Reviewerly/exHarmony huggingface dataset:
Multi-channel hypergraph topic neural network for clinical treatment pattern mining

Recently, increasing attention has been paid to mining clinical treatment patterns from electronic medical records (EMRs), which provide physicians with explicit knowledge to guide the patient’s treatment. However, the current work mainly focuses on the topic probability models. These shallow models fail to consider the complex high-order correlations. Hypergraphs provide a natural way to establish complex high-order correlations, but their potential to discover the semantics of latent topic has not been fully explored. In this paper, we integrate the topic model in hypergraph learning and propose a multi-channel hypergraph topic neural network (C3-HGTNN) to discover latent topic treatment patterns with learning high-order correlations. Specifically, the hypergraph network is constructed based on the interactions in the treatment traces, which describe the latent high-order correlations. Then, based on the heterogeneity of the nodes in the hypergraph, we generate multiple channels and perform a convolution operation on each channel to fully encode the hypergraph. Finally, we develop an extensive representation of the treatment trace and activity by combining the embeddings from various channels and generating relevant topic distributions to identify latent treatment patterns. Also, we demonstrate that our model is a form of deep inference for traditional relational topic models, filling the gap in learning high-order correlation in traditional topic models. Extensive experiments on our dataset and public dataset show the effectiveness and superiority of our model compared to the competitive baselines.
Higher Education Multimedia Teaching System Based on the Artificial Intelligence Model and Its Improvement

In order to improve the quality of multimedia teaching in higher education, this article analyzes the problems existing in multimedia education in higher education, improves the multimedia teaching system, and proposes a three-dimensional multimedia image surface extraction method based on crease. Moreover, this article uses the level set method to segment the Gaussian smoothed multimedia data to obtain the preliminarily divided target fault area and then extracts the fault surface as the feature of the target area. The analysis demonstrates that the multimedia teaching system of higher education based on the artificial intelligence model can effectively enhance the multimedia teaching mode and promote its further development.
Species specificity of the art of the age of metamodernism: features of transformational processes

The material of the article focuses on the problems of metamodernism, which today is becoming an active form of artistic practice. As the last stage in the more than one-century history of the modernist art movement, metamodernism tries to initiate a new model of artistic development, and this - in the theoretical aspect - leads to a revision of established aesthetic and art-scientific problems, in particular, the problem of species specificity of art, the origins of which go back to Antiquity. Based on the systematization of historical experience and new ideas developed in the space of modern humanism, in the article we define those outlines of the problem of species specificity of art, which are more or less clearly presented in the artistic productions of metamodernists. It is emphasized that during the last two decades it was they who managed to transform cultural and artistic processes in such genres as literature, painting and cinematography into the plane of new artistic and emotional expressiveness. The first attempts at transformative processes in music, which are known in the context of a "new interpretation" of sound stimuli, are also traced, which will allow later to consider multi-genre musical art as an example of a metamodern experiment. The reconstruction of the species structure of the art of the age of metamodernism actualizes a number of tangential aesthetic and art-scientific problems.This, first of all, concerns the problem of the structural elements of the work of art, which metamodernists try not only to revise, but also in some places to remove from the context of the work: the consequence of this is the "imagelessness" of art, the absolutization of "polystylism", a hypertrophied, even compared to postmodernism, defeat " of mimesis — reflection" for the sake of "simulacrum". The space of such "theoretical foundations" also includes "form", experiments with which can "blur" the boundaries of any kind of art, bringing it to the level of "artistic activity". In the logic of such an approach, it is also about denying the stable foundations of artistic creativity, which leads to an arbitrary or spontaneous creative process, the consequence of which is "permissiveness", which, in particular, is manifested in modern cinema. Deformation of the fundamental principles of the theory of artistic creativity prompts a revision of the phenomenon of "creative personality", and the artist loses his or her historically developed significance, falling into the trap of "template", "conjuncture" or "fashion". The article argues that it is precisely the problem of the specific nature of art that allows us to reproduce the process of fundamental changes that marked the transition from "postmodernism" to "metamodernism". At the same time, attention is focused on updating both the conceptual and categorical apparatus, which "ensures" the study of transformational processes, and on the correlation of the existing system of concepts and categories with those of an innovative nature.
SISTEM INFORMASI INVENTORI PERUSAHAAN IMPORTIR BUAH BERBASIS WEB (STUDI KASUS UD. NUMASARI BUAH)

There are several advantages of an inventory information system, including making it easier for companies to monitor the amount of stock of goods, making it easier for companies to produce reports or information for the benefit of the company, making it easier for companies to manage or market goods, minimizing. costs incurred due to human error and reduced working hours during the production process or ordering goods. Currently UD. Numasari Buah already has an inventory system that is still manual where inventory is carried out by manually counting the number of stock items in the warehouse one by one by writing on notebook paper or log book. So from that the authors want to create a web-based inventory information system that earmarked for UD. Numasari Buah to make it easier to do work in inventory at the company so that it becomes more efficient. So from the case above the author came up with the title "Web-Based Goods Inventory Information System at UD. Numasari Fruit.
AN ANALYSIS OF ENGLISH USED IN PROMOTING FASHION IN SOCIAL MEDIA

This research is conducted to know the use of English in promoting fashion in social media. In social media, there are many people who promoting their fashion to sell. They sometimes use English to promote in order to attract readers’ attention to buy. The aim of the research is to know how the meaning and word order of the English used in promoting fashion. This research is descriptive research. The data of the research are taken from instagram. The results of the research shows that the English used in promoting fashion is not correct and does not follow the correct word order and grammar.
DBHD: Density-based clustering for highly varying density

A major challenge in cluster analysis is the discovery of clusters with widely varying sizes, densities, and shapes. Most clustering algorithms lack the ability to detect heterogeneous clusters that differ greatly in all three properties simultaneously. In this work, we propose the Density Clustering for Highly varying Density algorithm (DBHD). DBHD uses a novel approach that considers local density information and introduces two new conditions to distinguish between different types of data points. Based on this and the adaptively computed density information, DBHD can detect the clusters described above and is robust to noise. Moreover, DBHD has intuitive and robust parameters. In extensive experiments, we show that our technique is considerably more effective in detecting clusters of different shapes, sizes, and densities than well-known (DBSCAN or OPTICS) and recently proposed algorithms such as DPC, SNN-DPC, or LSDBC.
Bit Error Rate Prediction Analysis on modified dyadic wavelet transform based Channel Estimation in comparison with Traditional wavelets for MIMO OFDM system

For the Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system, the goal of the work is to do Bit Error Rate (BER) Prediction Analysis on Novel Modified Dyadic Wavelets Transform Based Channel Estimation in comparison with Traditional wavelets. In order to undertake a comparison analysis, the classifying is done using both a sample size of n = 10 in traditional wavelets and n = 10 in dyadic wavelets. Software written in Python is used for simulation. In comparison to the Traditional Wavelet, the Bit Error Rate Prediction analysis reveals that the Dyadic Wavelets have a high accuracy of (92.36). (91.15). With a value of 0.32 (p 0.005), there is a statistically significant difference between the study groups. Based on the outcomes of the simulation and statistical analysis, Dyadic Wavelets provide significantly higher prediction accuracy than Traditional Wavelets.
Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation

Imitation learning that mimics experts' skills from their demonstrations has shown great success in discovering dynamic treatment regimes, i.e., the optimal decision rules to treat an individual patient based on related evolving treatment and covariate history. Existing imitation learning methods, however, still lack the capability to interpret the underlying rationales of the learned policy in a faithful way. Moreover, since dynamic treatment regimes for patients often exhibit varying patterns, i.e., symptoms that transit from one to another, the flat policy learned by a vanilla imitation learning method is typically undesired. To this end, we propose an Interpretable Skill Learning (ISL) framework to resolve the aforementioned challenges for dynamic treatment regimes through imitation. The key idea is to model each segment of experts' demonstrations with a prototype layer and integrate it with the imitation learning layer to enhance the interpretation capability. On one hand, the ISL framework is able to provide interpretable explanations by matching the prototype to exemplar segments during the inference stage, which enables doctors to perform reasoning of the learned demonstrations based on human-understandable patient symptoms and lab results. On the other hand, the obtained skill embedding consisting of prototypes serves as conditional information to the imitation learning layer, which implicitly guides the policy network to provide a more accurate demonstration when the patients' state switches from one stage to another. Thoroughly empirical studies demonstrate that our proposed ISL technique can achieve better performance than state-of-the-art methods. Moreover, the proposed ISL framework also exhibits good interpretability which cannot be observed in existing methods.
Evaluation of Solar Energy Generation and Radiation Prediction Using Machine Learning

Solar photo voltaic (PV) energy system backbone of the renewable energy system. Energy system is depended on weather conditions such as temperature and radiation intensity. The role of machine learning (ML) for solar energy generation and radiation forecasting. This paper presents ML algorithm or methods review for prediction of solar energy generation and radiation. This paper also presents the state of art on different ML methods and parameters for forecasting solar energy production and radiation.
FPGA Based Emulation Of B92 QKD Protocol

The global race to achieve quantum supremacy has made the design and development of quantum safe cryptography protocols inevitable. In the absence of commercial quantum computers, the classical computing based quantum system emulations are being used for the design and testing of quantum algorithms. In this paper, an FPGA based emulation of quantum system is tested for algorithm design by implementing the abstraction of B92 algorithm. The results of emulations are compared with the theoretical results of B92 algorithm for the performance bench-marking.
Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio

This paper examines a new measure of the exploration/exploitation trade-off in reinforcement learning (RL) called the occupancy information ratio (OIR). To this end, the paper derives the Information-Directed Actor-Critic (IDAC) algorithm for solving the OIR problem, provides an overview of the rich theory underlying IDAC and related OIR policy gradient methods, and experimentally investigates the advantages of such methods. The central contribution of this paper is to provide empirical evidence that, due to the form of the OIR objective, IDAC enjoys superior performance over vanilla RL methods in sparse-reward environments.
A Multilink Wavelength Assignment Scheme for QKD Optical Network Based on BB84 and SNS Protocols over Multicore Fiber

To address the problem of different security requirements for services, we use both the most widely used BB84 and the sending or not sending (SNS) protocol with higher security in the quantum key distribution (QKD) network over multicore fiber. Since SNS involves multiple links, communication node pairs will affect each other will affect each other when assigning channels, which will reduce the secure key rate. In this paper, we propose a resource assignment scheme for reducing the impact between links in which we define the impact rate to evaluate the impact of each wavelength. The simulation results show that the secret key rate per link is improved by 101.72 % compared with the first-fit algorithm.
All-optica Special Unitary Group of Degree Two (SU(2)) Unit and Splitter Based on MZI and Nonvolatile Phase-Change Material

This paper proposes a nonvolatile optical signal processing structure based on phase change material (PCM) Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> Se <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> and Mach–Zehnder interferometer (MZI), which can achieve arbitrary rotation of optical special unitary group of degree two (SU(2)) by adjusting the area ratio of the crystalline and amorphous parts on two fixed-length PCMs embedded on the MZI, and can achieve an all-optical splitter with arbitrary splitting ratio by configuring one fixed-length PCM.
Physics-Aware Predictive Models for Tunable Photonics Devices

Tunable photonics devices provide abundant transfer functions benefitting from the additional tunable degrees of freedom. However, modeling the tunable photonics devices generally requires multi-physics interaction simulations, which makes it extremely complex and time-consuming. In this paper, we propose a novel method for modeling tunable photonic devices, providing an effective approach to efficiently tune complex photonic devices. The proposed physics-aware models can learn from a small number of spectra measured from fabricated devices. After training processes, the trained models can provide accurate predictions of the unmeasured spectra, directing the tuning strategy.
An Effort Towards Improving Automatic-Transcription Systems

The automatic speech recognition (ASR) system is a modern popular emerging technological system with lots of scope for applications in different domains. However, developing a mature ASR system needs to address lots of challenges to accommodate the diversity of the accents of speakers from diverse countries, regions, communities, languages, dialects and many more. This work is an effort to understand the different aspects of improving modern automatic transcription systems. The performance on significant aspects like Accent detection, Gender Detection and Voice Recognition across five major audio features have been captured, analyzed and studied. Standard machine learning architecture has been applied to analyze the features and determine their impact on the performance of the transcription system. We observed a high level of accuracy on combined features and hope this study will help those who are working in ASR, and NLP.
A Hand Gesture-Based Contact-less Interface for Electronic Health Records

Clinical data monitoring and storing are essential components of continuous and preventive healthcare systems. Data such as blood pressure, pulse rate, temperature, etc., can be recorded by the hospital staff daily for in-patient subjects. The usual way of noting them down is to check different parameters using various medical instruments and write it on paper with the corresponding patient's details (e.g., name, patient-id, or government identity card number). However, after the outbreak of COVID-19, there is a set of World Health Organization (WHO) guidelines to behave in public places. Ordinary people and professionals feel hesitant to touch any media even if they have some protection such as gloves and sanitizer. In this crisis, there is a natural demand for contact-less activities instead of touch-based traditional ways. Gesture-based activities might be one of the low-cost alternatives to some sensor-based systems. This paper uses a profound learning-based finger point gesture to capture writing in the air and realize it on the screen through a predictive model. Here, the proposed framework has been demonstrated as a proof of concept to record blood pressure data for multiple patients without touching any electronic screen or paper. The proposed architecture is developed based on the gesture recognition and metric learning, which have been used to recognize different digits trained from the MNIST digit dataset. The mean test accuracy is reached 99.47% on the same dataset.
A Photonic in-Memory Analog Matrix-Vector-Multiplier Based on Passive Microring Resonator and Photonic Nonvolatile Memory

This paper proposes a low power consumption photonic matrix-vector multiplier based on microring resonator (MR) and photonic nonvolatile memory. Specifically, we use two passive MRs combined with a nonvolatile phase change material Ge <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> Te <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</inf> (GST) to form a basic dot-product computing unit that supports in-memory multiplication computing, and then interconnect them to achieve massively parallel matrix multiplication. As a proof of concept, we simulated a 4×4 MR-GST based matrix-vector multiplier. The simulation results show that, with acceptable accuracy, the computational efficiency of our proposed structure can reach 1.2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> MACs/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
DeePaste - Inpainting For Pasting

One of the challenges of supervised learning training is the need to procure substantial amount of tagged data. A well-known method of solving this problem is to use synthetic data in a copy-paste fashion so that we cut objects and paste them onto relevant backgrounds. Pasting the objects naively results in artifacts that cause models to give poor results on real data. We present a new method for pasting objects on different backgrounds so that the dataset created gives competitive performance on real data by only treating the border of the pasted object using inpainting. We show state-of-the-art results both on instance detection and foreground segmentation.
Block Chain Based File Tracking and Management System for Pension Applications

The Public authority offers different administrations in the field of schooling, medical care and benefits. The support of the disconnected documentation for all these administrations has become progressively troublesome and the recovery. People roaming around for pension benefits can be avoided by getting a digitized system for pension plans. The documents will be securely stored in blocks with the help of block chain concepts. The miners are the one who is going to check the documents and approve as well as the maintenance of the consensus in the block chain network. Pension chain means to address this by embracing block chain innovation to carry out the advancement following of the interaction and secure online documentation. The pension chain fundamentally centers on the benefits administration superannuation. At the hour of application, the date of fulfilment of the cycle is put away in the application structure, simplifying it for individuals to finish the procedures. The situation with the application is noticeable to the individual mentioning administration. The concerned specialists at that point check the believability of the application to affirm or dismiss it. If the application isn't affirmed inside the endorsed due date given the information given is substantial, at that point higher authorities of the relating segment and the candidate is cautioned. The Pension chain will support the company's general profitability, furthermore, approve exchanges without depending on outsiders, eliminating the essential issue of disappointment, and ensuring delicate information of individuals and government innately
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness

In response to the threat of adversarial examples, adversarial training provides an attractive option for improving robustness by training models on online-augmented adversarial examples. However, most existing adversarial training methods focus on improving the model’s robust accuracy by strengthening the adversarial examples but neglecting the increasing shift between natural data and adversarial examples, leading to a decrease in natural accuracy. To maintain the trade-off between natural and robust accuracy, we alleviate the shift from the perspective of feature adaption and propose a Feature Adaptive Adversarial Training (FAAT) optimizing the class-conditional feature adaption across natural data and adversarial examples. Specifically, we propose to incorporate a class-conditional discriminator to encourage the features to become <xref ref-type="disp-formula" rid="deqn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> class-discriminative and <xref ref-type="disp-formula" rid="deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> invariant to the change of adversarial attacks. The novel FAAT framework enables the trade-off between natural and robust accuracy by generating features with similar distribution across natural and adversarial data within the same class and achieves higher overall robustness benefiting from the class-discriminative feature characteristics. Experiments on various datasets demonstrate that FAAT produces more discriminative features and performs favorably against state-of-the-art methods.