Reviewerly/exHarmony huggingface dataset:
Quantum network security dependent on connection density between trusted nodes

In this paper we estimate how introduction of additional connections between trusted nodes through one, two and so on (i.e. connection density) to a quantum network with serial connection of trusted nodes affects its security. We provide proper scaling of failure probability of authentication and quantum key distribution protocols to the level of the whole quantum network. Expressions of the failure probability dependent on the total number of connected nodes between users and connection density for given mean failure probability of each element are derived. The result provides explicit trade-off between increase of the key transport security and consequent increase of spent resources. We believe that obtained result may be useful for both design of future network and optimization of existing ones.
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems

Voice assistants such as Alexa, Siri, and Google Assistant have become increasingly popular worldwide. However, linguistic variations, variability of speech patterns, ambient acoustic conditions, and other such factors are often correlated with the assistants misinterpreting the user's query. In order to provide better customer experience, retrieval based query reformulation (QR) systems are widely used to reformulate those misinterpreted user queries. Current QR systems typically focus on neural retrieval model training or direct entities retrieval for the reformulating. However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval. In this work, we propose a novel Query Expansion and Entity Weighting method (QEEW), which leverages the relationships between entities in the entity catalog (consisting of users' queries, assistant's responses, and corresponding entities), to enhance the query reformulation performance. Experiments on Alexa annotated data demonstrate that QEEW improves all top precision metrics, particularly 6% improvement in top10 precision, compared with baselines not using query expansion and weighting; and more than 5% improvement in top10 precision compared with other baselines using query expansion and weighting.
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices. The sequential inputs can be perturbed in various ways, e.g., streaming images can be deformed due to robot motion or imperfect camera lens. Using the notion of the Voronoi diagram and Lipschitz properties of stable RNNs, we provide a thorough analysis and characterize the maximum allowable perturbations while guaranteeing the full accuracy of the classification task. We illustrate and validate our theoretical results using a map dataset with clouds as well as the MNIST dataset.
Tone generation in an open-end organ pipe: How a resonating sphere of air stops the pipe

According to the classical Helmholtz picture, an organ pipe while generating its eigentone has two anti-nodes at the two open ends of a cylinder, the anti-nodes being taken as boundary condition for the corresponding sound. Since 1860 it is also known that according to the classical picture the pipe actually sounds lower, which is to say that the pipe so-to-speak sounds longer than it is, a long-standing enigma. As for the pipe's end, we have resolved this acoustic enigma by detailing the physics of the airflow at the pipe's open end and showing that the boundary condition is actually the pipe's acoustically resonating vortical sphere (PARVS). The PARVS geometry entails a sound-radiating hemisphere based on the pipe's open end and enclosing a vortex ring. In this way we obtain not only a physical explanation of sound radiation from the organ-pipe's open end, in particular, of its puzzling dependence upon the pipe's radius, but also an appreciation of it as realization of the sound of the flute, mankind's oldest musical instrument.
Some problems of the teaching staff quality work as a criterion for the effectiveness of educational services at the university

The article contains the analysis of the activities of the teaching staff of the higher educational institution, as well as examines both general theoretical and practical problems based on the author's observations that hinder the effectiveness of this activity. In particular, the author analyzes the basics of Russian native speaker’s perception of the content of the words “labor” and “work” and phrases based on them. Next, legal and professional problems that do not contribute to the improvement of the quality of their activities by university teachers (the Effective contract, scientometric indicators and distance learning) are considered. The paper contains proposals, the implementation of which will allow the university to evaluate the creative potential of the teacher and, in general, to increase the effectiveness of the educational services provided.
Addendum: On stochastic Kaczmarz type methods for solving large scale systems of ill-posed equations (2022 Inverse Problems 38 025003)

Abstract We address the convergence analysis derived in (Rabelo et al 2022 Inverse Problems 38 025003) for the sPLWK method; a SGD type method for solving large scale systems of ill-posed equations. The assumption constraining the growth rate of the stopping index function <?CDATA ${k}^{\ast }:\delta {\mapsto}{k}^{\ast }(\delta )\in \mathbb{N}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> <mml:mo>:</mml:mo> <mml:mi>δ</mml:mi> <mml:mo>↦</mml:mo> <mml:msup> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>δ</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>∈</mml:mo> <mml:mi mathvariant="double-struck">N</mml:mi> </mml:math> is removed; this assumption was needed in the proof of the semi-convergence result. The most important consequence of our findings is the fact that, what semi-convergence concerns, in the sPLWK method the growth rate of k *( δ ), as δ goes to zero, is independent of the decay rate of the noise level. This is in strong contrast to the deterministic theory, where one needs additional assumptions of the type lim δ →0 ‖ δ ‖ 2 k *( δ ) = 0 for many iterative schemes, i.e., the stopping index should not grow to fast.
A Standardized GIS Web Service Oriented Shared-nothing Architecture - Taking Species Distribution Modeling as an Example

Abstract The rapid development of big data analytics technologies and tools, particularly the related distributed computing technologies, which divide large problems into smaller ones, provides important technical support for developing big earth data research. To realize the effective utilization of big data computing resources, it is important to overcome the potential incompatibility and inconsistency among the distributed computing nodes. Using standardized GIS web service interfaces is a promising approach to link existing heterogeneous GIS platforms through standardized protocols and establish a universal, platform-independent computing layer to improve the efficiency of computing resources for big earth data analysis. This research analyzed the requirements of distributed computing, and further proposed a standardized GIS web service-oriented shared-nothing architecture by comparing various mainstream distributed computing architectures and related web service specifications in the industry. For example, the prediction of Lophelia pertusa coral distribution by random forest is the application of the method discussed in this paper.
Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model

Swimming monitoring based on acceleration sensor is an emerging research direction in the field of human motion recognition. As a public sport, swimming has a wide audience. The swimming monitoring system can facilitate people to monitor and record their own swimming data, so as to formulate a reasonable training plan. Aiming at the defects of single modal information representation ability, high contingency, and easy to be influenced by the outside world, this paper adopts the underwater posture training model of swimmers to perform multimodal information fusion. In this paper, a multimodal information fusion method based on evolutionary neural network is proposed, and an intelligent perception information processing model of the intelligent subject system is constructed. Aiming at the defect that the accuracy and speed of the underwater posture monitoring of swimmers cannot be guaranteed in a complex environment, an evolutionary neural network optimized by a multimodal adaptive genetic algorithm is constructed to perform multimodal information fusion to ensure the effectiveness of the system in the face of complex information. Regarding attitude detection, it mainly uses the three dimensions of the angle of movement, the influence of gravity, and the strength and speed of the movement to measure. The MPU6050 module processor has a wide range of applications and is a mold processing tool with high performance and level. It completes the data processing, data calculation, and data storage of the inspection system in this paper. This paper further studies the working principle, structure, and operation process of this module and adjusts the time error in the detection of carrier motion and attitude so that the processing function of this module can play an optimal state. Four kinds of swimming posture measurement experiments were carried out on the swimmers, and the experimental data were analyzed. The whole system is controlled by the host computer man-machine interaction software remotely and in real time through commands. The experimental results show that the system realizes the detection of the basic posture, meets the basic requirements of the system design, and provides a certain foundation for the follow-up research.
Focal Length and Object Pose Estimation via Render and Compare

We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are twofold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods.
Research on Chinese Surface Semantic Analysis Based on Semantic Neural Network

This article attempts to use semantic artificial neural network to complete the surface semantic analysis of Chinese. In the dictionary, the surface semantic information of the vocabulary is represented by the semen, and the artificial neural network stores the syntactic and semantic knowledge distribution in the network weights through training and learning, and then uses this knowledge to perform parallel reasoning, which is essentially the same as the serial symbolic reasoning method. The difference. This aspect is used to deal with the problem of quantifier phrases in Chinese, that is, the collocation between nouns and quantifiers. Satisfactory experimental results have been obtained, and the results show that the semantic analysis is improved by 6.7%.
A Crowd Equivalence-Based Massive Member Model Generation Method for Crowd Science Simulations

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As model-based scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. The member model generation method based on crowd equivalence is verified through simulations. A simulation tool is developed to generate massive member models to support crowd science simulations and crowd science studies.
Knot much like tonk

Abstract Connectives such as Tonk have posed a significant challenge to the inferentialist. It has been recently argued (Button 2016; Button and Walsh 2018) that the classical semanticist faces an analogous problem due to the definability of “nasty connectives” under non-standard interpretations of the classical propositional vocabulary. In this paper, we defend the classical semanticist from this alleged problem.
Augmented Reality Try-On Watch Application

Augmented reality is a growing trend in marketing and sales strategies in recent times. Augmented reality ads are immersive, meaning that marketers help build a specific emotional connection with customers. Unlike images or banners, for example, AR ads are interactive and lifelong users can see and interact with them. These days people prefer online shopping over traditional window shopping and augmented reality brands allow them to provide unique experiences with the convenience of allowing customers to tap their mobile devices. So, the main goal of creating the "AR Watch Tri-On Application" is to develop an Android application to try out different watches in a virtual manner using a mobile that supports the AR camera. The application can be used on online watch shopping websites and on applications such as Titan, Fastrack, Sonata, etc. This application eliminates the human effort of physically visiting watch shops which is a very time-consuming activity. The user can try many watches and different types of watches. Among the ongoing opportunities, augmented reality (AR) is widely accepted as the new game changer for big tech companies. The idea of augmented reality is much older than creating gaming experiences like Pokémon Go. It is used to improve customer experience and provide excellent solutions for easy detection of manufacturing defects. Throughout this paper, we will evaluate whether AR can be used for online shopping for fashion accessories and its future range. According to several studies conducted, approximately 20% -40% of garments sold through online fashion retailers are returned by consumers. Of this percentage, 60% - 70% is due to product return size issues. The main reason for this inefficiency is the wrong size ordered by the customer - many times by mistake. Because of this, the extra shipping cost for items returned by the retailer can lead to huge money loss and customer dissatisfaction.
Experimental quantum state discrimination using the optimal fixed rate of inconclusive outcomes strategy

Abstract The problem of non-orthogonal state discrimination underlies crucial quantum information tasks, such as cryptography and computing protocols. Therefore, it is decisive to find optimal scenarios for discrimination among quantum states. We experimentally investigate the strategy for the optimal discrimination of two non-orthogonal states considering a fixed rate of inconclusive outcomes (FRIO). The main advantage of the FRIO strategy is to interpolate between unambiguous and minimum error discrimination by solely adjusting the rate of inconclusive outcomes. We present a versatile experimental scheme that performs the optimal FRIO measurement for any pair of generated non-orthogonal states with arbitrary a priori probabilities and for any fixed rate of inconclusive outcomes. Considering different values of the free parameters in the FRIO protocol, we implement it upon qubit states encoded in the polarization mode of single photons generated in the spontaneous parametric down-conversion process. Moreover, we resort to a newfangled double-path Sagnac interferometer to perform a three-outcome non-projective measurement required for the discrimination task, showing excellent agreement with the theoretical prediction. This experiment provides a practical toolbox for a wide range of quantum state discrimination strategies using the FRIO scheme, which can greatly benefit quantum information applications and fundamental studies in quantum theory.
Reflections on Readability of Children's Literature Translation

With the development of globalization and the deepening of sino-foreign cultural exchanges, young readers and their parents in China are calling for better translations of children's literature to meet the needs of children in developing language, and reading habits, opening up horizons, experiencing a foreign culture. As an essential indicator for judging the quality of translation, readability is directly related to whether children can accept the translations. Some points have been reflected here on improving the readability of children's literature translations from four levels of phonetics, lexis, sentences, and discourse according to the developmental characteristics of children and their preferences of reading.
CV Parsing Using NLP

The objective of the project is to make a CV parsing algorithm using NLP Processing. The algorithm will parse resumes one by one and can create a Candidate Profile supported the talents mentioned within the resume. A corpus is created which contains various skills required for a particular job profile for example: Machine Learning, Data Science etc. Word Embedding’s are created from the corpus and these are used to match the talents within the candidate resume with the specified skills. Finally, the candidate profile is made and plotted as a bar chart for better visualization.
Research on Mathematics Classroom Teaching Optimization Model Based on GA Neural Network

The teaching characteristic of colleges and universities is determined by the teaching quality of teachers, but it is difficult to calculate with a linear mathematical explanation how to judge the teaching quality of teachers. In most colleges and universities, expert evaluations, supervision groups, peers listening to lectures in the classroom, and students' after-class evaluations are still used to determine the teaching ability of professors. It is undeniable that these judgment methods have certain practicality. Especially for new teachers, experts and peers can find problems in time and help new teachers quickly correct them, and they can also know how to better practice the “student-centered” teaching concept from the feedback of students after class. However, these judgment standards also have their limitations. For example, the classroom quality judgment standards for experts and peers are mostly set by some administrative departments according to social requirements, leadership requirements, and subjective cognition. Various institutions should have different weights for the same criterion, and if they all employ the same criteria, it will easily lead to imprecise conclusions. In addition, the expert group and students are also subjective and incomplete in the stage of teacher evaluation. Based on different nonquantitative factors, the evaluation standards for teachers in colleges and universities also need the scientific theoretical basis. Therefore, many scientists are considering whether to use a more intelligent and reasonable way to judge the quality of teaching. Nowadays, with the popularization of artificial intelligence (AI), the use of neural networks (NNs) is becoming more and more comprehensive. According to the characteristics of NNs, this paper uses the GA NNs algorithm to signify that this algorithm can be effectively used in the evaluation of teachers' teaching quality. In this paper, a large number of experimental results show that the use of GA NNs can be used to evaluate the quality of teaching, and the evaluation results can be obtained quantitatively in this way. For the assessment of teaching quality, quantitative results are also more powerful in teaching evaluation.
Whistleblowing System Deployment using ITIL v3 Framework

The Indonesian government has been urged to implement Whistleblowing systems for public services in order to achieve integrity zone development. Almost all service desk software provides user convenience as well as manages the service process in an organisation using standards based on the Information Technology Infrastructure Library (ITIL) v3. The service operation of Whistleblowing systems and the service desk systems are almost the same. However, currently only a few studies have examined the implementation of Whistleblowing systems which uses such a framework in a public higher education institution. The aim of this study is to design and develop a Whistleblowing system which is expected to fulfil the IT Service Management (ITSM) standard and to perform an integration process amongst the community and students. The software was designed and tested in the Universitas Islam Negeri Syarif Hidayatullah Jakarta using the ITIL v3 standards. The result showed that the efficiency parameter value produced a page speed point of 91 % (A). The minimum score was 91 % (A) and the fully loaded time was 2.4 seconds. A portability test showed a value of 100 % for the mobile-friendly score while the reliability test with a stress test also showed at 100 %. The alpha testing result was a very good predicate while beta testing received an acceptable score, good category, and classified as grade C. In conclusion, based on the abovementioned tests, this shows that the system can be used to manage student services, lecturers and staff, as well as to integrate the current service within the community.