Reviewerly/exHarmony huggingface dataset:
Combining hydrogeochemistry, statistics and explorative mapping to estimate regional threshold values of trace elements in groundwater (Sardinia, Italy)

Assessing geochemical baseline and threshold values of potentially toxic elements at adequate scales is fundamental for distinguishing geogenic contamination from anthropogenic pollution in groundwater. This study was aimed to estimate the regional threshold values of Li, Be, B, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cd, Sb, Te, Ba, Hg, Tl, Pb, Bi, and U (elements listed according to atomic numbers) in groundwater, compare results to guidelines established for drinking water and the protection of groundwater from contamination, investigate the geographical distribution of trace elements, and assess the potential influence of water-rock interaction. A pre-selection aimed at excluding groundwater samples affected by known anthropogenic activities was carefully carried out based on hydrogeochemical characteristics of waters and considering the potential sources of contamination. The resulting dataset was comprised of 1227 groundwater sampling sites located in Sardinia (Italy). Undetected values were treated using the Regression on Order Statistics method. For elements containing >75 % of undetected values and/or a limited number of samples in the dataset (Li, Rb, Sr, Mo, Ag, Te, Tl, Sb, Hg and Bi), the threshold values were estimated using either the 95th or 97.7th percentiles. For the other elements the mean + 2SD (Standard Deviation), the median + 2MAD (Median Absolute Deviation), and the TIF (Tukey Inner Fence) estimators were also calculated. Geochemical maps allowed to recognize the threshold value of each element at different scales. Regional threshold values of the regulated elements B, Al, V, Cr, Cu and Cd in groundwater were below the Italian and World Health Organization drinking water guidelines, whereas Mn and As were above them. Regional threshold values estimated with TIF exceeded the drinking water guidelines for Ni, Se, Pb and U. Results of this study showed that high concentrations of trace elements in groundwater were primarily dependent on the corresponding amount in parent materials with which the groundwater came into contact. Physical-chemical parameters and geochemical characteristics may contribute to enhancing concentrations of some trace elements in groundwater, e.g. As via reductive dissolution of Fe(III)-Mn(IV) hydroxides/oxides, Pb via formation of stable aqueous complexes, and other elements via adsorption onto fine particles with size below 0.4 μm (i.e. the pore size of filters used). Maps drawn on the centered log-ratio (clr) transformation of hydrogeochemical data, following the CoDA (Compositional Data Analysis) approach, allowed to pinpoint critical areas to be investigated in more detail. For each geological complex, groundwater samples likely representing nearly pristine conditions were identified. The monitoring of these representative groundwater samples may help to pinpoint eventual changes in environmental conditions.
Quantum correlation measure based on min relative entropy for two-partition and k-partition

As a peculiar resource of quantum mechanics, quantum correlation has been applied to many aspects. In quantum information processing and quantum computing, the quantum correlation plays an extremely important role, and it has been a subject of further studies, principally due to the general belief that it is a fundamental resource for different quantum information processing tasks. In addition, correlation measure is a very important physical quantity in studying the quantum correlation. A well-defined correlation measure needs to have some necessary properties. By proving these necessary properties, we can deepen our understanding of correlation measure. As one of the key concepts of quantum information theory, relative entropy is always used to measure the uncertainty contained in the state of physical system. In order to better understand the properties and applications of correlation measure based on relative entropy, in this paper, according to the properties of the min relative entropy, we give the quantum correlation measure based on min relative entropy for two-partition and k-partition. Furthermore, we prove that it satisfies some necessary properties of quantum correlation measures, including the nonnegativity, the invariance under local unitary operators, and the monotonicity under completely positive trace-preserving. By proving these properties, we show that the given correlation measure is well-defined. Security of communication has received much attention since ancient times. In today's society, the internet, instant messaging and e-commerce applications are all related to the information security, and the information security is related to the vital interests of everyone. The information encryption is one of the important methods to ensure information security. As an important way to ensure information security, quantum channel has received more and more attention. At the end of the paper, we introduce the concept of quantum channel, and discuss the influence of quantum channel on the correlation measure based on min relative entropy under k-partition. By proposing a new correlation measure and proving the effect quantum channel on the measure, we can better describe the uncertainty contained in the state of physical system.
A methodology for character recognition and revision of the linear equations solving procedure

Linear equations are valuable for real-world modeling phenomena involving at least one variable. However, verifying if the procedure followed by a human for solving a linear equation was done correctly is still a complicated matter. In this paper, we propose a methodology for the automatic character recognition and revision of the solving procedure of linear equations with one unknown. First, a camera is used to acquire an image of the handwritten solving procedure. Then, the image is pre-processed, and each character and equation lines are segmented. Subsequently, a convolutional neural network (CNN) is used to conduct the character recognition stage. Finally, a comparison rule is applied to revise the solving procedure. The character recognition was verified on a 2800 image data set (2100 for training and 700 for testing), including the ten digits and four symbols: ×, +, -, /. The revision procedure was tested on a data set with 140 handwritten equations (125 for training and 15 for testing). The results revealed that we recognized handwritten characters with an accuracy of 99%, which is similar to the state-of-the-art. Moreover, our proposal revised the solving procedure with an efficiency of 86.66%. • We present a method to segment individual equations in a solving procedure. • We propose comparison rules to revise a set of consecutive linear equations. • We automatically review the procedure for solving one unknown linear equation. • We revised the linear equation solving procedure with an efficiency of 86.66%.
An efficient web service annotation for domain classification and information retrieval systems using HADLNN classifier

Aimed at exchanging the dissimilar data between distributed applications, web services (WSs) annotations have progressed as a versatile and cost-effectual solution. Information retrieval (IR)assists in establishing the user-essential related information's searching impacts. However, rendering quick and efficient IR is a challenging problem. Also, the existent system yields slow accuracy, and as well the training time is high. Aimed at overcoming these problems, implemented an effective WS annotation aimed at domain classification and IR systems utilizing the Hybrid Artificial Deep Learning Neural Network (HADLNN). Firstly, the Semantic annotation (SA)stage is executed that comprises text preprocessing, repetitive data removal, feature extraction, and as well Ontology Construction. The text preprocessing offers the partitioning, stop word removal, and as well the stemming procedure aimed at the WSDL dataset. Next, continual WSs utilizing Hadoop Distributed File System (HDFS)is eliminated. After that, the CFC, confidence, support, and as well entropy attributes are taken out;next, the (Web Ontology Language) OWL files as of ontology construction are generated utilizing the protégé tool. After producing the OWL, the owl file is visualized utilizing Eclipse IDE and extracted the values utilizing the reasoner in the protege tool. After WS annotations, the domain is categorized centered on the connecting of WSs utilizing HADLNN. Lastly, the IR procedure is executed on MK-means that groups identical services as of the categorized domain. Preliminary outcomes exhibit that the system proposed offers efficient performance analogized to the existent techniques.
Robust time-domain phase shift structured illumination microscopy based on intensity correction

SIM techniques based on synchronous phase shift and vertical scanning have been proposed for fast and precise three-dimensional (3D) measurement. These methods are also named as time-domain phase shift (TSIM) method. In these methods, the envelope of light intensity depth response (IDR) curve is extracted to achieve the 3D map. However, when this method is applied in rough surface or complex environment, the imaging noise and background intensity variation will severely affect the shape of IDR which brings difficulties in envelope detection and causes large measurement errors. In this paper, we propose an improved TSIM (ITSIM) technique to enhance the measurement accuracy and robustness of TSIM. In this technique, the modulation distribution of each image is first calculated by a global frequency analysis algorithm, and the modulation depth response curve (MDR) of each pixel can be obtained. These MDR curves are then multiplexed with the corresponding IDR curves to correct the shape of the IDR curves. Finally, the envelope of the corrected IDR curve is detected to accurately reconstruct the 3D shape. The proposed method can effectively resist the intensity fluctuation and imaging noise, and thus can improve the measurement accuracy compared with the conventional TSIM. The experiments performed on standard step sample show that the root mean square error of TSIM and ITSIM is 8.5nm and 21.2nm, respectively, justifying the feasibility of proposed ITSIM.
Quantifying relational nouns in corpora

While relational nouns ( cousin ) are traditionally delineated in a binary and theory-dependent manner, this article approximates relationality as a continuous, objective corpus metric (Percent Possessive) – allowing for lexicon-wide exploration of which nouns are more or less relational and why. Comparing across nouns and accounting for the ontological class of the noun's referent (focusing on nouns denoting artifacts, natural kinds, occupations, humans and locations), I find that Percent Possessive is positively correlated with a noun's per-million-word frequency. Comparing across different web communities, I find that a noun is more frequent, and shows a greater ratio of definite to indefinite tokens, in the community where its Percent Possessive is significantly higher. I take these findings to be consistent with the claim that a noun is more easily interpreted as relational (as measured by Percent Possessive) when human interaction with its referent is more conventional (as measured by its frequency and definite-to-indefinite ratio). Inspired by the many authors who have suggested a socio-cultural component to relationality and possession, this article explores at scale in English how nouns reflect the conventions of the people who use them.
Enter the Giant Sea Slug

Abstract Eric Kandel decides to do more research on learning and memory, this time using the large neurons of the giant sea slug, Aplysia, as his preparation. Ladislav Tauc, already familiar with Aplysia, collaborates in the experiments, many of which are conducted at the Institut Marey in Paris. Kandel and Tauc discover that even an elementary neural circuit in Aplysia is capable of ‘learning’, displaying both habituation and sensitization.
Optimal resource allocation in practical quantum key distribution optical networks

In the application research of large-scale quantum communication network, one generally realizes resource allocation by constructing virtual service network and mapping it to actual physical space. In this mapping process, some assumptions are often made to simplify the model. For example, the key resource in the physical topology is assumed to be a fixed value, that is, the actual physical conditions and the performance differences of key supply caused by different protocols are ignored. This assumption may lead the network to fail to run appropriately in practical applications. In order to solve the above problems, from the perspective of link mapping, this paper proposes an improved virtual service mapping model and virtual service mapping algorithm with the quantum key distribution optical network as the underlying network, which makes it closer to the actual application scenario. On the one hand, by increasing the constraints of geographical location, the range from virtual nodes to the mappable physical nodes is reasonably restricted. On the other hand, from the perspective of hardware cost and actual key generation rate, the cost performance evaluation index is proposed to allocate and manage resources. In addition, by combining three mainstream quantum key distribution protocols (BB84, measurement-device-independent, and twin-field), we construct a universal virtual service mapping model in the quantum key distribution optical network, and realize the recommendation of the optimal protocol and the optimal allocation and management of resources.
Instruments and Effects

Abstract The history of musical instruments is full of innovative musicians and composers working to stretch what is possible in their music. The pursuit of new sounds and the emerging technologies surrounding them tell a story of curiosity, exploration, and discovery. Human beings push their art as far as they can, stretch it to include sounds that were not typical, use scales that were once foreign, and employ techniques for sound enhancement that were once not employed. Instruments are all at some level a form of technology. This chapter considers instruments as tools for creative expression in the classroom. Its focus is on the electric guitar and its associated effects and amplification, but it also considers bass guitars, keyboards and acoustic and digital drum sets. The chapter closes with a short look at custom instrument building.
Gear Considerations and Acoustics

Abstract This chapter is about live performance gear and acoustic considerations. It is meant to be an extension in some ways from what was covered in Chapter 5. The reader is encouraged to think about gear and acoustic considerations as they relate specifically to marshaling a cast and crew of supporters in live music performance. The responsibilities of different roles involved in live performance are considered, and examples of the necessary gear are described, including PA systems, microphones, and other stage gear. The chapter ends with an outline of the basics of acoustics as they apply to live audio engineering.
Walking, Wilderness, and Exposure

Abstract This chapter examines Thoreau’s experiences on Mt. Katahdin, vis-à-vis exposure science and wilderness therapy. A close reading of Thoreau’s account suggests the experience had a profound effect on him, emotionally and philosophically, an effect that is relevant to environmental exposure and eco-vulnerability in the contexts of the COVID-19 pandemic and climate change. Thoreau’s experience on that mountain presented to him an aspect of wilderness antithetical to the romantic, transcendentalist notions he previously held and ultimately led to more nuanced, therapeutic depictions found in works like “Walking” and Walden. That such a reconciliation was possible for Thoreau suggests a privileged insulation from trauma not afforded to many. It also indicates the value of both philosophical contemplation and wilderness therapy to promote psychological resilience and adaptability. The author argues philosophers should begin to engage conceptually and normatively with exposure, vulnerability, and resilience.
Justification logic and type theory as formalizations of intuitionistic propositional logic

Abstract We explore two ways of formalizing Kreisel’s addendum to the Brouwer–Heyting–Kolmogorov interpretation. To do this, we compare Artemov’s justification logic with simply typed $\lambda $ calculus. First, we provide a completeness result for Kripke style semantics of the implicational fragment of the intuitionistic logic of proofs. Then we introduce a map from justification terms into $\lambda $ terms, which can be viewed as a method of extracting the computational content of the justification terms. Then we examine the interpretation of Kreisel’s addendum in justification logic along with the image of the resulting justification terms under our map.
Bangla Song Suggestion Using Face Detection

Face recognition system has gained a lot of interest because of its wide range of applications and business potential. Facial recognition system is a bit of technology that can match a person's face in a digital picture or video frame to a database of faces. Face detection, feature extraction, and face recognition are the 3 steps of facial recognition software. In image analysis, face detection is a challenging problem. It's now included in a multitude of fields, such as security systems, streaming video analysis, and other scientific innovations. Also, melody is really do have a deeper emotional bond than other forms of art. It possesses a one-of-a-kind ability to lift one's mood. This investigation, on the other hand, focuses on creating an excellent music recommendation system that leverages Facial Recognition methods to determine the user's mood. Music recommendation technique is an independent learning system that analyzes several users' playlists and makes suggestions for each user's specific playlist. This concept is built on user-to-user suggestions. The applied algorithm is OpenCV which is outperform existing systems. This would result in the saving of time and effort spent physically conducting the operation. The technique overall aim consists to identify swiftly suggest melodies based on facial expressions. The suggested process will have both advantages and disadvantages.
A Deep Learning Approach for Detecting Bangladeshi Counterfeit Currency

Fake or duplicate currency made with the intent to deceive is referred to as counterfeit money. According to recent sources, demonetization resulted in an all-time high inflow of bogus notes into banks, increasing questionable activities. The method of printing or supplying counterfeit currency is growing more modern day by day as the era progresses. In addition, the detection of counterfeit currency is being upgraded. Currency detection has already attracted a lot of attention. In this day and age of information technology, counterfeit currency may be identified using a variety of tools, gadgets, and software. Most current efforts to detect a counterfeit note rely on image processing techniques. Many of the pieces have received positive feedback, but some have flaws. The number of research which has worked with all the Bangladeshi banknotes is very insignificant. That is why we are focusing on this issue. In this method, we have been working with Bangladeshi 5 taka, 10 taka, 20 taka, 50 taka, 100 taka, 200 taka, 500 taka, 1000 taka. We created a dataset for Bangladeshi currency named BANGLADESHI BANKNOTE which contains 8000 images. In this research Modified AlexNet (M-AlexNet) is used for feature extraction from BANGLADESHI BANKNOTE where classification is followed by Multi Support Vector Machine (M-SVM).
Training Tracker: A Training Management System

Competing requires us to sharpen our skills and present ourselves confidently. Organizations such as colleges, enterprises, training centers, and clubs provide diverse training to help us grow our skills. On the other hand, manual management causes challenges for the organizer, trainer, and trainee and is not always maintained. This paper proposed a specialized training management system. We propose a system that removes all inconsistencies and provides an appropriate solution for tracking and monitoring all training in one location. Multiple learning management systems may provide a temporary solution, but this system offers a permanent one-stop solution where anybody can monitor and verify each trainee’s participation. In our comparison research, we help to develop a learning community, which provides us with a large quantity of data for future analysis.
Metaheuristic Solver for Problems with Permutative Representation

Today, a large proportion of combinatorial optimization problems can be efficiently formulated as a mixed-integer program and solved with an exact solver. However, exact solvers do not scale well and thus custom metaheuristic algorithms are being designed to provide better scalability at the cost of no optimality guarantees and time-consuming development. This paper proposes a novel formalism for a large class of problems with permutative representation, together with a metaheuristic solver addressing these problems. This approach combines the advantages of both exact and metaheuristic solvers: straightforward problem formulation, scalability, low design time, and ability to find high quality solutions. Three different problems are formulated in the proposed formalism and solved with the proposed solver. The solver is benchmarked against the Gurobi Optimizer and significantly outperforms it in experiments with a fixed computational budget.
Masked Face Inpainting Using Generative Adversarial Network Based Architecture

One of the most difficult challenges in the area of computer vision is face inpainting which aims to recreate obscured face images. For this objective, numerous deep learning-based techniques have been developed. The results are encouraging, but there are still certain limitations such as reconstructed face regions are inconsistent with the surroundings or blurry. In this paper, we propose a Generative Adversarial Network (GAN) based architecture for inpainting the region beneath face masks. Face mask detection and face inpainting are the two components of the architecture. In the first section binary masks are obtained using UNet architecture and the missing sections under the masks are recreated in the second section. We suggest a model using the GAN architecture, which comprises of a generator and two discriminators, for this purpose. The local discriminator attempts to learn the deep features of missing regions while the global discriminator works to assure the overall structure of the face images. Using the publicly accessible Face Mask Lite Dataset, we developed a synthetic dataset to apply our suggested methodology. Our model surpasses other latest techniques.