Reviewerly/exHarmony huggingface dataset:
Entangled Coherent States in Teleportation

In the present paper, we will review the methods to produce superposition of entangled coherent state using polarizing beam splitter and Kerr non linearity. These coherent states have many attractive features and can be used in various schemes. Entanglement, refers to the superposition of a multiparticle system and explains a new type of correlations between any two subsystems of the quantum system, which is not existing in the classical physics. The present paper deals with the use of these states in quantum teleportation, entanglement diversion and entanglement swapping schemes. Entanglement diversion and entanglement-swapping refers to a scheme which may entangle those particles which had never interacted before. In the swapping scheme, two pairs of entangled state are taken. One particle from each pair is subjected to a Bell-state-measurement. This would result in projection of the other two outgoing particles in an entangled pair. Quantum Teleportation of two mode and three modes states is also studied with perfect fidelity. Minimum assured fidelity which is defined as the minimum of the fidelity for any unknown quantum information of the states is also discussed. It is also shown how the success rate of teleportation of a superposition of odd and even coherent states can be increased from 50% to almost 100%. The scheme suggested by van Enk and Hirota was modified by Prakash, Chandra, Prakash and Shivani in 2007. We find that an almost teleportation, diversion and swapping is possible by simply separating vacuum state from the even state. The present paper also deals with study of effect of decoherence and noise on these states and the effect of noise on fidelity and minimum assured fidelity. It is also discussed that these schemes can also be applied to the process of entanglement diversion and entanglement swapping.
Single shot quantitative polarized light imaging system for rapid planar biaxial testing of soft tissues

Quantitative Polarized Light Imaging (QPLI) is an established technique used to compute the orientation of collagen fibers based on their birefringence. QPLI systems typically require rotating linear polarizers to obtain sufficient data to estimate orientation, which limits acquisition speeds and is not ideal for its application to mechanical testing. In this paper, we present a QPLI system designed with no moving parts; a single shot technique which is ideal to characterize collagen fiber orientation and kinematics during mechanical testing. Our single shot QPLI system (ssQPLI) sorts polarized light into four linear polarization states that are collected simultaneously by four cameras. The ssQPLI system was validated using samples with known orientation and retardation, and we demonstrate its use with planar biaxial testing of mouse skin. The ssQPLI system was accurate with a mean orientation error of 1.35° ± 1.58°. Skin samples were tested with multiple loading protocols and in each case the mean orientation of the collagen network reoriented to align in the direction of primary loading as expected. In summary, the ssQPLI system is effective at quantifying collagen fiber organization, and, when combined with mechanical testing, can rapidly provide pixel-wise measures of fiber orientation during biaxial loading.
Wedge Angle and Direction Detection of Multi-opening Object Using Deep Learning

CNN is used to identify the number of openings, wedge angle and direction of multi-opening object with probing Gaussian beam. 97.37% accuracy is achieved for different classifications with 1° precision and up to 36 openings.
Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

In this paper, respiratory irregularities of humans are investigated, and a self Adaptive Fuzzy Logic-based Proportional plus Derivative Controller (AFLPDC) is designed to provide a controlled supply of oxygen to the patient, suffering from breathlessness. The Fuzzy Logic-based Proportional plus Derivative Controller (FLPDC) is tuned, by implementing an adaptive scheme, which provides desired control action according to the process parameter variations. The electrical equivalent model of the respiratory system is developed, and the model is validated with the help of a Spirometer and MATLAB System Identification Toolbox. To build up this model, the respiratory tract is well thought out into four sections: the nasal cavity, trachea, bronchi, and finally, the alveolar sacs. Each of these sections is represented as a series of combinations of Resistance, Inductance, and Compliance. The control capability of the proposed AFLPDC is investigated on the derived respiratory model. The supply of the desired level of supplemental oxygen by AFLPDC to the patients suffering from the diseases like Bronchitis and Emphysema during respiratory dysfunction is observed in the MATLAB SIMULINK environment without human intervention. The response revealed by AFLPDC is compared with the responses of the conventional PD controller and the Fuzzy Logic-based PD controller (FLPDC).
Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data

In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the credit-scoring context. The model list includes the binormal, bigamma, bibeta, bilogistic, power, and bifractal curves. The models are then tested against empirical credit-scoring ROC data from publicly available presentations and papers, as well as from European retail lending institutions. Except for the power curve, all the presented models fit the data quite well. However, based on the results and other favourable properties, it is suggested that the binormal curve is the preferred choice for modelling credit-scoring ROC curves.
A Content-Aware Non-Local Means Method for Image Denoising

Parameter setting and information redundancy are essential issues for the non-local means (NLM) algorithm. This paper introduces a new factor based on the Hessian matrix to adapt the smoothing parameter. Then, a strategy is proposed to implement the NLM by representing patches in terms of features, which uses the 2D histogram and summed-area table. Compared with other methods, the metric for patch similarity in this paper is based on statistical features of patches instead of Euclidean distance. More importantly, not many predefined thresholds are needed. Experimental results show that the proposed algorithm obtains better visual quality and numerical results, especially for images with rich contents and high noise.
Creating an Organizationally Embedded Strategic Communication Plan for Libraries

This is the first in a series of articles appearing in Library Leadership &amp; Management that provides library leaders with an outline for creating a sustainable and consistent communication plan across all platforms and venues. Going beyond traditional marketing and branding, this series suggests a comprehensive approach to communication - both to people and to the computer networks that people use – for everyday communication, for disaster planning, and for the library’s strategic endeavors. There are many elements that can communicate and convey a library’s identity and values – social media posts, budget structures, library guides, strategic plans, etc. A coordinated and strategic plan for communication and outreach will strengthen your library’s value by creating a common experience and understanding by your library community, including library patrons, boards, administrators, and donors as well as internet search engines, social media networks, and their users. This plan will keep the library focused on objectives from the strategic plan and not straying beyond the plan, which could otherwise drain resources from what is trying to be achieved. This first article outlines the purpose and development of a library’s values, mission, and vision statements, a strategic plan, a communication plan, and the embedding of that plan in organizational culture. Subsequent articles explore offshoots of this communication including budgeting message and presentation, social media strategies, and search engine optimization and semantic web identity.
An Expert System Dataset for Checking the Potential for Administering a Covid-19 Vaccine in Indonesia: Forward-Chaining Inference Machine Approach

Covid-19 has now spread extremely worldwide. World still fighting against Covid-19, as well as vaccination programs. The problem to get one of the existing vaccines, it is required first to carry out a vaccination eligibility screening test, then usually carried out with traditional form method by the healthcare staff. Seeing the development of an expert system that can be used as an alternative for health workers to help reduce the number of Covid-19 suppression so that it does not spread to health workers, because after all, someone who will vaccinate Covid-19 must be in good health, this is related to the pre-vaccination procedure. We proposed the expert system with forward-chaining method. The result is the dataset (rules collection) of an expert system based on a forward-chaining inference machine for administering all Covid-19 vaccines.
Diagnostic Approach to Developing Asymmetry in Opportunist Screening Mammography; Correlation of Ultrasound, Magnetic Resonance Imaging, and Histopathologic Findings with Developing Asymmetry: A Cross-sectional Study

Background: Developing asymmetries are uncommon mammographic findings with a chance of being associated with malignancy. Objectives: The current study aimed at correlating ultrasound, magnetic resonance imaging (MRI) findings, and histopathology of patients with developing focal asymmetry in opportunist screening mammograms setting, and presents a diagnostic approach to developing asymmetry. Methods: This was a cross-sectional study on a database of opportunist screening mammography at the Breast Clinic, Cancer Center, at Tehran University of Medical Sciences from January 2017 to December 2018. Mammogram screenings (n = 12,169) were evaluated for developing asymmetry. Findings of mammography, ultrasound, MRI findings, and histopathology of patients with developing asymmetry were collected and analyzed. Results: Fifty-four cases (0.44%) had developed asymmetry in screening mammograms. After excluding 18 patients with considering exclusion criteria, the data of 36 patients were analyzed. The summation artifact was the etiology of developing asymmetry in 11 (30.5%) patients. Ultrasound was performed in 26 patients, and 14 (38.8%) patients had no correlated findings. All 3 malignant cases had ultrasound correlates, and a significant association existed between sonography and the risk of malignancy in patients having developing asymmetry (P = 0.003). Three malignant cases of the study underwent MRI, 1 with segmental clumped non-mass enhancement, and 2 showed a mass with rim enhancement. A significant association was revealed between a family history of breast cancer (P = 0.04) and developing asymmetry. The positive predictive value of developing asymmetry for malignancy was 8.3%. Conclusions: Patients having developing asymmetry should be evaluated for malignancy, using supplementary techniques, such as additional mammographic views, ultrasound primarily, or MRI. A biopsy is required for indeterminate findings.
Bio-Optimization of Deep Learning Network Architectures

Deep learning is reaching new heights as a result of its cutting-edge performance in a variety of fields, including computer vision, natural language processing, time series analysis, and healthcare. Deep learning is implemented using batch and stochastic gradient descent methods, as well as a few optimizers; however, this led to subpar model performance. However, there is now a lot of effort being done to improve deep learning’s performance using gradient optimization methods. The suggested work analyses convolutional neural networks (CNN) and deep neural networks (DNN) using several cutting-edge optimizers to enhance the performance of architectures. This work uses specific optimizers (SGD, RMSprop, Adam, Adadelta, etc.) to enhance the performance of designs using different types of datasets for result matching. A thorough report on the optimizers’ performance across a variety of architectures and datasets finishes the study effort. This research will be helpful to researchers in developing their framework and appropriate architecture optimizers. The proposed work involves eight new optimizers using four CNN and DNN architectures. The experimental results exploit breakthrough results for improving the efficiency of CNN and DNN architectures using various datasets.
Decentralized Cloud Storage Using Blockchain

Abstract: Because of its accessibility and ease of use, cloud storage has become the most widely used type of storage on the market in recent years. However, the privacy and data security of cloud storage are at risk. The protection of data security and privacy is the main topic of this essay. We suggest a blockchain-based decentralised storage system. Since blockchain is a distributed peer-to-peer system, any processing node connected to the internet can join and build peers' networks, maximising resource usage. Blockchain protects data security. The user's file is encrypted and shared among a number of network peers in the proposed system utilising the IPFS (Interplanetary File System) protocol. Hashes are generated by IPFS. The path of the file is indicated by the hash value, which is kept on the blockchain. This project is focused on decentralised secure data storage, high data availability, and effective storage resource usage.
Technical, psychological and behavioural aspects of teaching EFL and ESP university courses online: A case study of the 2020-2021 curriculum

Taking measures against the coronavirus pandemic in 2020-2021 many universities all over the world had to transform their formal classes into distance or online courses. The article deals with the issues of online EFL and ESP teaching and the learning process in distance format under an instructor’s supervision. The research is based on the feedback data of the survey conducted for the Political Science students who were taking up the English language university course online in the 2020-2021 curriculum instead of formal classes. The aim of the research was to reveal the technical, psychological and behavioural aspects of English language distance learning in a virtual MS Teams classroom as well as to analyse the students’ self-assessment of the efficiency and results of the distance EFL course. Applying quantitative and qualitative data analyses together with the method of involved observation, the authors arrive at conclusions concerning the still existing technical problems preventing students from working effectively, the disruptive character of certain psychological and behavioural patterns in a virtual classroom, as well as discrepancies between the amount of effort students put into their work and their academic achievements. Study results show both the drawbacks of the distance online format compared to the traditional formal classroom and the possibilities of effective implementation of distance forms of education at some stages of the English language educational process or within particular modules of the curriculum.
PEMETAAN KABUPATEN KOTA DI PROVINSI JAWA TIMUR BERDASARKAN TINGKAT KASUS PENYAKIT MENGGUNAKAN PENDEKATAN AGGLOMERATIF HIERARCHICAL CLUSTERING

Health is a human right that is one of the indicators of welfare that must felt by all people following the ideals of the Indonesian people to realize a high degree of health. The high level of public health is one of the base capital for realization of the implementation of national development. One indicator of policy target evaluation in the health sector is the availability of adequate public health facilities and services. In addition, the level of the number of people suffering from disease complaints needs to be the attention of the government and the community to become the material for evaluating policy targets in the health sector. Based on data on the percentage of the population who have health complaints in 2020 obtained from the BPS of the Republic of Indonesia, East Java Province is ranked 7th, which is 32.8%. While the data in 2021 East Java Province is ranked 8th at 28.55%. This figure is quite high compared to other provinces so the East Java Provincial government needs to pay attention to reducing cases of disease complaints. Therefore, this study aims to analyze and map regencies and cities in East Java Province based on the distribution of the characteristics of the number of disease cases in each regency and city using the Agglomerative Hierarchical Clustering method. Based on the value of the cophenetic correlation coefficient, the average linkage method is the best method with the optimal number of clusters formed 4 clusters based on the cluster validation test using the connectivity validity index test, Dunn index, and silhouette. The results of this study can be used as a reference for the government of East Java Province in taking policies to improve national development planning in the health sector to increase the high level of public health
Security Analysis of Sending or Not-Sending Twin-Field Quantum Key Distribution with Weak Randomness

Sending-or-not sending twin-field quantum key distribution (SNS TF-QKD) has the advantage of tolerating large amounts of misalignment errors, and its key rate can exceed the linear bound of repeaterless quantum key distribution. However, the weak randomness in a practical QKD system may lower the secret key rate and limit its achievable communication distance, thus compromising its performance. In this paper, we analyze the effects of the weak randomness on the SNS TF-QKD. The numerical simulation shows that SNS TF-QKD can still have an excellent performance under the weak random condition: the secret key rate can exceed the PLOB boundary and achieve long transmission distances. Furthermore, our simulation results also show that SNS TF-QKD is more robust to the weak randomness loopholes than the BB84 protocol and the measurement-device-independent QKD (MDI-QKD). Our results emphasize that keeping the randomness of the states is significant to the protection of state preparation devices.
Deep Structure-Aware Approach for QA Over Incomplete Knowledge Bases

The incompleteness of Knowledge Base (KB) greatly limits the performance of Question Answering (QA) system. Combining documents and incomplete KBs to develop QA system has become a hot spot in the research of Knowledge Base Question Answering (KBQA). Recent work ignores the relevance of KB and documents, and hinder the fusion of structured knowledge and unstructured text. This paper firstly builds a question-related subgraph entity encoder (QRS-Encoder) to gain nodes embedding; secondly, a structure-aware document reader (SAD-Reader) with deep structure information and related question is constructed, which can capture the Meta Dependency Path (MDP) nodes in the dependency graph constructed from question related documents. The MDP nodes are fused into node embedding representation in SAD-Reader, which effectively increases the node’s attention to the non-local dependencies in the document. Empirical results on WebQSP dataset show that our model outperforms state-of-the-art (SOTA) model in terms of both F1 and Hit@1 under incomplete KBs setting, which proves the effectiveness of our model.
Overview of NLPCC2022 Shared Task 5 Track 2: Named Entity Recognition

This paper presents an overview of the NLPCC 2022 shared task 5 track 2, i.e., Named Entity Recognition (NER), which aims at extracting entities of interest from domain-specific texts (material science). The task provides 5600 labeled sentences (with the BIO tagging format) collected from ACS material science publications. Participants are required to train a NER model with these labeled sentences to automatically extract entities of material science. 47 teams registered and 19 of them submitted the results; the results are summarized in the evaluation section. The best-submitted model shows around 0.07 improvement with respect to $$F_{1}score$$ over the baseline BiLSTM-CRF model.
Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs

Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become essential. This paper provides contributions along this direction by considering task-driven data processing for incoming nodes without connectivity information. We model the incoming node attachment as a random process dictated by the parameterized vectors of probabilities and weights of attachment. The attachment is driven by the existing graph topology, the corresponding graph signal, and an associated processing task. We consider two such tasks, one of interpolation at the incoming node, and that of graph signal smoothness. We show that the model bounds implicitly the spectral perturbation between the nominal topology of the expanded graph and the drawn realizations. In the absence of connectivity information our topology, task, and data-aware stochastic attachment performs better than purely data-driven and topology driven stochastic attachment rules, as is confirmed by numerical results over synthetic and real data.
Theseus: A framework for managing knowledge graphs about geographical divisions and their evolution

Abstract This article presents the Theseus Framework designed for the automatic creation of knowledge graphs (KGs) that describe the evolution of geographical divisions (GD) over time. This framework is based on an ontological model dedicated to the representation of evolving GD (e.g., divisions into regions, districts, municipalities). Both the detection and annotation of changes are automated using an algorithm based on several tests that measure the similarity between geographic areas. Theseus supports a complete processing chain that ranges from the preparation of the geographic data, including the detection and annotation of changes (using semantic tags such as fusion, split , etc.), to the generation of KGs ready to be immersed in the Linked Data Web.
Examining Gender Disparities in Computational Archaeology Publications: A Case Study in the Journal of Computational Applications in Archaeology and the Computer Applications and Quantitative Methods in Archaeology Conference Proceedings

Article: Examining Gender Disparities in Computational Archaeology Publications: A Case Study in the Journal of Computational Applications in Archaeology and the Computer Applications and Quantitative Methods in Archaeology Conference Proceedings