations, such as surgical treatments or hospitalizations, require explanation from providers and patients. XAI delivers interpretable explanations in natural language or other simple-to-understand formats, allowing physicians, patients, and other stakeholders to better comprehend the logic behind a suggestion—and, if required, to dispute its validity.
Employment of Blockchain Technology in Supply Chain Management

Supply chain management is a significant part of industries all over the world as the satisfaction of customers owing to safe, secure and timely delivery of products relies on the strength of the supply chain system of any company, right from the stage when the products were anything but an unprocessed combination of raw materials. Our focus in this paper is to further strengthen the supply chain industry, take a turn from the traditional sense and apply a relatively new technology to the classic processes as we know them and study the synergy it produces. The paper commits to the role of explaining the culmination of supply and blockchain can lead to an efficient and secure way of doing business in various industries in the future. Furthermore, it describes that blockchain not only makes the supply chain management secure for all parties but it also makes the whole process efficient. A case study was done at an Amul warehouse, where different processes, and the turnaround time of carriers employed for them, were thoroughly studied and factors responsible for any delays and lack of efficiency were identified and scrutinised upon and finally alternative methods using blockchain technology were presented as means for improving the quality of the supply chain system as a unit, impacting the security, efficiency, trust and traceability.
Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing

Recent works have shown an interest in comparing visually similar but semantically different instances. The paired Totally Looks Like (TLL) image dataset is a good example of visually similar paired images to figure out how humans compare images. In this research, we consider these more generic annotated categories to build a semantic manifold distance. We introduce an atypical triplet-loss using the inverse Kullback-Leibler divergence to model the distribution of the anchor-positive (a-p) distances. In the new redefinition of triplet-loss, the anchor-negative (a-n) loss is conditional to the a-p distance distribution which prevents the loss correction fluctuations in the plain summed triplet-loss function of absolute distances. The proposed atypical triplet-loss builds a manifold from relative distances to a “super” anchor represented by the a-p distribution. The evaluation on the paired images of the TLL dataset showed that the retrieving score from the first candidate guess (top-1) is $$75\%$$ which is $$\times 2.5$$ higher compared to the recall score of the baseline triplet-loss which is limited to $$29\%$$ , and with a top-5 pairing score as high as $$78\%$$ which represents a gain of $$\times 1.4$$ .
Explainable Data Fusion on Edge: Challenges and Opportunities

Advancements in technology and the availability of small electronic devices have led to the availability of big data. This humungous amount of data is nowadays available in nearly every field, be it science or social domain. As much of these collected data are time-sensitive, they need to be utilized timely to give an effective outcome. Data fusion is of paramount significance in enhancing the collected data's effectiveness. But, today, as AI has gained significant momentum, machine learning (ML) techniques, mainly deep neural network (DNN), are widely utilized to deliver what is expected from the data. Edge intelligence tries to fulfil the promise of giving efficient and timely output. Deep learning, undoubtedly being robust, also possesses some disadvantages, and one of these relates to the explainability of ML methods that help solve any problem. With dense layers in DNN and millions of parameters, it is difficult to interpret the model creating a black box. The chapter aims to outline the explainability of data fusion at the edge. It highlights different data models of fusion, discusses a framework for AI and data fusion at the edge and identifies potential challenges and possible solutions in this regard. While doing so, the chapter intends to cover the fundamentals of explainability in AI, the need to convert the black box system into a transparent one, and the associated opportunities for explainable artificial intelligence.
Detection of Counterfeit Drugs in Medical Supply Chain Using Blockchain Technology

The creation and appropriation of fake medications are an earnest and progressively basic overall issue, particularly in agricultural nations. The fake medicine business estimates around 200 billion dollar yearly. One reason for drug forging is the flawed flexible chain framework in the drug industry. Medicines change possession from makers to distributors, distributors and afterward drug specialists before it arrives at the client. In the current gracefully chain framework, data are not shared between frameworks, makers do not have the foggiest idea what befallen their items, drugs administrative authority has zero ability to see of the framework, reviews are muddled and exorbitant, and organizations cannot subsequent patients. In this task, we disclose how to utilize blockchain innovation in drug flexibly chain to add discernibility, perceivability, and security to the medications gracefully framework. The suggested framework can be utilized for the drug industry to follow the medications from its assembling until it is conveyance to tolerance. The patient’s condition after the medicine has been consumed will be recorded or kept in the network to gain future insights. A permissioned blockchain allows only certain participants to enter in an existing network after the person has successfully gone through the procedure of authentication.
Attendance Management System Using Modern Face Recognition and Gesture Recognition Using Deep Learning

Human face location and acknowledgment assume significant parts in numerous applications like video reconnaissance and face picture information base administration. In our undertaking, we have contemplated chipped away at both face acknowledgment and recognition procedures and created calculations for them. In face acknowledgment, the calculation utilized is finding face picture, analyzing facial highlights, looking at against obscure face, and causing forecast in which we perceive an obscure test picture by contrasting it and the realized preparing pictures put away in the data set just as give data in regards to the individual perceived. In gesture recognition, there is a calculation which tells the framework and unravels the different hand motions that are taken care of into the framework. It can distinguish hand signals from a picture, video, or live. This component is particularly useful for individuals who utilize gesture-based communication or are actually debilitated (deaf and/or mute). This framework can assist them with conveying their instructor and can save the additional time that would have gone in understanding their reaction toward the class and the educator (in the case of online classes).
Detection of Tampering in Multimedia Using Blockchain Technology

A blockchain technology-based decentralized multimedia sharing system is proposed to detect and prevent tampering in multimedia. In this digital age, we are increasingly dependent on multimedia material, particularly digital images and videos, to provide accurate evidence for occurrence of events. However, the existence of many advanced yet user-friendly content editing tools has led to rethink about the credibility of such content. Our current net is an online source of knowledge. It is based on the concept of repeating and distributing info. Be it video, email, or article. All of those are copies of the original. For instance, if you wish to look at a video on YouTube or receive an online educational certificate of accomplishment, you are actually accessing a digital copy of the initial, not the original one. This is how you see your daily feed on Instagram and Twitter, even for media on WhatsApp, the concept is the same throughout the Internet. One is susceptible to receiving false information. The whole multimedia system is so vulnerable to unwanted media modification. So, the important question here is: Where does the problem lie? The problem is basically the whole structure on which the current Internet is based. The structure we use to share media is not good enough. The problem of tampering in multimedia could be seen in all walks of life. Be it in business, politics, economy, education, or entertainment. In this article, we propose a “difference hash algorithm”-based multimedia blockchain framework that will address this kind of issue. First we will be calculating the difference hash value of the two pictures separately. Then we will calculate the Hamming distance of two pictures’ difference hash value and judge the similarity of two pictures by the size of the Hamming distance.
Factors Influencing Madrasah E-Learning Satisfaction on Mathematics Teachers of Madrasah Tsanawiyah in Jakarta

This study analyzes the effect of attitude, quality, and flexibility on satisfaction and the implications for the desire to continue using madrasah e-learning for mathematics teachers in seventh grade for algebra material. The research includes non-experimental quantitative research. The population of this research is a mathematics teacher at the seventh-grade Madrasah Tsanawiyah in DKI Jakarta Province who teaches algebra material using madrasah e-learning. Sampling by convenience sampling and obtaining a sample of 225 teachers. The method of data collection is using a questionnaire. A Likert scale 1-5 questionnaire to collect data, data analysis method using PLS-SEM. Interviews with nine teachers were conducted to explore the causes of teachers agreeing and disagreeing with using e-learning madrasah in algebra learning. The results showed that teacher attitudes, e-learning quality, and e-learning flexibility significantly and positively affected e-learning satisfaction. E-learning satisfaction significantly and positively affects the desire to continue using e-learning. The interviews showed that teachers agreed because e-learning madrasah could help and facilitate teachers and students, and teachers did not agree because they encountered some difficulties in learning algebra.
A Double Meta<sup><i>n</i></sup>-Semantic Search Model Based on Ontology and Semantic Similarity: Asthma Disease

With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Meta n -search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Meta n -Semantic Search Model (2[Formula: see text]-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Meta n indicates that the search is done in depth ([Formula: see text]-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).
WAD-CMSN: Wasserstein distance-based cross-modal semantic network for zero-shot sketch-based image retrieval

Zero-shot sketch-based image retrieval (ZSSBIR) aims at retrieving natural images given free hand-drawn sketches that may not appear during training. Previous approaches used semantic aligned sketch-image pairs or utilized memory expensive fusion layer for projecting the visual information to a low-dimensional subspace, which ignores the significant heterogeneous cross-domain discrepancy between highly abstract sketch and relevant image. This may yield poor performance in the training phase. To tackle this issue and overcome this drawback, we propose a Wasserstein distance-based cross-modal semantic network (WAD-CMSN) for ZSSBIR. Specifically, it first projects the visual information of each branch (sketch, image) to a common low-dimensional semantic subspace via Wasserstein distance in an adversarial training manner. Furthermore, a novel identity matching loss is employed to select useful features, which can not only capture complete semantic knowledge, but also alleviate the over-fitting phenomenon caused by the WAD-CMSN model. Experimental results on the challenging Sketchy (Extended) and TU-Berlin (Extended) datasets indicate the effectiveness of the proposed WAD-CMSN model over several competitors.
Multirobot System to Explore Unknown Environment with Connection Maintenance

This paper addresses the problem of exploring an unknown environment using multiple robots and maintaining the communication among the team of robots. The existing exploration methods assume that the communication is maintained among the team of robots throughout the exploration process. However, this is not possible when the area to be explored is very large and when there is limited communication range. The presented exploration methods provide communication maintenance among the team of robots and maintain coordination. Three different approaches are presented for exploration. They are centralized approach, leader-follower approach, and ad hoc approach. The concept of centralized approach is developed where the base station is connected to all the robots in the team. The concept of leader-follower approach is developed, where among the team of robots, a leader will be selected. The concept of ad hoc approach is developed in which even though the robots are not within the communication range of one another, communication will occur through ad hoc network. The performance evaluation is done for the above three approaches based on the path length, exploration time, and total number of cycles required to explore the complete area. Systematic approach is used to examine the effect of influence of the exploration parameters like the number of robots, communication range of a robot, and sensor range of a robot on the performance metrics. It is investigated that when there is increase in the exploration parameters, then the exploration time is reduced.
Image dehazing with hybrid total variation–L0 regularization

We propose a dehazing problem model with hybrid regularization and design an effective algorithm to restore the latent image and transmission map simultaneously. In the proposed dehazing problem model, we use the total variation (TV) to regularize the latent image and adopt the hybrid TV and L0-norm (TV–L0) regularization to model the transmission map. In the proposed optimization algorithm, we first use the dark channel prior to achieve an initial guess of the global atmospheric light and transmission map. Then we convert the original problem into two subproblems: one aims to update the latent image based on TV regularization, whereas the other estimates the transmission map with hybrid TV–L0 regularization. Both of the subproblems can be solved efficiently with variable splitting and penalty technology, and the minimizer is reached by alternately solving the two subproblems. Experimental results show that our approach can achieve a high-quality restored image that is comparable to some state-of-the-art methods.
Multiscale image dehazing network based on spatially weighted sparse representation

Due to the influence of hazy weather, the images captured often suffer from severe degradation, which will reduce the effectiveness of the devices for computer-related applications. To eliminate the degradation of hazy images, a multiscale image dehazing network based on spatially weighted sparse representation is proposed. The model is mainly divided into two modules: multiscale module (MSM) and detail enhancement module (DEM). The MSM can effectively obtain more feature information in more hazy images. Specifically, the degradation of hazy image is considered in terms of haze distribution and image details. A spatially weighted nonlocal sparse attention module was developed to focus on channel-level features adaptively and to pay more attention to the recovery of dense haze areas in response to the problem of uneven distribution of haze in images. In addition, an independent feature fusion module is proposed to capture better the contextual information, which can also effectively address the traditional fusion, leading to redundant feature extraction and hindering the reconstructive performance of the network. Finally, a DEM is employed by a combination of pyramid pooling and resblock group, which guides the adaptive fusion of latent features, where different details and plausible appearances are integrated. Experimental results on public datasets show that this method is effective and superior in the metrics of structure similarity (SSIM), peak-signal-to-noise ratio, multiscale-SSIM, and root-mean-square error.